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contractions and signal those that will lead to preterm delivery","organization":"Beijing University of Technology","date":"2016-11","country":"China","amountUSD":401606,"officialTopics":"Discovery and Translational Sciences|MNCH Discovery and Tools","division":"Gender Equality|Global Health","regionServed":"GLOBAL","year":2016,"primaryTopic":"其他／待人工复核","topics":["其他／待人工复核"],"topicEvidence":{}}},{"id":"gates-public:OPP1149161","collection":"gates-public","collection_title":"盖茨基金公开项目","kind":"项目","title":"to support organization and management of the Fourth Global Symposium on Health Systems Research in Vancouver, BC, 14-18 November 2016","summary":"卫生服务与体系建设 · Health Systems Global","url":"gates-grants.html","source":"data/public-topic-records.json","details":{"id":"OPP1149161","collection":"gates","portfolios":["全球社医卫管候选"],"journal":"","authors":[],"publicationType":"资助承诺","analysisEligible":true,"doi":"","sourceUrl":"https://www.gatesfoundation.org/about/committed-grants","sourceLabel":"Gates Foundation Committed Grants，2026-10-14 快照","verification":"官方公开用途与承诺字段；不是拨款或成效；未逐项复核项目详情","title":"to support organization and management of the Fourth Global Symposium on Health Systems Research in Vancouver, BC, 14-18 November 2016","organization":"Health Systems Global","date":"2016-03","country":"Switzerland","amountUSD":250000,"officialTopics":"Delivery of Solutions to Improve Global Health","division":"Global Development","scopeNote":"社医卫管高相关规则初筛；未逐项人工复核","year":2016,"primaryTopic":"卫生服务与体系建设","topics":["卫生服务与体系建设","全球健康治理与政策合作"],"topicEvidence":{"卫生服务与体系建设":["health systems"],"全球健康治理与政策合作":["global 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Development","date":"2016-04","country":"United States","amountUSD":999564,"officialTopics":"Delivery of Solutions to Improve Global Health","division":"Global Development","scopeNote":"社医卫管高相关规则初筛；未逐项人工复核","year":2016,"primaryTopic":"卫生服务与体系建设","topics":["卫生服务与体系建设","数字健康、AI与测量方法","全球健康治理与政策合作"],"topicEvidence":{"卫生服务与体系建设":["health services","primary health care"],"数字健康、AI与测量方法":["measurement"],"全球健康治理与政策合作":["global health"]}}},{"id":"gates-public:OPP1151682","collection":"gates-public","collection_title":"盖茨基金公开项目","kind":"项目","title":"to support the Duke Center for Policy Impact for Global Health to engage at the global and country level to develop innovative evidence-based policy solutions to tough challenges in global health financing","summary":"全球健康治理与政策合作 · Duke 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153-175.","portfolios":[],"relevance":"管理与经济学参照","primaryTopic":"企业创新与组织机制","topics":["企业创新与组织机制"],"topicEvidence":{"企业创新与组织机制":["突破性创新","组织架构"]}}},{"id":"studio-library:PMC13630716","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Background The drivers of mortality and their associated risk factors evolve throughout the course of life. Understanding these transitions is essential for effective, equitable, and age-appropriate health interventions. Methods As part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023, we examined life expectancy and the probability of death from birth to age 70 years (70q0) from 1990 to 2023 for each Association of Southeast Asian Nations (ASEAN) member state. In addition, we assessed causes of death and risk factor by age and sex across different life stages. Life expectancy was estimated from age-specific morta","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13630716","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00159-3","url":"https://doi.org/10.1016/s2468-2667(26)00159-3","text":"Background The drivers of mortality and their associated risk factors evolve throughout the course of life. Understanding these transitions is essential for effective, equitable, and age-appropriate health interventions. Methods As part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023, we examined life expectancy and the probability of death from birth to age 70 years (70q0) from 1990 to 2023 for each Association of Southeast Asian Nations (ASEAN) member state. In addition, we assessed causes of death and risk factor by age and sex across different life stages. Life expectancy was estimated from age-specific mortality rates with standard demographic techniques, and 70q0 from mortality and GBD life tables. Cause-specific mortality was estimated using the Cause of Death Ensemble model, and risk-attributable mortality using the comparative risk assessment framework. Findings In 2023, regional life expectancy was 70·1 years (95% uncertainty interval 69·6-70·7) for males and 76·2 years (75·7-76·7) for females, ranging from 87·5 years (87·3-87·8) for females in Singapore to 62·1 years (60·8-63·5) for males in Myanmar. Correspondingly, all-cause 70q0 was higher for males than for females. Neonatal disorders were the leading cause of mortality among infants younger than 1 year. Road injuries were the primary driver of mortality among males from adolescence through to early adulthood, accounting for 25·0% (17·0-31·0) of total mortality (8230 [5740-10 800] deaths) in males aged 15-19 years, with mortality rates varying from 3·0 per 100 000 population (2·3-3·7) to 49·0 per 100 000 population (25·0-67·1) between countries. Among females, maternal disorders were the leading cause of death in ages 25-34 years. In mid-adulthood, non-communicable diseases such as ischaemic heart disease and stroke emerged as major causes. Tobacco use was the dominant risk factor across most of adulthood in males, whereas high blood pressure was the prevailing risk factor among adult females. Interpretation Although the leading causes of death have converged across countries, the outcomes of these causes remain divergent, reflecting persistent inequalities across the region. These patterns present a strategic opportunity for multilateral action to mobilise collective resources and achieve regional health equity. Funding Gates Foundation.","kind":"期刊文献","locator":"摘要","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 ASEAN Causes of Death Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13630716/","licenseUrl":"","openTextChunks":11}},{"id":"studio-library:PMC13630716-s0","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Introduction The Association of Southeast Asian Nations (ASEAN) region, established in 1967, constitutes 8·6% of the global population and is one of the fastest-growing economies in the world. 1 However, the region is characterised by stark socioeconomic asymmetries. The Human Development Index ranged from 0·946 in Singapore to 0·606 in Cambodia in 2025, 2 whereas the projected gross domestic product per capita in 2026 ranges from more than US$107 760 in Singapore to US$1520 in Myanmar. 3 The socioeconomic diversity fundamentally defines the region's health landscape. 4 Since 1990, ASEAN has undergone an unevenly paced epidemiological transit","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13630716-s0","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00159-3","url":"https://doi.org/10.1016/s2468-2667(26)00159-3","text":"Introduction The Association of Southeast Asian Nations (ASEAN) region, established in 1967, constitutes 8·6% of the global population and is one of the fastest-growing economies in the world. 1 However, the region is characterised by stark socioeconomic asymmetries. The Human Development Index ranged from 0·946 in Singapore to 0·606 in Cambodia in 2025, 2 whereas the projected gross domestic product per capita in 2026 ranges from more than US$107 760 in Singapore to US$1520 in Myanmar. 3 The socioeconomic diversity fundamentally defines the region's health landscape. 4 Since 1990, ASEAN has undergone an unevenly paced epidemiological transition, with persistent infectious and maternal and child health burdens in several states alongside rising non-communicable diseases (NCDs) in all. 5 , 6 Disparities in health system capacity have yielded discrepancies in health outcomes. 4 , 7 , 8 Although health inequities related to specific diseases have been explored within the region, 7 , 8 , 9 , 10 no research has systematically examined comparative all-cause mortality profiles and assessed age-sex-specific patterns from birth to adulthood across the entire ASEAN. A life-course approach addresses this gap by linking early-life exposures and structural determinants to later-life health outcomes and disparities. 11 Earlier regional applications were confined to childhood and adolescence or individual countries, without adulthood or cross-country comparison. 12 Because mortality drivers and their risk factors evolve across the life course, 13 , 14 we apply this framework using the latest findings from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 to examine causes of death across all 11 ASEAN member states. This granular, stage-specific analysis identifies age-sex-specific diseases and risk factors driving mortality to inform targeted interventions, promote longevity, and improve health equity across the region. Research in context Evidence before this study We searched PubMed from database inception to April 28, 2026, for English-language articles using the following search terms: “((“mortality” [MeSH]) AND (“cause of death” [MeSH]) AND (“risk","kind":"开放全文","locator":"Introduction（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 ASEAN Causes of Death Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13630716/","licenseUrl":""}},{"id":"studio-library:PMC13630716-s1","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Methods Overview Leveraging the best available data sources and a systematic analytical modelling approach, GBD 2023 produced geographically and temporally comparable estimates for various demographic and epidemiological metrics, including life expectancy and mortality for 292 causes of death, by age and sex, across 204 countries and territories, and from 1990 to 2023. 15 , 16 In this study, we examined life expectancy and probability of death from birth to age 70 years (70q0) across ASEAN and its member states from 1990 to 2023, and evaluated mortality patterns specific to cause and risk factor by life stages, sex, and location in 2023. Alth","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13630716-s1","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00159-3","url":"https://doi.org/10.1016/s2468-2667(26)00159-3","text":"Methods Overview Leveraging the best available data sources and a systematic analytical modelling approach, GBD 2023 produced geographically and temporally comparable estimates for various demographic and epidemiological metrics, including life expectancy and mortality for 292 causes of death, by age and sex, across 204 countries and territories, and from 1990 to 2023. 15 , 16 In this study, we examined life expectancy and probability of death from birth to age 70 years (70q0) across ASEAN and its member states from 1990 to 2023, and evaluated mortality patterns specific to cause and risk factor by life stages, sex, and location in 2023. Although Timor-Leste acceded to ASEAN only in October, 2025, we included it to provide the most current evidence base for the bloc as presently constituted, supporting forward-looking regional health planning. We defined life stages as: infancy and childhood (birth to age 9 years); 17 adolescence (ages 10–24 years), following the expanded definition proposed by Sawyer and colleagues, 18 which more closely reflects the biological, neurocognitive, and social transitions of this life stage; and adulthood (age 25 years and older). 19 GBD cause and risk factor hierarchy GBD classifies causes and risk factors into four hierarchical levels. Level 1 comprises three categories: communicable, maternal, neonatal, and nutritional diseases; NCDs; and injuries, partitioned into 22 Level 2 clusters and disaggregated further at Levels 3 and 4. Risk factors are similarly organised, from three Level 1 groups (environmental and occupational, behavioural, and metabolic) into 20 Level 2 clusters with further subdivisions. 15 , 20 For the present analysis, we focused on causes of death at Level 3 and risk factors at Level 2. Data sources and processing Data sources are detailed in the appendix (pp 6–13) ; mortality data were gathered from vital registrations, surveys, literature, and census data across ASEAN. Standard GBD processing corrections were applied, including age and sex splitting of deaths with insufficient information and redistribution of non-specific or implausible causes (garbage codes) to valid underlying causes. Sources were excluded ","kind":"开放全文","locator":"Methods（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 ASEAN Causes of Death Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13630716/","licenseUrl":""}},{"id":"studio-library:PMC13630716-s2","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Results Life expectancy and probability of death In 2023, life expectancy in the ASEAN region was estimated at 70·1 years (95% UI 69·6–70·7) for males and 76·2 years (75·7–76·7) for females, a regional increase since 1990 of 12·2% for males and 10·3% for females. Singapore had the highest estimated life expectancy for both sexes (83·1 years [82·9–83·4] for males and 87·5 years [87·3–87·8] for females), whereas the lowest life expectancy was in Myanmar for males (61·6 years [60·1–63·0]) and Laos for females (67·2 years [66·0–68·4]). Since 1990, Laos saw the largest relative increase in life expectancy (33·3% for males and 27·5% for females), a","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13630716-s2","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00159-3","url":"https://doi.org/10.1016/s2468-2667(26)00159-3","text":"Results Life expectancy and probability of death In 2023, life expectancy in the ASEAN region was estimated at 70·1 years (95% UI 69·6–70·7) for males and 76·2 years (75·7–76·7) for females, a regional increase since 1990 of 12·2% for males and 10·3% for females. Singapore had the highest estimated life expectancy for both sexes (83·1 years [82·9–83·4] for males and 87·5 years [87·3–87·8] for females), whereas the lowest life expectancy was in Myanmar for males (61·6 years [60·1–63·0]) and Laos for females (67·2 years [66·0–68·4]). Since 1990, Laos saw the largest relative increase in life expectancy (33·3% for males and 27·5% for females), and the Philippines saw the smallest relative increase (5·0% for males and 4·3% for females; figure 1 , appendix p 18 ). Figure 1 Trends in life expectancy at birth in ASEAN and its member states, 1990–2023 Lines show mean estimates of life expectancy at birth for the ASEAN region as a whole and each of its 11 member states from 1990 to 2023. Shaded areas represent 95% uncertainty intervals. Sudden declines in life expectancy at birth coincided with major mortality shocks, including the 1999 post-referendum violence in Timor-Leste, the 2004 Indian Ocean tsunami in Indonesia, the 2008 Cyclone Nargis in Myanmar, and the COVID-19 pandemic in 2021. ASEAN=Association of Southeast Asian Nations. Across all ASEAN member states, the 70q0 from all causes combined was higher for males than for females ( appendix p 19 ). In 2023, the regional 70q0 was estimated at 40·3% for males and 26·9% for females, and was lowest in Singapore (13·4% for males and 7·5% for females). Nine of 11 countries had male 70q0 exceeding 30%, with four surpassing 40% and the peak being in Myanmar (56·9%). Among females, four countries exceeded 30%, with the highest 70q0 observed in Laos (42·6%). Between 1990 and 2023, 70q0 decreased by 22·6% for males and 25·7% for females, most steeply in Singapore (–61·7% in males and –65·1% in females) and least in the Philippines (–7·6% in males and –8·4% in females; appendix p 19 ). Causes of death by life stages Neonatal disorders were the leading cause of death among infants younger than 1 year, accounting for 42·9% (95%","kind":"开放全文","locator":"Results（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 ASEAN Causes of Death Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13630716/","licenseUrl":""}},{"id":"studio-library:PMC13630716-s3","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Discussion This study provides a life-course perspective on the drivers of premature mortality across the ASEAN region. Our findings reveal substantial disparities in life expectancy and 70q0, with a life expectancy gap as wide as 20 years between the lowest-performing and highest-performing member states. Throughout the life course, leading mortality drivers showed similar age patterns: neonatal disorders dominate infancy; lower respiratory infections prevail in early childhood and drowning in later childhood to early adolescence; road injuries impose a substantial burden in adolescence and young adulthood, particularly among males; maternal","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13630716-s3","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00159-3","url":"https://doi.org/10.1016/s2468-2667(26)00159-3","text":"Discussion This study provides a life-course perspective on the drivers of premature mortality across the ASEAN region. Our findings reveal substantial disparities in life expectancy and 70q0, with a life expectancy gap as wide as 20 years between the lowest-performing and highest-performing member states. Throughout the life course, leading mortality drivers showed similar age patterns: neonatal disorders dominate infancy; lower respiratory infections prevail in early childhood and drowning in later childhood to early adolescence; road injuries impose a substantial burden in adolescence and young adulthood, particularly among males; maternal disorders account for a notable share among young females in many ASEAN states; and NCDs, including ischaemic heart disease, stroke, and diabetes, dominate from mid-adulthood onwards. Yet, outcomes diverge markedly; neonatal mortality rates, for instance, differ 40-fold between the highest-performing and lowest-performing countries. These patterns underscore substantial health inequities and differences in health-system capacity, suggesting opportunities for joint regional action. The increase in life expectancy and reduction in 70q0 between 1990 and 2023 reflect remarkable achievements in population health. The COVID-19 pandemic was associated with a temporary decline in 2020–21, before life expectancy resumed its upward trajectory. These advancements were accompanied by socioeconomic development, including gross domestic product growth, rising Human Development Index, and expanded universal health coverage. 24 , 25 However, progress remains uneven. In 2024, Singapore's gross domestic product per capita was 65 times that of Myanmar, with government health spending at 21% of public expenditure versus 4% in Myanmar. 26 The divergence is mirrored in health outcomes across the region. The leading causes of death echo the 15 priority conditions in the Global Health 2050 report by the Lancet Commission on Investing in Health, which targets a 50% reduction in probability of premature death before age 70 years by 2050. 27 These conditions—spanning eight neonatal, childhood, maternal, and infectious causes and seven NCDs—align clos","kind":"开放全文","locator":"Discussion（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 ASEAN Causes of Death Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13630716/","licenseUrl":""}},{"id":"studio-library:PMC13630716-s4","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Declaration of interests MSA reports grants or contracts from the Xiamen University Malaysia Research Fund (XMUMRF/2025-C15/ITCM/0006, XMUMRF/2023-C11/ISEM/0041, and XMUMRF/2026-C17/ITCM/0007; all internal research grants administered by Xiamen University Malaysia and disbursed to institution). JBA reports support from the University of Eastern Pangasinan through their employment. RJM reports support from the Wellcome Trust (315982/Z/24/Z). (For the purpose of Open Access, RJM has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. This grant provides core funding for Mahidol Oxford","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13630716-s4","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00159-3","url":"https://doi.org/10.1016/s2468-2667(26)00159-3","text":"Declaration of interests MSA reports grants or contracts from the Xiamen University Malaysia Research Fund (XMUMRF/2025-C15/ITCM/0006, XMUMRF/2023-C11/ISEM/0041, and XMUMRF/2026-C17/ITCM/0007; all internal research grants administered by Xiamen University Malaysia and disbursed to institution). JBA reports support from the University of Eastern Pangasinan through their employment. RJM reports support from the Wellcome Trust (315982/Z/24/Z). (For the purpose of Open Access, RJM has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. This grant provides core funding for Mahidol Oxford Tropical Medicine Research and contributes to RJM's salary. RJM is requested by Wellcome to include this text in the funding statement and cover letter of all manuscripts on which RJM is a coauthor.) SO reports research fellowship support from the Lynch Family Foundation; grants or contracts from NHMRC Australia (EL1 Investigator Grant); and a leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid with International Association for Suicide Prevention (Vice President 2025–26). YLS reports grants or contracts from NTU EPM & NSTC, Taiwan (postdoctoral fellow contract); a leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid with Benang Merah Research Center (co-founder); and other financial or non-financial interests with Jago Beasiswa (international scholarship mentor). JHVT reports a leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid with Benang Merah Research Center (co-founder).","kind":"开放全文","locator":"Declaration of interests（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 ASEAN Causes of Death Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13630716/","licenseUrl":""}},{"id":"studio-library:PMC13630716-s5","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Acknowledgments This paper was developed as part of the GBD Collaborator Network and in accordance with the GBD Protocol, with support from the GBD Secretariat, IHME, NUS–IHME Global Burden of Disease Research Centre, and GBD Collaborator Network, under the IHME ID 6310. GBD was partly funded by the Gates Foundation. Gemini (Gemini 3 Flash, 2026 release; Google, Mountain View, CA, USA) was used to assist in drafting and refining portions of this manuscript. The tool was used solely as an editorial aid. The authors take full responsibility for the accuracy, integrity, and originality of the content. Editorial note: The Lancet Group takes a neu","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13630716-s5","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00159-3","url":"https://doi.org/10.1016/s2468-2667(26)00159-3","text":"Acknowledgments This paper was developed as part of the GBD Collaborator Network and in accordance with the GBD Protocol, with support from the GBD Secretariat, IHME, NUS–IHME Global Burden of Disease Research Centre, and GBD Collaborator Network, under the IHME ID 6310. GBD was partly funded by the Gates Foundation. Gemini (Gemini 3 Flash, 2026 release; Google, Mountain View, CA, USA) was used to assist in drafting and refining portions of this manuscript. The tool was used solely as an editorial aid. The authors take full responsibility for the accuracy, integrity, and originality of the content. Editorial note: The Lancet Group takes a neutral position with respect to territorial claims in published text, figures, and institutional affiliations.","kind":"开放全文","locator":"Acknowledgments（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 ASEAN Causes of Death Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13630716/","licenseUrl":""}},{"id":"studio-library:PMC13630716-s6","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Acknowledgments Taishun Li*, Periklis Charalampous*, Stephanie C C van der Lubbe*, Yawen Zhou, Simon I Hay, Jeofrey Bautista Abalos, Edimansyah Abdin, Hanif Abdul Rahman, Asnawi Abdullah, Abdulrakib Abdulrahim, Michael R M Abrigo, Wirawan Adikusuma, Tawseef Ahmad, Sima Ahmadpour, Mohamed Mustaf Ahmed, Budi Aji, Mohammed Alfaqeeh, Khaidar Ali, Syed Mohamed Aljunid, Joseph Uy Almazan, Ali Salman Al-Shami, Junaid Amin, Roshan A Ananda, Chin-Siang Ang, Nguyen Hoang Anh, Sumadi Lukman Anwar, Irfan Ardani, Rizal Arifin, Keivan Armani, Mohammad Ashfaq, Muhammad Shahzad Aslam, Yuni Asri, Zaw Zaw Aung, Zulfa Ayuningsih, Fitriyono Ayustaningwarno, Abul","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13630716-s6","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00159-3","url":"https://doi.org/10.1016/s2468-2667(26)00159-3","text":"Acknowledgments Taishun Li*, Periklis Charalampous*, Stephanie C C van der Lubbe*, Yawen Zhou, Simon I Hay, Jeofrey Bautista Abalos, Edimansyah Abdin, Hanif Abdul Rahman, Asnawi Abdullah, Abdulrakib Abdulrahim, Michael R M Abrigo, Wirawan Adikusuma, Tawseef Ahmad, Sima Ahmadpour, Mohamed Mustaf Ahmed, Budi Aji, Mohammed Alfaqeeh, Khaidar Ali, Syed Mohamed Aljunid, Joseph Uy Almazan, Ali Salman Al-Shami, Junaid Amin, Roshan A Ananda, Chin-Siang Ang, Nguyen Hoang Anh, Sumadi Lukman Anwar, Irfan Ardani, Rizal Arifin, Keivan Armani, Mohammad Ashfaq, Muhammad Shahzad Aslam, Yuni Asri, Zaw Zaw Aung, Zulfa Ayuningsih, Fitriyono Ayustaningwarno, Abul Kalam Azad, Mohd Yusmaidie Aziz, Ahmed Bali, Muhammad Irfan Bashir, Bagas Suryo Bintoro, Ahmad Naoras Bitar, Ai Cao My Bui, Asmat Burhan, Muhammad Daoud Butt, Dimples Acquiatan Camporedondo, Yu Cao, Hana Chen, Nicholas WS Chew, Ruixin Chi, Bryan Chong, Dennis Chong, Karleen Chong, Christina Malini Christopher, Dinh-Toi Chu, Mayank Dalakoti, Pojsakorn Danpanichkul, Bryan Gervais de Liyis, Edward Christopher Dee, Sadia Din, Mai Ngoc Do, Thao Huynh Phuong Do, Minh Cuong Duong, Angel Belle Cheng Dy, Ferry Efendi, Ekawati Ekawati, Muhammad Esa, Maryam Farooqui, Nelsensius Klau Fauk, Neeraj Kumar Fuloria, Shivkanya Fuloria, Francis Sarial Ganancial, Fernando Jr Barroga Garcia, Adrian Eleazar Go, Bey Hing Goh, Ernesto Ramos Gregorio, Nguyen Hai Nam, Harapan Harapan, Arief Hargono, Andy Martahan Andreas Hariandja, Faizul Hasan, Zaw Myo Hein, Rohman Hikmat, Aaron Kwun Hang Ho, Tinh Ho Huu, Mehdi Hosseinzadeh, Mega Hasanul Huda, Fadly Husain, Mohd Salami Ibrahim, Umar Idris Ibrahim, Korakrit Imwattana, Lalu Muhammad Irham, Fahrul Islam, Jazlan Jamaluddin, Roland Dominic G Jamora, Hanish Singh Jayasingh Chellammal, Yovanthi Anurangi Jayasinghe, Jost B Jonas, Muhammad Khan, Parisa Khoshvaght, Siti Khuzaiyah, Ko Ko, Soewarta Kosen, Chong-Han Kua, Garry Kuan, Yee Cheng Kueh, Maria Dyah Kurniasari, Dea Anita Ariani Kurniasih, Asep Kusnali, Aria Kusuma, Dian Kusuma, Daphne Teck Ching Lai, Agung Dwi Laksono, Tri Laksono, Jerrald Lau, Bao Huy Le, Thao Thi Thu Le, Hoi Leong Lee, Shaun Wen Huey Lee, Elvynna Leong, Mohammad Farris Iman Leong Bi","kind":"开放全文","locator":"Acknowledgments（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 ASEAN Causes of Death Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13630716/","licenseUrl":""}},{"id":"studio-library:PMC13630716-s7","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Contributor Information GBD 2023 ASEAN Causes of Death Collaborators: Taishun Li , Periklis Charalampous , Stephanie C C van der Lubbe , Yawen Zhou , Simon I Hay , Jeofrey Bautista Abalos , Edimansyah Abdin , Hanif Abdul Rahman , Asnawi Abdullah , Abdulrakib Abdulrahim , Michael R M Abrigo , Wirawan Adikusuma , Tawseef Ahmad , Sima Ahmadpour , Mohamed Mustaf Ahmed , Budi Aji , Mohammed Alfaqeeh , Khaidar Ali , Syed Mohamed Aljunid , Joseph Uy Almazan , Ali Salman Al-Shami , Junaid Amin , Roshan A Ananda , Chin-Siang Ang , Nguyen Hoang Anh , Sumadi Lukman Anwar , Irfan Ardani , Rizal Arifin , Keivan Armani , Mohammad Ashfaq , Muhammad Shahzad ","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13630716-s7","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00159-3","url":"https://doi.org/10.1016/s2468-2667(26)00159-3","text":"Contributor Information GBD 2023 ASEAN Causes of Death Collaborators: Taishun Li , Periklis Charalampous , Stephanie C C van der Lubbe , Yawen Zhou , Simon I Hay , Jeofrey Bautista Abalos , Edimansyah Abdin , Hanif Abdul Rahman , Asnawi Abdullah , Abdulrakib Abdulrahim , Michael R M Abrigo , Wirawan Adikusuma , Tawseef Ahmad , Sima Ahmadpour , Mohamed Mustaf Ahmed , Budi Aji , Mohammed Alfaqeeh , Khaidar Ali , Syed Mohamed Aljunid , Joseph Uy Almazan , Ali Salman Al-Shami , Junaid Amin , Roshan A Ananda , Chin-Siang Ang , Nguyen Hoang Anh , Sumadi Lukman Anwar , Irfan Ardani , Rizal Arifin , Keivan Armani , Mohammad Ashfaq , Muhammad Shahzad Aslam , Yuni Asri , Zaw Zaw Aung , Zulfa Ayuningsih , Fitriyono Ayustaningwarno , Abul Kalam Azad , Mohd Yusmaidie Aziz , Ahmed Bali , Muhammad Irfan Bashir , Bagas Suryo Bintoro , Ahmad Naoras Bitar , Ai Cao My Bui , Asmat Burhan , Muhammad Daoud Butt , Dimples Acquiatan Camporedondo , Yu Cao , Hana Chen , Nicholas WS Chew , Ruixin Chi , Bryan Chong , Dennis Chong , Karleen Chong , Christina Malini Christopher , Dinh-Toi Chu , Mayank Dalakoti , Pojsakorn Danpanichkul , Bryan Gervais de Liyis , Edward Christopher Dee , Sadia Din , Mai Ngoc Do , Thao Huynh Phuong Do , Minh Cuong Duong , Angel Belle Cheng Dy , Ferry Efendi , Ekawati Ekawati , Muhammad Esa , Maryam Farooqui , Nelsensius Klau Fauk , Neeraj Kumar Fuloria , Shivkanya Fuloria , Francis Sarial Ganancial , Fernando Jr Barroga Garcia , Adrian Eleazar Go , Bey Hing Goh , Ernesto Ramos Gregorio , Nguyen Hai Nam , Harapan Harapan , Arief Hargono , Andy Martahan Andreas Hariandja , Faizul Hasan , Zaw Myo Hein , Rohman Hikmat , Aaron Kwun Hang Ho , Tinh Ho Huu , Mehdi Hosseinzadeh , Mega Hasanul Huda , Fadly Husain , Mohd Salami Ibrahim , Umar Idris Ibrahim , Korakrit Imwattana , Lalu Muhammad Irham , Fahrul Islam , Jazlan Jamaluddin , Roland Dominic G Jamora , Hanish Singh Jayasingh Chellammal , Yovanthi Anurangi Jayasinghe , Jost B Jonas , Muhammad Khan , Parisa Khoshvaght , Siti Khuzaiyah , Ko Ko , Soewarta Kosen , Chong-Han Kua , Garry Kuan , Yee Cheng Kueh , Maria Dyah Kurniasari , Dea Anita Ariani Kurniasih , Asep Kusnali , Aria Kusuma , Dian Kusuma , Daphne Teck Chi","kind":"开放全文","locator":"Contributor Information（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 ASEAN Causes of Death Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13630716/","licenseUrl":""}},{"id":"studio-library:PMC13630716-s8","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Supplementary Material Supplementary appendix","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13630716-s8","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00159-3","url":"https://doi.org/10.1016/s2468-2667(26)00159-3","text":"Supplementary Material Supplementary appendix","kind":"开放全文","locator":"Supplementary Material（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 ASEAN Causes of Death Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13630716/","licenseUrl":""}},{"id":"studio-library:PMC13630716-s9","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","summary":"References 1. World Economic Forum ASEAN will drive global growth. Here's how to unleash it. 2025. https://www.weforum.org/stories/2025/12/asean-global-growth-digital-economy-wef/ 2. UNDP Human Development Insights. 2025. https://hdr.undp.org/data-center/country-insights 3. International Monetary Fund World economic outlook (October 2025)—GDP per capita, current prices. https://www.imf.org/external/datamapper/NGDPDPC@WEO 4. Chongsuvivatwong V, Phua KH, Yap MT, et al. Health and health-care systems in southeast Asia: diversity and transitions. Lancet. 2011;377:429–437. doi: 10.1016/S0140-6736(10)61507-3. 5. Dans A, Ng N, Varghese C, Tai ES, Fi","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13630716-s9","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00159-3","url":"https://doi.org/10.1016/s2468-2667(26)00159-3","text":"References 1. World Economic Forum ASEAN will drive global growth. Here's how to unleash it. 2025. https://www.weforum.org/stories/2025/12/asean-global-growth-digital-economy-wef/ 2. UNDP Human Development Insights. 2025. https://hdr.undp.org/data-center/country-insights 3. International Monetary Fund World economic outlook (October 2025)—GDP per capita, current prices. https://www.imf.org/external/datamapper/NGDPDPC@WEO 4. Chongsuvivatwong V, Phua KH, Yap MT, et al. Health and health-care systems in southeast Asia: diversity and transitions. Lancet. 2011;377:429–437. doi: 10.1016/S0140-6736(10)61507-3. 5. Dans A, Ng N, Varghese C, Tai ES, Firestone R, Bonita R. The rise of chronic non-communicable diseases in southeast Asia: time for action. Lancet. 2011;377:680–689. doi: 10.1016/S0140-6736(10)61506-1. 6. Coker RJ, Hunter BM, Rudge JW, Liverani M, Hanvoravongchai P. Emerging infectious diseases in southeast Asia: regional challenges to control. Lancet. 2011;377:599–609. doi: 10.1016/S0140-6736(10)62004-1. 7. GBD 2021 ASEAN Cardiovascular Diseases Collaborators The epidemiology and burden of cardiovascular diseases in countries of the Association of Southeast Asian Nations (ASEAN), 1990–2021: findings from the Global Burden of Disease Study 2021. Lancet Public Health. 2025;10:e467–e479. doi: 10.1016/S2468-2667(25)00087-8. 8. GBD 2021 ASEAN Injuries Collaborators The epidemiology and burden of injury in countries of the Association of Southeast Asian Nations (ASEAN), 1990–2021: findings from the Global Burden of Disease Study 2021. Lancet Public Health. 2025;10:e456–e466. doi: 10.1016/S2468-2667(25)00069-6. 9. GBD 2021 ASEAN Mental Disorders Collaborators The epidemiology and burden of ten mental disorders in countries of the Association of Southeast Asian Nations (ASEAN), 1990–2021: findings from the Global Burden of Disease Study 2021. Lancet Public Health. 2025;10:e480–e491. doi: 10.1016/S2468-2667(25)00098-2. 10. GBD 2021 ASEAN Tobacco Collaborators The epidemiology and burden of smoking in countries of the Association of Southeast Asian Nations (ASEAN), 1990–2021: findings from the Global Burden of Disease Study 2021. Lancet Public Health. 2025;10:e442–e455.","kind":"开放全文","locator":"References（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 ASEAN Causes of Death Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13630716/","licenseUrl":""}},{"id":"studio-library:PMC13630716-s10","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Associated Data Supplementary Materials Supplementary appendix Data Availability Statement To download the input data used in these analyses, please visit the Global Health Data Exchange at http://ghdx.healthdata.org/gbd-2023 . All results from this study are publicly accessible. To download estimates produced in these analyses, please visit the GBD Results tool at https://vizhub.healthdata.org/gbd-results/ .","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13630716-s10","title":"Causes of death across the life course in the Association of Southeast Asian Nations (ASEAN): a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00159-3","url":"https://doi.org/10.1016/s2468-2667(26)00159-3","text":"Associated Data Supplementary Materials Supplementary appendix Data Availability Statement To download the input data used in these analyses, please visit the Global Health Data Exchange at http://ghdx.healthdata.org/gbd-2023 . All results from this study are publicly accessible. To download estimates produced in these analyses, please visit the GBD Results tool at https://vizhub.healthdata.org/gbd-results/ .","kind":"开放全文","locator":"Associated Data（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 ASEAN Causes of Death Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13630716/","licenseUrl":""}},{"id":"studio-library:PMC13641054","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Effects of a healthy checkout ordinance on the sale of soft drinks and confectionery in Berkeley, CA, USA: a quasi-experimental study.","summary":"Background Placing products in prominent locations such as store checkouts can promote impulse purchases of foods and beverages high in added sugars. On March 1, 2021, Berkeley, CA, USA, implemented the world's first mandatory healthy checkout ordinance (HCO), prohibiting the display of foods high in added sugars and sweetened beverages, including soft drinks (known as soda in the USA) and confectionery (known as candy in the USA), at store checkouts. The HCO was enforceable on Jan 1, 2022. We aimed to evaluate the association of the HCO with sales of soda (sweetened with sugar or non-sugar sweeteners) and candy in the city. Methods In this q","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13641054","title":"Effects of a healthy checkout ordinance on the sale of soft drinks and confectionery in Berkeley, CA, USA: a quasi-experimental study.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00169-6","url":"https://doi.org/10.1016/s2468-2667(26)00169-6","text":"Background Placing products in prominent locations such as store checkouts can promote impulse purchases of foods and beverages high in added sugars. On March 1, 2021, Berkeley, CA, USA, implemented the world's first mandatory healthy checkout ordinance (HCO), prohibiting the display of foods high in added sugars and sweetened beverages, including soft drinks (known as soda in the USA) and confectionery (known as candy in the USA), at store checkouts. The HCO was enforceable on Jan 1, 2022. We aimed to evaluate the association of the HCO with sales of soda (sweetened with sugar or non-sugar sweeteners) and candy in the city. Methods In this quasi-experimental study, we analysed Circana retail scanner data (March 3, 2019, to Dec 31, 2023) from 71 stores in Berkeley and three other comparison cities in California, restricted to chains operating in Berkeley. Data were point-of-sale records for selected food and beverage categories, which were aggregated by store and year-quarter for analysis. Primary outcomes were quarterly volume sales of soda and candy overall and by package size, using thresholds based on products commonly sold at checkouts. Using difference-in-differences regressions with store and year-quarter fixed effects, we estimated the association of the HCO with outcomes after its implementation and after it became enforceable. Findings Circana data included 71 (39%) of 183 identified standalone chain locations across the four cities: 19 (20%) of 93 supermarkets, 42 (59%) of 71 drugstores (ie, retail shops that combine a pharmacy with a general convenience store), and ten (53%) of 19 mass merchandisers. The analytical sample comprised more than 9 million product-store-month soda observations (1·0 million in Berkeley) and more than 52 million product-store-month candy observations (5·6 million in Berkeley), aggregated to 1420 store-quarter observations per outcome. After implementation, the estimated difference in mean soda sales was -2478 L per store-quarter (95% CI -5228 to 272; -23·4%), relative to comparison cities. After enforcement, soda sales were -3891 L per store-quarter (-7774 to -7; -39·5%). Estimates indicated reduced sales of large packages of soda after enforcement (-3800 L, -7571 to -30; -42·5%) and varied by store type. Overall candy sales increased after implementation (220 kg per store-quarter, 95% CI 21 to 420; 10·4%); after enforcement, the estimated difference was 156 kg (95% CI -78 to 390; 7·8%), with the 95% CI including the null. Supermarket sales of small packages of candy decreased by 132 kg after implementation (95% CI -212 to -51; -34·9%) and 156 kg after enforcement (-233 to -78; -43·2%). Interpretation Berkeley's mandatory HCO was found to be associated with decreased soda sales but not decreased overall candy sales after its enforcement. Estimates varied by product category, package size, and store type. Mandatory checkout standards can influence purchases, but implementation and enforcement strategies might need tailoring. Funding US National Institute of Diabetes and Digestive and Kidney Diseases and Bloomberg Philanthropies.","kind":"期刊文献","locator":"摘要","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article under the CC BY-NC-ND 4.0 license.","verified":"2026-10-23","authors":"White JS, Xu Z, Huang Y, Powell LM, Grummon AH, Falbe J.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13641054/","licenseUrl":"","openTextChunks":0}},{"id":"studio-library:PMC13598462","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Background Second-hand smoke (SHS) exposure remains a major source of morbidity and mortality among non-smokers. This study presents the first dedicated Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) publication to systematically quantify SHS prevalence and evaluate its effect as a global risk factor across a broader range of outcomes. Methods Using the GBD 2023 comparative risk assessment framework, we estimated SHS exposure prevalence and attributable disease burden across 204 countries and territories from 1990 to 2023, stratified by age and sex. Exposure estimates were derived by synthesising population-based surveys an","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13598462","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00168-4","url":"https://doi.org/10.1016/s2468-2667(26)00168-4","text":"Background Second-hand smoke (SHS) exposure remains a major source of morbidity and mortality among non-smokers. This study presents the first dedicated Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) publication to systematically quantify SHS prevalence and evaluate its effect as a global risk factor across a broader range of outcomes. Methods Using the GBD 2023 comparative risk assessment framework, we estimated SHS exposure prevalence and attributable disease burden across 204 countries and territories from 1990 to 2023, stratified by age and sex. Exposure estimates were derived by synthesising population-based surveys and household composition data through spatiotemporal Gaussian process regression. Relative risks for nine health outcomes, now including asthma, were estimated using the Burden of Proof methodology, and applied to calculate attributable deaths and disability-adjusted life-years (DALYs). Findings In 2023, an estimated 2·71 billion (95% UI 2·44-3·02) people worldwide were exposed to SHS, including 767 million (687-858) children aged 0-14 years. Age-standardised prevalence was highest in southeast Asia, east Asia, and Oceania (48·4% [44·0-53·4]), with female individuals in some countries experiencing nearly 1·8 times the exposure prevalence of male individuals. Despite a 22·2% (10·6-32·4) decline in global age-standardised prevalence since 1990, this progress has not been sufficient to reduce the absolute number of exposed individuals, which has remained stable globally and has risen sharply in sub-Saharan Africa (98·5% [62·0-144·4] increase) and North Africa and the Middle East (72·1% [47·8-101·8] increase). In 2023, SHS exposure accounted for 1·66 million (1·33-2·07) deaths and 44·8 million (35·8-54·3) DALYs globally. Ischaemic heart disease was the leading contributor to SHS-attributable DALYs (12·0 million [9·31-15·2]) overall, while lower respiratory infections predominated among children (5·16 million [3·48-7·12]). Interpretation We estimated that SHS remains a substantial driver of global health loss, particularly through cardiovascular and paediatric respiratory diseases. Persistent geographical disparities and growing absolute numbers of exposed individuals in several regions underscore the urgent need for accelerated implementation and enforcement of comprehensive tobacco control measures, particularly to protect women and children in both public and domestic environments. Funding Bloomberg Philanthropies and the Gates Foundation.","kind":"期刊文献","locator":"摘要","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 Second-hand smoke Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13598462/","licenseUrl":"","openTextChunks":11}},{"id":"studio-library:PMC13598462-s0","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Introduction Tobacco use remains a leading contributor to the global burden of disease, accounting for an estimated 8 million deaths in 2023. 1 A substantial part of this burden falls on non-smokers exposed to second-hand smoke (SHS) through mechanisms similar to those of active smoking. SHS combines the sidestream smoke from the burning tip of tobacco products with the smoke exhaled by smokers. 2 The sidestream component, which dominates the air non-smokers breathe, is unfiltered and incompletely combusted, carrying higher per-unit concentrations of the toxic and carcinogenic compounds than mainstream smoke inhaled by the smoker. 3 Through o","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13598462-s0","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00168-4","url":"https://doi.org/10.1016/s2468-2667(26)00168-4","text":"Introduction Tobacco use remains a leading contributor to the global burden of disease, accounting for an estimated 8 million deaths in 2023. 1 A substantial part of this burden falls on non-smokers exposed to second-hand smoke (SHS) through mechanisms similar to those of active smoking. SHS combines the sidestream smoke from the burning tip of tobacco products with the smoke exhaled by smokers. 2 The sidestream component, which dominates the air non-smokers breathe, is unfiltered and incompletely combusted, carrying higher per-unit concentrations of the toxic and carcinogenic compounds than mainstream smoke inhaled by the smoker. 3 Through oxidative stress, endothelial dysfunction, and chronic inflammation, these exposures raise the risk of cardiovascular, respiratory, neoplastic, and metabolic disease. 4 , 5 , 6 Children are especially susceptible, owing to developing airways, an immature immune system, and higher minute ventilation per body mass. 7 These adverse health effects have been recognised for decades, and the scientific consensus that no safe level of exposure exists has made SHS a central focus of tobacco control. This priority was formalised by the 2003 WHO Framework Convention on Tobacco Control (FCTC), an international public health treaty whose Article 8 calls for smoke-free legislation protecting non-smokers in workplaces, public transport, and indoor public places. Yet, global enforcement and coverage remain inconsistent: approximately 70% of the world's population is not protected by comprehensive smoke-free laws, and domestic settings, where exposure tends to be most prolonged, remain largely unregulated. 8 Research in context Evidence before this study Exposure to second-hand smoke (SHS) is a globally recognised health risk, with no safe level of inhalation. To assess the existing body of evidence on SHS exposure and disease burden, we searched PubMed for articles published from Jan 1, 2010 to Dec 31, 2025, using the terms (“second-hand smoke” OR “environmental smoke”) AND (“prevalence” OR “epidemiology”) AND (“disease burden” OR “mortality” OR “DALYs” OR “YLL” OR “disability-adjusted life-years”) AND (“global” OR “national” OR “regional”).","kind":"开放全文","locator":"Introduction（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 Second-hand smoke Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13598462/","licenseUrl":""}},{"id":"studio-library:PMC13598462-s1","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Methods Overview As part of GBD 2023, we estimated the burden of disease attributable to SHS exposure from 1990 to 2023 using the comparative risk assessment framework for 204 countries and territories, by age and sex. Exposure was defined as any current exposure to smoke from combustible tobacco products at home or in the workplace; exposure occurring exclusively in other settings (eg, bars, restaurants, public transport, and private vehicles) is not captured. The population at risk was restricted to non-smokers and occasional smokers; all daily smokers were classified as unexposed to avoid overlap with active smoking. The analysis comprised","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13598462-s1","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00168-4","url":"https://doi.org/10.1016/s2468-2667(26)00168-4","text":"Methods Overview As part of GBD 2023, we estimated the burden of disease attributable to SHS exposure from 1990 to 2023 using the comparative risk assessment framework for 204 countries and territories, by age and sex. Exposure was defined as any current exposure to smoke from combustible tobacco products at home or in the workplace; exposure occurring exclusively in other settings (eg, bars, restaurants, public transport, and private vehicles) is not captured. The population at risk was restricted to non-smokers and occasional smokers; all daily smokers were classified as unexposed to avoid overlap with active smoking. The analysis comprised three steps: estimating prevalence of exposure to SHS by age, sex, geography, and time; determining the relative risk (RR) of nine health outcomes associated with SHS exposure; and calculating population-attributable fractions (PAFs) and attributable burden. This study adheres to GATHER ( appendix, pp 16–17 ) 11 and GBD 2023 is registered with the University of Washington Institutional Review Board (study number 9060). Data sources To estimate prevalence of SHS exposure, we combined cross-sectional self-reported workplace exposure and household-level age and sex composition data with GBD 2023 daily smoking prevalence estimates to compute the probability of residing with at least one daily smoker ( appendix pp 6–8 ). Following previous GBD iterations, 12 we assumed that all non-smokers or occasional smokers living with a daily smoker are exposed to SHS within the home. For workplace exposure, we extracted 395 population-based, representative sources (91 new in GBD 2023) covering 119 of 204 countries and territories. For household composition, we incorporated 938 sources (87 new) covering 173 of 204 locations. Geographical coverage and recency are provided in the appendix (pp 19–20) . A complete list of data sources is available through the Global Health Data Exchange . Modelling strategy We implemented a modelling strategy similar to that described in the GBD 2021 Risk Factors Capstone. 12 Self-reported microdata on workplace SHS exposure were used in a spatiotemporal Gaussian process regression (ST-GPR) 1 to generate a comp","kind":"开放全文","locator":"Methods（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 Second-hand smoke Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13598462/","licenseUrl":""}},{"id":"studio-library:PMC13598462-s2","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Results In 2023, an estimated 2·71 billion (95% UI 2·44–3·02) individuals worldwide were exposed to SHS, of whom 1·49 billion (1·35–1·65) were female and 767 million (687–858) were children aged 0–14 years ( table 1 ; appendix p 24 ). Age-standardised prevalence of SHS exposure was estimated at 33·9% (30·5–37·8) globally for both sexes combined, 37·3% (33·7–41·4) for females, and 30·6% (27·5–34·2) for males ( table 1 ). Across the GBD super-regions, southeast Asia, east Asia, and Oceania observed the largest age-standardised prevalence at 48·4% (44·0–53·4), followed by North Africa and the Middle East at 38·8% (34·9–43·2), and central Europe,","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13598462-s2","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00168-4","url":"https://doi.org/10.1016/s2468-2667(26)00168-4","text":"Results In 2023, an estimated 2·71 billion (95% UI 2·44–3·02) individuals worldwide were exposed to SHS, of whom 1·49 billion (1·35–1·65) were female and 767 million (687–858) were children aged 0–14 years ( table 1 ; appendix p 24 ). Age-standardised prevalence of SHS exposure was estimated at 33·9% (30·5–37·8) globally for both sexes combined, 37·3% (33·7–41·4) for females, and 30·6% (27·5–34·2) for males ( table 1 ). Across the GBD super-regions, southeast Asia, east Asia, and Oceania observed the largest age-standardised prevalence at 48·4% (44·0–53·4), followed by North Africa and the Middle East at 38·8% (34·9–43·2), and central Europe, eastern Europe, and central Asia at 37·7% (33·5–42·5; table 1 ). The country with the most individuals exposed to SHS was China (664 million [592–751]), followed by India (444 million [395–497]) and Indonesia (142 million [134–150]; appendix pp 25–40 ). In contrast, Kiribati, Tuvalu, and the Federated States of Micronesia had among the highest age-standardised prevalence, each exceeding 60% ( appendix pp 41–56 ). Table 1 Second-hand smoke age-standardised prevalence (A) and population exposed (B) by sex and percentage change between 1990 and 2023 Female Male Both sexes 2023 1990 Percentage change 2023 1990 Percentage change 2023 1990 Percentage change Age-standardised prevalence (95% UI) Global 37·3 (33·7 to 41·4) 50·6 (46·7 to 54·4) −26·4% (−35·0 to −16·9) 30·6 (27·5 to 34·2) 36·6 (33·0 to 40·2) −16·4% (−28·8 to −1·6) 33·9 (30·5 to 37·8) 43·6 (39·9 to 47·3) −22·2% (−32·4 to −10·6) Southeast Asia, East Asia, and Oceania 59·5 (54·7 to 64·7) 65·9 (61·2 to 70·2) −9·7% (−18·5 to 0·2) 37·4 (33·3 to 42·2) 39·2 (34·9 to 43·5) −4·5% (−17·7 to 11·8) 48·4 (44·0 to 53·4) 52·5 (48·1 to 56·9) −7·8% (−18·3 to 4·4) Central Europe, Eastern Europe, and central Asia 40·7 (36·3 to 45·6) 52·4 (47·6 to 57·4) −22·4% (−33·1 to −10·0) 34·7 (30·7 to 39·3) 38·3 (34·1 to 42·9) −9·4% (−23·6 to 7·6) 37·7 (33·5 to 42·5) 45·4 (40·8 to 50·2) −16·9% (−29·0 to −2·8) High income 27·5 (25·5 to 29·7) 45·3 (41·9 to 48·8) −39·4% (−45·2 to −32·6) 30·6 (27·5 to 34·4) 40·1 (36·4 to 43·9) −23·6% (−34·7 to −10·6) 29·0 (26·5 to 32·0) 42·7 (39·2 to 46·3) −32·0% (−40·3 ","kind":"开放全文","locator":"Results（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 Second-hand smoke Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13598462/","licenseUrl":""}},{"id":"studio-library:PMC13598462-s3","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Discussion This study provides updated quantitative insights into the global prevalence, mortality, and disability associated with SHS exposure by age and sex from 1990 to 2023. Over these 33 years, age-standardised prevalence declined from 43·6% (39·9–47·3) to 33·9% (30·5–37·8), a relative reduction of 22·2% (10·6–32·4), yet population growth kept the absolute number of individuals exposed broadly stable. Marked geographical disparities persist, with prevalence approaching 50% in southeast Asia, east Asia, and Oceania. Children and female individuals also remain disproportionately exposed—767 million (687–858) children aged 0–14 years and 1·","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13598462-s3","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00168-4","url":"https://doi.org/10.1016/s2468-2667(26)00168-4","text":"Discussion This study provides updated quantitative insights into the global prevalence, mortality, and disability associated with SHS exposure by age and sex from 1990 to 2023. Over these 33 years, age-standardised prevalence declined from 43·6% (39·9–47·3) to 33·9% (30·5–37·8), a relative reduction of 22·2% (10·6–32·4), yet population growth kept the absolute number of individuals exposed broadly stable. Marked geographical disparities persist, with prevalence approaching 50% in southeast Asia, east Asia, and Oceania. Children and female individuals also remain disproportionately exposed—767 million (687–858) children aged 0–14 years and 1·49 billion (1·35–1·65) female individuals were exposed to SHS in 2023. SHS exposure was associated with ischaemic heart disease, lower respiratory infections, COPD, and other diseases, accounting for 1·66 million (1·33–2·07) deaths, 39·1 million (31·4–48·1) YLLs, 5·73 million (3·83–7·50) YLDs, and 44·8 million (35·8–54·3) DALYs in 2023. The age-standardised DALY rate fell more than twice as fast as exposure prevalence, reflecting concurrent declines in the background rates of the SHS-attributable diseases rather than reduced exposure alone. These findings underscore the persistent gap in protecting non-smokers from the harms of SHS. Reducing population exposure to SHS is a cornerstone of global tobacco control efforts. Progress under Article 8 of the FCTC and the WHO MPOWER framework, whose protect component directly addresses SHS, has been measurable but uneven. 15 As of 2024, 79 countries have achieved the highest standard in the protect measure, with all public places completely smoke-free or at least 90% of the population covered by comprehensive subnational legislation, despite ongoing variability in compliance. Consequently, the proportion of the global population covered by comprehensive smoke-free legislation has increased from 3% in 2007 to 33% in 2024, now covering 2·6 billion people. 8 The Americas stand out for both the scale of policy implementation and the magnitude of estimated reductions in exposure. Of the 79 countries attaining the highest level of achievement in smoke-free protections globally, 24 are in t","kind":"开放全文","locator":"Discussion（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 Second-hand smoke Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13598462/","licenseUrl":""}},{"id":"studio-library:PMC13598462-s4","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Declaration of interests S Afzal reports support for the present manuscript from the Institute of Public Health, Lahore (study materials, manuscripts, medical writing, and library resources); grants or contracts from the Dean's Office, Institute of Public Health, Lahore (intellectual contributions and time protection); payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from the Dean, Institute of Public Health, Lahore; support for attending meetings or travel from the Dean, Institute of Public Health, Lahore; participation on a Data Safety Monitoring Board or Advisory Board as Member, ","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13598462-s4","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00168-4","url":"https://doi.org/10.1016/s2468-2667(26)00168-4","text":"Declaration of interests S Afzal reports support for the present manuscript from the Institute of Public Health, Lahore (study materials, manuscripts, medical writing, and library resources); grants or contracts from the Dean's Office, Institute of Public Health, Lahore (intellectual contributions and time protection); payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from the Dean, Institute of Public Health, Lahore; support for attending meetings or travel from the Dean, Institute of Public Health, Lahore; participation on a Data Safety Monitoring Board or Advisory Board as Member, Pakistan National Bioethics Committee; Member, Institutional Review Board of Fatima Jinnah Medical University; Member, Ethical Review Board and Data Monitoring Board, Institute of Public Health Lahore Pakistan; in charge, Clinical Research Organization, King Edward Medical University; and Member, Annals of King Edward Medical University Advisory Board; leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid as Member, Pakistan Higher Education Commission Research Committee; Member, Pakistan Medical and Dental Commission Research and Journals Committee; Member, Pakistan National Bioethics Committee; Member, Pakistan Society of Internal Medicine; Member, Pakistan Association of Medical Editors; Member, Medical Microbiology and Infectious Diseases Society; Fellow of Leads International; Fellow of Faculty of Public Health UK; and Fellow of College of Physicians and Surgeons Pakistan; receipt of equipment, materials, drugs, medical writing, gifts or other services from Bergen University, Norway (computer software and equipment for research writing); and other financial or non-financial interests as Dean, Public Health Institute of Public Health Birdwood Lahore; Member, National Bioethics Committee Pakistan; and Member, Prime Minister Taskforce on HCV, HIV and Infectious Diseases; outside the submitted work. M Alhaj Moustafa reports grants or contracts from Genmab (research grant); consulting fees from ADC Therapeutics and Abbvie (payment to self), and from Genetech (payment to institution","kind":"开放全文","locator":"Declaration of interests（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 Second-hand smoke Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13598462/","licenseUrl":""}},{"id":"studio-library:PMC13598462-s5","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Acknowledgments This paper was developed as part of the GBD Collaborator Network and GBD Protocol with support from the GBD Secretariat, Institute for Health Metrics and Evaluation (IHME), and the GBD Collaborator Network under the IHME identification number 6096. This study was funded by Bloomberg Philanthropies and the Gates Foundation. This paper was checked with the assistance of Claude (version Sonnet 4.5, Anthropic, San Francisco, CA, USA) for a series of Lancet , GBD, and IHME compliance issues, including formatting guidelines, word counts, spelling, grammar, British English conventions, and middle-dot notation. AI was not used in the ","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13598462-s5","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00168-4","url":"https://doi.org/10.1016/s2468-2667(26)00168-4","text":"Acknowledgments This paper was developed as part of the GBD Collaborator Network and GBD Protocol with support from the GBD Secretariat, Institute for Health Metrics and Evaluation (IHME), and the GBD Collaborator Network under the IHME identification number 6096. This study was funded by Bloomberg Philanthropies and the Gates Foundation. This paper was checked with the assistance of Claude (version Sonnet 4.5, Anthropic, San Francisco, CA, USA) for a series of Lancet , GBD, and IHME compliance issues, including formatting guidelines, word counts, spelling, grammar, British English conventions, and middle-dot notation. AI was not used in the drafting of original content, data analysis, or interpretation of results. All scientific judgements, interpretations, and editorial decisions are those of the named authors, who take full responsibility for the content of the paper. Editorial note: The Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations.","kind":"开放全文","locator":"Acknowledgments（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 Second-hand smoke Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13598462/","licenseUrl":""}},{"id":"studio-library:PMC13598462-s6","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Acknowledgments Luisa S Flor, Jason A Anderson, Brooks W Morgan, Marie Ng, Bhoomadevi A, Hasan Aalruz, Ukachukwu O Abaraogu, Samar Abd ElHafeez, Michael Abdelmasseh, Meriem Abdoun, Zinat Abdrakhmanova, Rashad Abdul-Ghani, Toufik Abdul-Rahman, Asrat Agalu Abejew, Olugbenga Olusola Abiodun, Olumide Abiodun, Richard Gyan Aboagye, Nagah M Abourashed, Dariush Abtahi, Fuad Hamdi A Abuadas, Aminu Kende Abubakar, Bilyaminu Abubakar, Rana Kamal Abu-Farha, Hana J Abukhadijah, Aseel Aburub, Salahdein Aburuz, Apurba Acharya, Kiran Acharya, Lisa C Adams, Tajudeen Adesanmi Adebisi, Babatope Oluwadamilare Adebiyi, Oluwatobi E Adegbile, Adekola George Adepoj","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13598462-s6","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00168-4","url":"https://doi.org/10.1016/s2468-2667(26)00168-4","text":"Acknowledgments Luisa S Flor, Jason A Anderson, Brooks W Morgan, Marie Ng, Bhoomadevi A, Hasan Aalruz, Ukachukwu O Abaraogu, Samar Abd ElHafeez, Michael Abdelmasseh, Meriem Abdoun, Zinat Abdrakhmanova, Rashad Abdul-Ghani, Toufik Abdul-Rahman, Asrat Agalu Abejew, Olugbenga Olusola Abiodun, Olumide Abiodun, Richard Gyan Aboagye, Nagah M Abourashed, Dariush Abtahi, Fuad Hamdi A Abuadas, Aminu Kende Abubakar, Bilyaminu Abubakar, Rana Kamal Abu-Farha, Hana J Abukhadijah, Aseel Aburub, Salahdein Aburuz, Apurba Acharya, Kiran Acharya, Lisa C Adams, Tajudeen Adesanmi Adebisi, Babatope Oluwadamilare Adebiyi, Oluwatobi E Adegbile, Adekola George Adepoju, Daniel Adeyemi Adepoju, Miracle Ayomikun Adesina, Wirawan Adikusuma, Bereket Alemayehu Admasu, Aanuoluwapo Adeyimika Afolabi, Mahnaz Afshari, Muhammad Sohail Afzal, Saira Afzal, Dhiraj Motilal Agarwal, Feleke Doyore Agide, Antonella Agodi, Abdul Momin Rizwan Ahmad, Aqeel Ahmad, Danish Ahmad, Khabir Ahmad, Saheem Ahmad, Sajjad Ahmad, Suhaib Ahmad, Ali Ahmed, Ayman Ahmed, Haroon Ahmed, Jahanzaib Mian Ahmed, Khalid A Ahmed, Md Faisal Ahmed, Mohamed Mustaf Ahmed, Mohamed Sherif Ali Ahmed, Shahzaib Ahmed, Damilare Michael Akintunde, Tomi Akinyemiju, Hammad Akram, Salah Al Awaidy, Mohammad Al Qadire, Yazan Al Thaher, Mohammad Ahmmad Mahmoud Al Zoubi, Muaaz M Alajlani, Mohammed Sultan Al-Ak’hali, Mohammad Khursheed Alam, Mohammad Jahoor Alam, Rasmieh Mustafa Al-Amer, Ruaa A Alamoudi, Shams MF Al-Anzi, Fahmi Y Al-Ashwal, Ahmad Al-Azayzih, Ahmad M Al-Bashaireh, Mohammed Albashtawy, Abdulelah Mastour Aldhahir, Mohammed S Aldossary, Raouf Alebshehy, Ali M Alfalki, Mohammed Ridha Algethami, Saeed M M Alghamdi, Khalid F Alhabib, Majdi MB Alhadidi, Muhamad Alhaj Moustafa, Baidaa Alhalabi, Fadwa Naji Alhalaiqa, Amidu Alhassan, Mohammad Jamil Mohammad Alhawajreh, Ashraf Alhumaidi, Inayat Ali, Kamran Ali, Mohammad Daud Ali, Rafat Ali, Syed Shujait Ali, Montaha Al-Iede, Sheikh Mohammad Alif, Syed Mohamed Aljunid, Mohammed Ahmed Al-Kaif, Zaid I Alkhatib, Hazim Alkousheh, Sameer Abdulmalik Alkubati, Wesam Taher Almagharbeh, Fahad Awwadh Almarshadi, Abdullah Al-Maruf, Sabah Al-Marwani, Mohmmad Minwer Alnaeem, Mahmoud A Alomari, Mohammad R Alo","kind":"开放全文","locator":"Acknowledgments（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 Second-hand smoke Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13598462/","licenseUrl":""}},{"id":"studio-library:PMC13598462-s7","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Contributor Information GBD 2023 Second-hand smoke Collaborators: Luisa S Flor , Jason A Anderson , Brooks W Morgan , Marie Ng , Bhoomadevi A C , Hasan Aalruz , Ukachukwu O Abaraogu , Samar Abd ElHafeez , Michael Abdelmasseh , Meriem Abdoun , Zinat Abdrakhmanova , Rashad Abdul-Ghani , Toufik Abdul-Rahman , Asrat Agalu Abejew , Olugbenga Olusola Abiodun , Olumide Abiodun , Richard Gyan Aboagye , Nagah M Abourashed , Dariush Abtahi , Fuad Hamdi A Abuadas , Aminu Kende Abubakar , Bilyaminu Abubakar , Rana Kamal Abu-Farha , Hana J Abukhadijah , Aseel Aburub , Salahdein Aburuz , Apurba Acharya , Kiran Acharya , Lisa C Adams , Tajudeen Adesanmi Ade","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13598462-s7","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00168-4","url":"https://doi.org/10.1016/s2468-2667(26)00168-4","text":"Contributor Information GBD 2023 Second-hand smoke Collaborators: Luisa S Flor , Jason A Anderson , Brooks W Morgan , Marie Ng , Bhoomadevi A C , Hasan Aalruz , Ukachukwu O Abaraogu , Samar Abd ElHafeez , Michael Abdelmasseh , Meriem Abdoun , Zinat Abdrakhmanova , Rashad Abdul-Ghani , Toufik Abdul-Rahman , Asrat Agalu Abejew , Olugbenga Olusola Abiodun , Olumide Abiodun , Richard Gyan Aboagye , Nagah M Abourashed , Dariush Abtahi , Fuad Hamdi A Abuadas , Aminu Kende Abubakar , Bilyaminu Abubakar , Rana Kamal Abu-Farha , Hana J Abukhadijah , Aseel Aburub , Salahdein Aburuz , Apurba Acharya , Kiran Acharya , Lisa C Adams , Tajudeen Adesanmi Adebisi , Babatope Oluwadamilare Adebiyi , Oluwatobi E Adegbile , Adekola George Adepoju , Daniel Adeyemi Adepoju , Miracle Ayomikun Adesina , Wirawan Adikusuma , Bereket Alemayehu Admasu , Aanuoluwapo Adeyimika Afolabi , Mahnaz Afshari , Muhammad Sohail Afzal , Saira Afzal , Dhiraj Motilal Agarwal , Feleke Doyore Agide , Antonella Agodi , Abdul Momin Rizwan Ahmad , Aqeel Ahmad , Danish Ahmad , Khabir Ahmad , Saheem Ahmad , Sajjad Ahmad , Suhaib Ahmad , Ali Ahmed , Ayman Ahmed , Haroon Ahmed , Jahanzaib Mian Ahmed , Khalid A Ahmed , Md Faisal Ahmed , Mohamed Mustaf Ahmed , Mohamed Sherif Ali Ahmed , Shahzaib Ahmed , Damilare Michael Akintunde , Tomi Akinyemiju , Hammad Akram , Salah Al Awaidy , Mohammad Al Qadire , Yazan Al Thaher , Mohammad Ahmmad Mahmoud Al Zoubi , Muaaz M Alajlani , Mohammed Sultan Al-Ak'hali , Mohammad Khursheed Alam , Mohammad Jahoor Alam , Rasmieh Mustafa Al-Amer , Ruaa A Alamoudi , Shams MF Al-Anzi , Fahmi Y Al-Ashwal , Ahmad Al-Azayzih , Ahmad M Al-Bashaireh , Mohammed Albashtawy , Abdulelah Mastour Aldhahir , Mohammed S Aldossary , Raouf Alebshehy , Ali M Alfalki , Mohammed Ridha Algethami , Saeed M M Alghamdi , Khalid F Alhabib , Majdi MB Alhadidi , Muhamad Alhaj Moustafa , Baidaa Alhalabi , Fadwa Naji Alhalaiqa , Amidu Alhassan , Mohammad Jamil Mohammad Alhawajreh , Ashraf Alhumaidi , Inayat Ali , Kamran Ali , Mohammad Daud Ali , Rafat Ali , Syed Shujait Ali , Montaha Al-Iede , Sheikh Mohammad Alif , Syed Mohamed Aljunid , Mohammed Ahmed Al-Kaif , Zaid I Alkhatib , Hazim Alkousheh , Sameer Abdulmalik","kind":"开放全文","locator":"Contributor Information（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 Second-hand smoke Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13598462/","licenseUrl":""}},{"id":"studio-library:PMC13598462-s8","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Supplementary Material Supplementary appendix","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13598462-s8","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00168-4","url":"https://doi.org/10.1016/s2468-2667(26)00168-4","text":"Supplementary Material Supplementary appendix","kind":"开放全文","locator":"Supplementary Material（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 Second-hand smoke Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13598462/","licenseUrl":""}},{"id":"studio-library:PMC13598462-s9","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","summary":"References 1. Hay SI, Ong KL, Santomauro DF, et al. Burden of 375 diseases and injuries, risk-attributable burden of 88 risk factors, and healthy life expectancy in 204 countries and territories, including 660 subnational locations, 1990–2023: a systematic analysis for the Global Burden of Disease Study 2023. Lancet. 2025;406:1873–1922. doi: 10.1016/S0140-6736(25)01637-X. 2. US Department of Health and Human Services . US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; Atlanta, GA: 2010. How tobacco smoke cau","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13598462-s9","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00168-4","url":"https://doi.org/10.1016/s2468-2667(26)00168-4","text":"References 1. Hay SI, Ong KL, Santomauro DF, et al. Burden of 375 diseases and injuries, risk-attributable burden of 88 risk factors, and healthy life expectancy in 204 countries and territories, including 660 subnational locations, 1990–2023: a systematic analysis for the Global Burden of Disease Study 2023. Lancet. 2025;406:1873–1922. doi: 10.1016/S0140-6736(25)01637-X. 2. US Department of Health and Human Services . US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; Atlanta, GA: 2010. How tobacco smoke causes disease: the biology and behavioral basis for smoking-attributable disease: a report of the Surgeon General. 3. International Agency for Research on Cancer Working Group on the Evaluation of Carcinogenic Risks to Humans. WHO Tobacco smoke and involuntary smoking. 2004. https://monographs.iarc.fr/ENG/Monographs/vol83/mono83.pdf 4. Feng S, Liu R, Thompson C, Colwell B. The associations between secondhand smoke exposure and various cardiovascular diseases: a meta-analysis. Nicotine Tob Res. 2025;28:318–327. doi: 10.1093/ntr/ntaf111. 5. Possenti I, Romelli M, Carreras G, et al. Association between second-hand smoke exposure and lung cancer risk in never-smokers: a systematic review and meta-analysis. Eur Respir Rev. 2024;33 doi: 10.1183/16000617.0077-2024. 6. Flor LS, Anderson JA, Ahmad N, et al. Health effects associated with exposure to secondhand smoke: a Burden of Proof study. Nat Med. 2024;30:149–167. doi: 10.1038/s41591-023-02743-4. 7. US Department of Health and Human Services . US Department of Health and Human Services, Public Health Service, Office of the Surgeon General; Atlanta, GA: 2006. The health consequences of involuntary exposure to tobacco smoke: a report of the Surgeon General. 8. WHO . World Health Organization; Geneva: 2025. WHO report on the global tobacco epidemic, 2025: warning about the dangers of tobacco. 9. Oberg M, Jaakkola MS, Woodward A, Peruga A, Prüss-Ustün A. Worldwide burden of disease from exposure to second-hand smoke: a retrospective analysis of data from 192 countries. Lancet. 2011;377:","kind":"开放全文","locator":"References（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 Second-hand smoke Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13598462/","licenseUrl":""}},{"id":"studio-library:PMC13598462-s10","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Associated Data Supplementary Materials Supplementary appendix Data Availability Statement This study follows GATHER. For detailed information on data sources and estimates, please visit the Global Health Data Exchange GBD 2023 website at http://ghdx.healthdata.org/gbd-2023.","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13598462-s10","title":"Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00168-4","url":"https://doi.org/10.1016/s2468-2667(26)00168-4","text":"Associated Data Supplementary Materials Supplementary appendix Data Availability Statement This study follows GATHER. For detailed information on data sources and estimates, please visit the Global Health Data Exchange GBD 2023 website at http://ghdx.healthdata.org/gbd-2023.","kind":"开放全文","locator":"Associated Data（节选）","license":"This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 Second-hand smoke Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13598462/","licenseUrl":""}},{"id":"studio-library:PMC13501145","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Global and regional progress towards reduced burden of oral conditions between 2019 and 2023: a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Background WHO's Global Oral Health Action Plan (GOHAP) targets include a 10% relative reduction in the combined prevalence of main oral conditions by 2030. The first GOHAP evaluation was planned for 2023, with subsequent evaluations scheduled for 2026 and 2030. The aim of this systematic analysis of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 was to evaluate global and regional progress (2019-23) towards this target and related complementary indicators. Methods Using modelled estimates from epidemiological surveys and registries in 204 countries and territories, we estimated the age-standardised prevalence of u","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13501145","title":"Global and regional progress towards reduced burden of oral conditions between 2019 and 2023: a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00143-x","url":"https://doi.org/10.1016/s2468-2667(26)00143-x","text":"Background WHO's Global Oral Health Action Plan (GOHAP) targets include a 10% relative reduction in the combined prevalence of main oral conditions by 2030. The first GOHAP evaluation was planned for 2023, with subsequent evaluations scheduled for 2026 and 2030. The aim of this systematic analysis of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 was to evaluate global and regional progress (2019-23) towards this target and related complementary indicators. Methods Using modelled estimates from epidemiological surveys and registries in 204 countries and territories, we estimated the age-standardised prevalence of untreated caries in deciduous and permanent teeth, severe periodontitis, edentulism, other oral disorders and orofacial clefts, and the age-standardised incidence of lip and oral cavity cancer. Progress towards GOHAP targets was measured by comparing the percentage change from 2019 to 2023 in the above indicators against the stated GOHAP target of a 10% relative reduction. Findings Globally, the age-standardised prevalence of main oral conditions declined (-1·55% [95% uncertainty interval -2·32 to -0·82]) between 2019 and 2023, but global cases increased by 3·07% (2·32 to 3·75), from 3·62 billion (3·35 to 3·95) to 3·73 billion (3·45 to 4·06). Among complementary indicators, reductions in age-standardised prevalence were observed for untreated caries in permanent teeth (-1·99% [-2·49 to -1·40]) whereas the age-standardised incidence of lip and oral cavity cancer increased (8·35% [0·13 to 16·32]), as did the age-standardised prevalence of orofacial clefts (2·25% [0·77 to 3·83]). Progress was heterogeneous across GBD super-regions, with south Asia having the largest reductions in the age-standardised prevalence of main oral conditions (-3·34% [-4·95 to -1·80]) and untreated caries in deciduous teeth (-9·09% [-17·62 to -0·08]). Sub-Saharan Africa showed the largest increase in prevalent cases of main oral conditions (9·51% [8·09-10·94]) and age-standardised incidence of lip and oral cavity cancer (14·86% [1·19-32·45]). The high-income super-region had an increase in the age-standardised prevalence of main oral conditions (1·24% [0·56-1·96]) and untreated caries in deciduous teeth (9·21% [6·60-11·89]). Interpretation Our modelled estimates of global trends in the prevalence of oral conditions from 2019 to 2023 suggest that early progress is modest and, at the current pace, insufficient to meet the 2030 GOHAP targets. Accelerated, equity-focused prevention and integration of essential oral health-care services within primary care and universal health coverage are needed, alongside strengthened oral cancer control and congenital anomaly care. Funding The Gates Foundation.","kind":"期刊文献","locator":"摘要","license":"https://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 Oral Disorders Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13501145/","licenseUrl":"","openTextChunks":5}},{"id":"studio-library:PMC13501145-s0","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Global and regional progress towards reduced burden of oral conditions between 2019 and 2023: a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Introduction Oral diseases are a foundational yet persistently underprioritised component of global health, with far-reaching implications for quality of life, health equity, and health systems worldwide. 1 , 2 , 3 Despite being largely preventable, the burden of oral diseases has remained high over the past three decades, 4 and its economic and social costs underscore the urgent need for robust, equitable global strategies. 5 Recognition of oral diseases within global public health agendas, including WHO's Global Oral Health Action Plan (GOHAP) and related policy initiatives, 6 , 7 reflects an imperative to embed oral disease prevention and ","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13501145-s0","title":"Global and regional progress towards reduced burden of oral conditions between 2019 and 2023: a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00143-x","url":"https://doi.org/10.1016/s2468-2667(26)00143-x","text":"Introduction Oral diseases are a foundational yet persistently underprioritised component of global health, with far-reaching implications for quality of life, health equity, and health systems worldwide. 1 , 2 , 3 Despite being largely preventable, the burden of oral diseases has remained high over the past three decades, 4 and its economic and social costs underscore the urgent need for robust, equitable global strategies. 5 Recognition of oral diseases within global public health agendas, including WHO's Global Oral Health Action Plan (GOHAP) and related policy initiatives, 6 , 7 reflects an imperative to embed oral disease prevention and health care in broader health systems to achieve sustainable improvements in population health. 8 , 9 , 10 The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides one of the few globally comparable resources for estimating the oral disease burden across locations and over time. 11 , 12 Data from GBD 2019 were instrumental in setting evidence-based targets within the WHO GOHAP 2023–2030. GOHAP is a call to action that outlines 11 global targets, six strategic objectives, and 100 recommended actions to be achieved by 2030. 6 The GOHAP monitoring framework establishes two key overarching global targets: to achieve universal coverage for oral health-care services (target A) and to reduce the oral disease burden (target B). Specifically, target B calls for a relative reduction of 10% by 2030 in the combined global prevalence of the main oral conditions over the life course. Ten complementary indicators accompany overarching global target B, six of which relate to reductions in the prevalence of untreated caries of deciduous teeth (indicator B.4), untreated caries of permanent teeth (B.5), severe periodontitis (B.6), edentulism (B.7), and orofacial clefts (B.9), and reductions in the incidence of lip and oral cavity cancer (B.8). WHO used data from GBD 2019 to establish baseline measures for the GOHAP targets. The first evaluation of GOHAP was scheduled for 2023, with follow-up assessments planned for 2026 and 2029–30. 6 Research in context Evidence before this study Oral conditions are highly prevalent and ","kind":"开放全文","locator":"Introduction（节选）","license":"https://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 Oral Disorders Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13501145/","licenseUrl":""}},{"id":"studio-library:PMC13501145-s1","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Global and regional progress towards reduced burden of oral conditions between 2019 and 2023: a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Methods Study overview This study was designed as a monitoring and accountability analysis of progress towards the GOHAP targets, rather than as a causal assessment of epidemiological change or policy effectiveness. Accordingly, the GBD estimates should be interpreted as modelled approximations that integrate diverse data sources and rely on explicit statistical assumptions. Additionally, the short observation window (2019–23), chosen to align with the GOHAP initial monitoring period, might limit the ability to detect structural changes in disease patterns and should be understood as reflecting early progress rather than long-term trends. Thi","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13501145-s1","title":"Global and regional progress towards reduced burden of oral conditions between 2019 and 2023: a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00143-x","url":"https://doi.org/10.1016/s2468-2667(26)00143-x","text":"Methods Study overview This study was designed as a monitoring and accountability analysis of progress towards the GOHAP targets, rather than as a causal assessment of epidemiological change or policy effectiveness. Accordingly, the GBD estimates should be interpreted as modelled approximations that integrate diverse data sources and rely on explicit statistical assumptions. Additionally, the short observation window (2019–23), chosen to align with the GOHAP initial monitoring period, might limit the ability to detect structural changes in disease patterns and should be understood as reflecting early progress rather than long-term trends. This study was developed as part of the GBD Collaborator Network and GBD Protocol, 13 with support from the GBD Secretariat, the Institute for Health Metrics and Evaluation (IHME), and the GBD Oral Disorders Collaborator Network under IHME ID 6181. Ethical approval was not required for this study. The complete GBD methodology is available in the primary reports of GBD 2023 and their accompanying appendices. 14 , 15 Our study complies with the GATHER statement, 8 and the GATHER checklist is available in the appendix (p 38) . Data sources Seven oral conditions were evaluated: untreated caries in deciduous teeth; untreated caries in permanent teeth; severe periodontitis; edentulism; other oral disorders; lip and oral cavity cancer; and orofacial clefts. Case definitions and corresponding ICD-10 codes are shown in table 1 . The term main oral conditions refers to the combined group of untreated caries, severe periodontitis, edentulism, and other oral disorders. Table 1 Case definitions and key modelling inputs for each oral condition Untreated caries in deciduous teeth Untreated caries in permanent teeth Severe periodontitis Edentulism Other oral disorders Lip and oral cavity cancer Orofacial clefts Reference definition Coronal cavity at dentin level or root cavity in cementum Coronal cavity at dentin level or root cavity in cementum PPD ≥6 mm (CPITN 4 or CPI 4 or PPD >5 mm) or CAL ≥6 mm No remaining permanent teeth from clinical oral examination Dental, tongue, or jaw disorders not included elsewhere Malignant neoplasms of the lip","kind":"开放全文","locator":"Methods（节选）","license":"https://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 Oral Disorders Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13501145/","licenseUrl":""}},{"id":"studio-library:PMC13501145-s2","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Global and regional progress towards reduced burden of oral conditions between 2019 and 2023: a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Results For each condition, we first present global estimates of prevalent or incident cases, along with variations across super-regions. We then report global age-standardised prevalence or incidence, again highlighting differences across super-regions. Between 2019 and 2023, global cases of main oral conditions increased by 3·07% (95% UI 2·32–3·75), from 3·62 billion (3·35–3·95) to 3·73 billion (3·45–4·06; table 2 ). Cases increased across almost all super-regions, ranging from 1·68% (0·79–2·42) in southeast Asia, east Asia, and Oceania to 9·51% (8·09–10·94) in sub-Saharan Africa, except for central Europe, eastern Europe, and central Asia ","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13501145-s2","title":"Global and regional progress towards reduced burden of oral conditions between 2019 and 2023: a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00143-x","url":"https://doi.org/10.1016/s2468-2667(26)00143-x","text":"Results For each condition, we first present global estimates of prevalent or incident cases, along with variations across super-regions. We then report global age-standardised prevalence or incidence, again highlighting differences across super-regions. Between 2019 and 2023, global cases of main oral conditions increased by 3·07% (95% UI 2·32–3·75), from 3·62 billion (3·35–3·95) to 3·73 billion (3·45–4·06; table 2 ). Cases increased across almost all super-regions, ranging from 1·68% (0·79–2·42) in southeast Asia, east Asia, and Oceania to 9·51% (8·09–10·94) in sub-Saharan Africa, except for central Europe, eastern Europe, and central Asia and Latin America and the Caribbean, where no meaningful change was observed ( figure 1 ; appendix table S1 ). Table 2 Global prevalent or incident cases in 2019 and 2023 and percentage changes in global cases over 2019–23 for oral conditions Prevalent or incident cases in 2019, millions (95% UI) Prevalent or incident cases in 2023, millions (95% UI) Percentage change in cases (95% UI) Main oral conditions 3620 (3350 to 3950) 3730 (3450 to 4060) 3·07% (2·32 to 3·75) Untreated caries in deciduous teeth 533 (444 to 631) 509 (428 to 590) −4·56% (−8·51 to −0·43) Untreated caries in permanent teeth 2200 (1920 to 2560) 2260 (1980 to 2630) 2·78% (2·19 to 3·41) Severe periodontitis 1030 (879 to 1190) 1090 (915 to 1260) 5·73% (4·57 to 6·91) Edentulism 336 (289 to 397) 373 (324 to 440) 11·09% (9·64 to 12·93) Other oral disorders 148 (142 to 154) 154 (149 to 160) 4·56% (4·42 to 4·69) Lip and oral cavity cancer 0·363 (0·315 to 0·422) 0·430 (0·367 to 0·505) 18·4% (9·76 to 26·9) Orofacial clefts 4·05 (3·30 to 4·94) 4·26 (3·46 to 5·20) 5·14% (3·63 to 6·78) Main oral conditions include untreated caries, severe periodontitis, edentulism, and other oral disorders only. Prevalent cases of main oral conditions do not match the sum of prevalent cases of individual conditions because some conditions coexist within the same individuals. Data are shown as age-standardised prevalence for all oral conditions, except for lip and oral cavity cancer, which is shown as age-standardised incidence. Cases are presented to three significant figures and perce","kind":"开放全文","locator":"Results（节选）","license":"https://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 Oral Disorders Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13501145/","licenseUrl":""}},{"id":"studio-library:PMC13501145-s3","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Global and regional progress towards reduced burden of oral conditions between 2019 and 2023: a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Discussion Coinciding with the first planned assessment of GOHAP targets, this analysis shows a modest improvement in the combined prevalence of main oral conditions that masks a still-expanding global burden. Against the GOHAP target of a 10% reduction by 2030, the combined age-standardised prevalence of main oral conditions fell by –1·55% between 2019 and 2023, yet cases rose by 3·07% to 3·73 billion, signalling that population growth and ageing are outpacing current prevention efforts and oral health-care service capacity. 18 Although most complementary targets are off track, it is important to recognise that they are not equally responsiv","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13501145-s3","title":"Global and regional progress towards reduced burden of oral conditions between 2019 and 2023: a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00143-x","url":"https://doi.org/10.1016/s2468-2667(26)00143-x","text":"Discussion Coinciding with the first planned assessment of GOHAP targets, this analysis shows a modest improvement in the combined prevalence of main oral conditions that masks a still-expanding global burden. Against the GOHAP target of a 10% reduction by 2030, the combined age-standardised prevalence of main oral conditions fell by –1·55% between 2019 and 2023, yet cases rose by 3·07% to 3·73 billion, signalling that population growth and ageing are outpacing current prevention efforts and oral health-care service capacity. 18 Although most complementary targets are off track, it is important to recognise that they are not equally responsive to short-term policy change or health-system reorganisation. Thus, expectations for measurable progress should differ accordingly. Only untreated caries in permanent teeth showed a measurable decline (−1·99%), while lip and oral cavity cancers (8·35%) and orofacial clefts (2·25%) showed noticeable increases. This pattern is particularly salient given that, although they are less prevalent than the main oral conditions, lip and oral cavity cancer and orofacial clefts are the only oral conditions in GBD directly associated with mortality. Although these changes might reflect true epidemiological shifts, they could also be the result of improved surveillance, registry coverage, or reporting. This is particularly important for cancers and congenital anomalies, for which ascertainment sensitivity varies across regions. Progress towards the GOHAP targets was found to be highly uneven across regions, underscoring the need for region-specific policy responses rather than one-size-fits-all approaches. South Asia had the largest decline in the overarching target (−3·34%), a pace that need to be sustained to meet the 2030 targets, alongside reductions in several complementary indicators (ie, untreated caries in deciduous and permanent teeth, severe periodontitis, and edentulism). However, the incidence of lip and oral cavity cancer in south Asia increased markedly (14·33%). The GBD cause list includes heterogeneous oral conditions with distinct aetiologies and management needs, complicating the identification of shared intervention t","kind":"开放全文","locator":"Discussion（节选）","license":"https://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 Oral Disorders Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13501145/","licenseUrl":""}},{"id":"studio-library:PMC13501145-s4","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Global and regional progress towards reduced burden of oral conditions between 2019 and 2023: a systematic analysis for the Global Burden of Disease Study 2023.","summary":"Declaration of interests S Bhaskar reports grants or contracts from the Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for Scientific Research (KAKENHI), Grant ID 23KF0126; and JSPS and the Australian Academy of Science, JSPS International Fellowship, Grant ID P23712 ; payment or honoraria from the Japan Stroke Society, Japan, for an invited keynote presentation at the 50th Annual Meeting of the Japan Stroke Society, Osaka; and Semey Medical University, Kazakhstan, for an invited professorship and teaching course in neurocardiovascular therapy; support for attending meetings or travel, or both, from the Japan Stroke Society, ","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13501145-s4","title":"Global and regional progress towards reduced burden of oral conditions between 2019 and 2023: a systematic analysis for the Global Burden of Disease Study 2023.","journal":"The Lancet. Public health","year":"2026","doi":"10.1016/s2468-2667(26)00143-x","url":"https://doi.org/10.1016/s2468-2667(26)00143-x","text":"Declaration of interests S Bhaskar reports grants or contracts from the Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for Scientific Research (KAKENHI), Grant ID 23KF0126; and JSPS and the Australian Academy of Science, JSPS International Fellowship, Grant ID P23712 ; payment or honoraria from the Japan Stroke Society, Japan, for an invited keynote presentation at the 50th Annual Meeting of the Japan Stroke Society, Osaka; and Semey Medical University, Kazakhstan, for an invited professorship and teaching course in neurocardiovascular therapy; support for attending meetings or travel, or both, from the Japan Stroke Society, Japan; travel award for invited keynote presentation at the 50th Annual Meeting of the Japan Stroke Society, Osaka, and Asialink, The University of Melbourne; travel support for opening invited keynote at the 31st Asialink Leaders Foundation Week Program 2026, Melbourne; leadership or fiduciary role in Rotary District 9675, Sydney, Australia (District Chair, Diversity, Equity, Inclusion & Belonging); Global Health & Migration Hub Community, Global Health Hub Germany, Berlin, Germany (Chair, Founding Member and Manager); PLOS One , BMC Neurology , Frontiers in Neurology , Frontiers in Stroke , Frontiers in Public Health , Journal of Aging Research , Neurology International , Diagnostics , and BMC Medical Research Methodology (Editorial Board Member); College of Reviewers, Canadian Institutes of Health Research (CIHR), Government of Canada (Member); World Headache Society, Bengaluru, India (Director of Research); Cariplo Foundation, Milan, Italy (Expert Adviser/Reviewer); National Cerebral and Cardiovascular Center, Department of Neurology, Division of Cerebrovascular Medicine and Neurology, Suita, Osaka, Japan (Visiting Director); Cardiff University Biobank, Cardiff, UK (Member, Scientific Review Committee); Rotary Reconciliation Action Plan (Chair); and Japan Connect, Osaka, Japan (Healthcare and Medical Adviser) outside the submitted work. B Goulart reports stock or stock options in Badger Meter Inc, British American Tobacco, and Altria Group (payments made to B Goulart) outside the submitted work. I Ilic reports support fo","kind":"开放全文","locator":"Declaration of interests（节选）","license":"https://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).","verified":"2026-10-23","authors":"GBD 2023 Oral Disorders Collaborators.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13501145/","licenseUrl":""}},{"id":"studio-library:PMC13633534","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Support for Human and Civil Rights in Physical Disability, Mental Illness, and Substance Use Disorder.","summary":"Importance While there is much research on stigma toward marginalized populations, public perceptions regarding deservingness of basic human and civil rights of these groups have not been examined in a nationwide sample of US adults. Objective To understand the US public's perceptions regarding deservingness of basic human and contested civil rights for people with severe physical disabilities (SPDs), severe mental illness (SMI), and substance use disorders (SUDs). Design, setting, and participants This cross-sectional study was a nationwide US internet-based quota panel survey of US adults (aged ≥18 years) who completed the survey. Responses","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13633534","title":"Support for Human and Civil Rights in Physical Disability, Mental Illness, and Substance Use Disorder.","journal":"JAMA health forum","year":"2026","doi":"10.1001/jamahealthforum.2026.3431","url":"https://doi.org/10.1001/jamahealthforum.2026.3431","text":"Importance While there is much research on stigma toward marginalized populations, public perceptions regarding deservingness of basic human and civil rights of these groups have not been examined in a nationwide sample of US adults. Objective To understand the US public's perceptions regarding deservingness of basic human and contested civil rights for people with severe physical disabilities (SPDs), severe mental illness (SMI), and substance use disorders (SUDs). Design, setting, and participants This cross-sectional study was a nationwide US internet-based quota panel survey of US adults (aged ≥18 years) who completed the survey. Responses were collected from January 17 to February 12, 2025. Main outcomes and measures Participants rated their support for 5 human rights (adequate standard of living, high standards of health, legal capacity, freedom from torture, living independently) and 3 civil rights (being a parent, running for political office, owning a gun) for people with SPDs, SMI, and SUDs using a 5-point Likert scale, with marginalized subgroups presented to participants in randomized order. McNemar test for paired binary data was used to examine differences in mean support across subgroups, and linear probability regression models were used to identify demographic variables associated with support. Results In this survey of 1442 US adult participants (849 female [58.9%]; 588 male [40.8%]; 3 nonbinary [0.2%]; and 2 preferred not to say [0.1%]), the SPD subgroup received significantly higher support across all 5 human rights and 3 civil rights compared with SMI and SUD subgroups. For example, 79.0% (95% CI, 76.9%-81.2%) of participants supported the right to live independently for people with SPDs, while 67.0% supported this right for those with SMI (95% CI, 64.6%-69.5%) and 68.4% supported this right for those with SUDs (95% CI, 66.0%-70.9%). Civil rights received lower support across all 3 subgroups; for example, 40.1% (95% CI, 37.5%-42.6%) supported the right to run for political office for those with SMI and 41.4% (95% CI, 38.8%-44.0%) for those with SUDs compared with 62.7% (95% CI, 60.1%-65.2%) for those with SPDs. Democratic affiliation and lived experience with behavioral health care were associated with higher support for human rights (eg, among those with SUDs, up to 17.1 and 14.2 percentage points higher support, respectively). Male sex and younger age (18-24 years) were associated with greater support for civil rights (eg, male sex with 17.9 percentage points higher support for gun ownership among those with SPDs and age 18-24 years with a 27.1 percentage-point difference among those with SMI). Conclusions and relevance These findings contribute important evidence to the existing literature on stigma, highlighting discrepancies in public support for basic human and contested civil rights across marginalized subgroups, which can inform interventions to address public attitudes toward people with behavioral health conditions.","kind":"期刊文献","locator":"摘要","license":"This is an open access article distributed under the terms of the CC-BY-NC-ND License, which does not permit alteration or commercial use, including those for text and data mining, AI training, and similar technologies.","verified":"2026-10-23","authors":"Williams J, Kouveliotes M, Jones N, Shields MC.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13633534/","licenseUrl":"","openTextChunks":0}},{"id":"studio-library:PMC13633533","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Essential Retail Pharmacies Under the Consolidated Appropriations Act of 2026.","summary":"","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13633533","title":"Essential Retail Pharmacies Under the Consolidated Appropriations Act of 2026.","journal":"JAMA health forum","year":"2026","doi":"10.1001/jamahealthforum.2026.3527","url":"https://doi.org/10.1001/jamahealthforum.2026.3527","text":"","kind":"期刊文献","locator":"摘要","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article distributed under the terms of the CC-BY-NC-ND License, which does not permit alteration or commercial use, including those for text and data mining, AI training, and similar technologies.","verified":"2026-10-23","authors":"Gravlee E, Li QJ, Rozelle JW, Ondanat Veetil SK, Qato DM.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13633533/","licenseUrl":"","openTextChunks":0}},{"id":"studio-library:PMC13633535","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Patient Navigation and Postpartum Health Care Outcomes: A Randomized Clinical Trial.","summary":"Importance Postpartum care in the US is fragmented and inadequate, with major disparities in quality. Patient navigation is a promising health services intervention to improve health care provision and reduce disparities. Objective To evaluate whether postpartum patient navigation improved postpartum health care quality among people with low incomes. Design, setting, and participants This randomized clinical trial was conducted at a single urban academic medical center from January 2020 to July 2024 and included English- or Spanish-speaking pregnant people 16 years or older with Medicaid insurance. Data analysis started in October 2024. Inter","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13633535","title":"Patient Navigation and Postpartum Health Care Outcomes: A Randomized Clinical Trial.","journal":"JAMA health forum","year":"2026","doi":"10.1001/jamahealthforum.2026.3338","url":"https://doi.org/10.1001/jamahealthforum.2026.3338","text":"Importance Postpartum care in the US is fragmented and inadequate, with major disparities in quality. Patient navigation is a promising health services intervention to improve health care provision and reduce disparities. Objective To evaluate whether postpartum patient navigation improved postpartum health care quality among people with low incomes. Design, setting, and participants This randomized clinical trial was conducted at a single urban academic medical center from January 2020 to July 2024 and included English- or Spanish-speaking pregnant people 16 years or older with Medicaid insurance. Data analysis started in October 2024. Intervention Participants were randomly assigned to 1 year of patient navigation vs usual care. Those randomized to navigation received individualized services that were designed to overcome postpartum-specific barriers to care from a trained lay navigator. Main outcomes and measures The primary outcome, as ascertained from medical records, was receipt of 6 care elements considered to be essential to optimal postpartum care by 12 weeks postpartum. Secondary outcomes included the proportion of components received by 12 weeks and receipt of health services at 11 to 13 months. Results A total of 405 people (mean [SD] age, 28.2 [5.7] years; 10 [2%] were Asian, multiracial, or another race; 166 [41%] were Hispanic; 202 [50%] were non-Hispanic Black; and 27 [7%] were non-Hispanic White) were randomized (203 [50%] in patient navigation; 202 [50%] received usual care). The frequency of the primary outcome (ie, receipt of all 6 essential components of postpartum care) was similar between the groups (19 [9.4%] in patient navigation vs 16 [7.9%] in usual care; P = .60). However, the mean (SD) proportion of components received was significantly higher among navigation recipients (71% [17%] vs 64% [23%]; P = .002). Three components drove this difference: postpartum visit completion (195 [96%] vs 161 [80%]; P Conclusions The results of this randomized clinical trial suggest that although patient navigation did not result in a higher frequency of achieving all 6 components of postpartum care, the proportion of care components received was significantly higher. At 1 year postpartum, those randomized to patient navigation demonstrated multiple improved health services outcomes, including more frequent transition to primary care. Trial registration ClinicalTrials.gov Identifier: NCT03922334.","kind":"期刊文献","locator":"摘要","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article distributed under the terms of the CC-BY-NC-ND License, which does not permit alteration or commercial use, including those for text and data mining, AI training, and similar technologies.","verified":"2026-10-23","authors":"Yee LM, Feinglass J, Williams BR, Diaz L, Carmona-Barrera V, Cheung Y, Davis K, Yeh C, Niznik CM, Kominiarek MA, Grobman WA.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13633535/","licenseUrl":"","openTextChunks":0}},{"id":"studio-library:PMC13633537","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Financial Effects of an Out-of-Pocket Cap in Traditional Medicare.","summary":"Importance Traditional Medicare (TM) exposes beneficiaries to substantial financial risk because, unlike Medicare Advantage (MA), there is no out-of-pocket (OOP) maximum for medical spending. Policymakers have proposed introducing an OOP cap, but its fiscal and behavioral effects are uncertain. Objective To estimate the financial impacts of introducing OOP spending caps of $3000, $5000, or $10 000 annually in TM on beneficiary costs, enrollment patterns, and Centers for Medicare & Medicaid Services expenditures between 2026 and 2034. Design, setting, and participants This economic evaluation using principles of microsimulation analysis projec","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13633537","title":"Financial Effects of an Out-of-Pocket Cap in Traditional Medicare.","journal":"JAMA health forum","year":"2026","doi":"10.1001/jamahealthforum.2026.3386","url":"https://doi.org/10.1001/jamahealthforum.2026.3386","text":"Importance Traditional Medicare (TM) exposes beneficiaries to substantial financial risk because, unlike Medicare Advantage (MA), there is no out-of-pocket (OOP) maximum for medical spending. Policymakers have proposed introducing an OOP cap, but its fiscal and behavioral effects are uncertain. Objective To estimate the financial impacts of introducing OOP spending caps of $3000, $5000, or $10 000 annually in TM on beneficiary costs, enrollment patterns, and Centers for Medicare & Medicaid Services expenditures between 2026 and 2034. Design, setting, and participants This economic evaluation using principles of microsimulation analysis projected enrollment, spending, and health care use outcomes under baseline (no OOP cap) and 3 alternative cap scenarios using publicly available national Medicare and insurance data from 2020 to 2024 with projections through 2034. The model incorporated elasticity-based behavioral responses, including beneficiary switching between TM and MA, changes in service use, and increases to MA benchmarks and spending. Sensitivity analyses varied model assumptions probabilistically. Exposure Introduction of annual OOP caps for Parts A and B in TM at $3000, $5000, or $10 000. Main outcomes and measures Projected changes in beneficiary enrollment by coverage type, mean annual beneficiary OOP and premium spending, and annual Medicare expenditures. Results Between 2026 and 2034, a $3000 OOP cap was projected to reduce mean annual beneficiary expenditures by $1568, compared with $1138 under a $5000 cap and $707 under a $10 000 cap. Corresponding increases in annual Medicare spending were estimated at $96 billion (90% simulation uncertainty interval [SUI], $80-$114 billion), $70 billion (90% SUI, $55-$85 billion), and $39 billion (90% SUI, $29-$50 billion), respectively. OOP caps, especially at lower thresholds, would substantially shift beneficiaries from MA to TM. Conclusions and relevance Results of this study suggest that implementing an OOP spending cap in TM would provide meaningful financial protection for beneficiaries but impose substantial fiscal costs on Medicare.","kind":"期刊文献","locator":"摘要","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article distributed under the terms of the CC-BY-NC-ND License, which does not permit alteration or commercial use, including those for text and data mining, AI training, and similar technologies.","verified":"2026-10-23","authors":"Ryan AM, Dixit MN, Maughan MM, Meyers DJ, Geng F.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13633537/","licenseUrl":"","openTextChunks":0}},{"id":"studio-library:PMC13630575","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Combined impact of smoking, alcohol and social inequalities on premature mortality in urban India.","summary":"Associations with mortality of social inequalities, smoking and drinking differ widely between populations. Here we recruited 500,810 adults aged ≥35 years from Chennai city during 1998-2001 in South India and followed to 2020 for verbal-autopsy-assessed cause-specific mortality. Among those initially without chronic disease, multivariable-adjusted Cox regression (censored at age 70 years) related initial characteristics to mortality rates. Even among non-smoking non-drinkers, those with no schooling had almost three times the mortality rate ratio (RR) of those with >11 years education (men, RR of 2.76, 95% confidence interval (CI) 2.58-2.95;","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13630575","title":"Combined impact of smoking, alcohol and social inequalities on premature mortality in urban India.","journal":"Nature health","year":"2026","doi":"10.1038/s44360-026-00148-4","url":"https://doi.org/10.1038/s44360-026-00148-4","text":"Associations with mortality of social inequalities, smoking and drinking differ widely between populations. Here we recruited 500,810 adults aged ≥35 years from Chennai city during 1998-2001 in South India and followed to 2020 for verbal-autopsy-assessed cause-specific mortality. Among those initially without chronic disease, multivariable-adjusted Cox regression (censored at age 70 years) related initial characteristics to mortality rates. Even among non-smoking non-drinkers, those with no schooling had almost three times the mortality rate ratio (RR) of those with >11 years education (men, RR of 2.76, 95% confidence interval (CI) 2.58-2.95; women, RR of 2.93, 95% CI 2.69-3.19), with substantial mortality excesses from each major disease category. These social inequalities were further exacerbated by much higher smoking and drinking prevalences among less educated men. Adjusted for education, male smoking (versus not, RR of 1.26, 95% CI 1.22-1.29) and drinking (versus not, RR of 1.52, 95% CI 1.48-1.56) were independently associated with risk and together almost doubled male mortality (both versus neither, RR of 1.89, 95% CI 1.84-1.94). Few women (<0.1%) smoked or drank.","kind":"期刊文献","locator":"摘要","license":"Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise ","verified":"2026-10-23","authors":"Gajalakshmi V, Bragg F, Lacey B, Kanimozhi V, Chandrasekaran M, Kumar RA, Sherliker P, Peto R, Lewington S.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13630575/","licenseUrl":"","openTextChunks":0}},{"id":"studio-library:PMC13246439","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Role of community and sexual contacts as drivers of MPXV clade I.","summary":"Initial investigation into the emerging mpox outbreak caused by novel monkeypox virus (MPXV) clade Ib in the eastern Democratic Republic of the Congo has identified signs of sustained human-to-human transmission and epidemiological links to sexual contacts involving female sex workers (FSWs), which have not been observed in previous clade Ia outbreaks. Using mathematical models incorporating age-dependent contact patterns, we quantified the role of frequent sexual interactions as opposed to community contacts in clade Ib's dynamics and found that this additional mode of transmission could explain its increased outbreak potential compared with","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13246439","title":"Role of community and sexual contacts as drivers of MPXV clade I.","journal":"Nature health","year":"2026","doi":"10.1038/s44360-026-00080-7","url":"https://doi.org/10.1038/s44360-026-00080-7","text":"Initial investigation into the emerging mpox outbreak caused by novel monkeypox virus (MPXV) clade Ib in the eastern Democratic Republic of the Congo has identified signs of sustained human-to-human transmission and epidemiological links to sexual contacts involving female sex workers (FSWs), which have not been observed in previous clade Ia outbreaks. Using mathematical models incorporating age-dependent contact patterns, we quantified the role of frequent sexual interactions as opposed to community contacts in clade Ib's dynamics and found that this additional mode of transmission could explain its increased outbreak potential compared with clade Ia. As with the globally circulating clade IIb, which is transmitted predominantly among men who have sex with men, our findings reinforce the importance of protecting key population groups-specifically FSWs for clade Ib-in controlling ongoing mpox outbreaks.","kind":"期刊文献","locator":"摘要","license":"Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise ","verified":"2026-10-23","authors":"Murayama H, Asakura TR, Dickens BL, Boyle D, Foo JH, Jin S, Mukadi PK, Ejima K, Jung SM, Nishi A, Prem K, Wakamba AM, Saila-Ngita D, Niyukuri D, Endo A.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13246439/","licenseUrl":"","openTextChunks":0}},{"id":"studio-library:PMC13156043","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Mapping pesticide mixtures to cancer risk at the country scale with spatial exposomics.","summary":"Despite decades of concern over the carcinogenic potential of agricultural pesticides, toxicological studies relying on single endpoints have yet to establish a definitive link between environmental pesticide exposure and cancer in real-world contexts. Here we use an integrative spatial Bayesian framework that merges high-resolution environmental pesticide risk modelling with comprehensive cancer registry data to map pesticide-linked cancer clusters in Peru with unprecedented precision. Our process-based model, encompassing 31 key pesticide active ingredients, together with an innovative stratification of cancer cases by developmental lineage","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13156043","title":"Mapping pesticide mixtures to cancer risk at the country scale with spatial exposomics.","journal":"Nature health","year":"2026","doi":"10.1038/s44360-026-00087-0","url":"https://doi.org/10.1038/s44360-026-00087-0","text":"Despite decades of concern over the carcinogenic potential of agricultural pesticides, toxicological studies relying on single endpoints have yet to establish a definitive link between environmental pesticide exposure and cancer in real-world contexts. Here we use an integrative spatial Bayesian framework that merges high-resolution environmental pesticide risk modelling with comprehensive cancer registry data to map pesticide-linked cancer clusters in Peru with unprecedented precision. Our process-based model, encompassing 31 key pesticide active ingredients, together with an innovative stratification of cancer cases by developmental lineage, reveals a robust spatial association between environmental pesticide exposure risk and cancer incidence. In pesticide-associated cancer hotspots, exposomic profiling of liver tissue-a primary target of chemical carcinogens-uncovers a distinct transcriptomic signature of pesticide exposure, implicating a non-genotoxic mode of action that disrupts core regulatory circuitries sustaining cell identity. Collectively, these findings strongly support a mechanistic link between pesticide exposure and cancer, challenging assumptions of human non-carcinogenicity derived from reductionist experimental models. This study redefines the exposome as a lineage-conditioned, mechanistically tractable framework and shows how complex pesticide mixtures can contribute to carcinogenic trajectories, with profound and far-reaching implications for global health policy and socio-ecological equity.","kind":"期刊文献","locator":"摘要","license":"Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise ","verified":"2026-10-23","authors":"Honles J, Cerapio JP, Monge C, Marchio A, Ruiz E, Fernández R, Casavilca-Zambrano S, Contreras-Mancilla J, Vidaurre T, Condom T, Zerathe S, Dangles O, Deharo É, Herrera-Zuñiga J, Pineau P, Bertani S.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13156043/","licenseUrl":"","openTextChunks":0}},{"id":"studio-library:PMC13433293","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Predicting schistosome transmission in rural Uganda using water contact data from wearable GPS devices.","summary":"Granular spatial data and models of human behaviour are currently lacking for schistosomiasis. We collected ten days of wearable GPS logger data from 452 individuals in rural Uganda to model water contact as a proxy indicator of usage at 69 georeferenced open-water sites and 32 public taps or boreholes. Among participants, 63.9 and 33.2% visited at least one water site and tap or borehole, respectively. Exponential spatial decay models accurately predicted site-specific open-water contact (area under the receiver operating characteristic curve = 0.87) and tap or borehole usage (0.92). There was no evidence of tap or borehole usage influencing","url":"index.html#library","source":"data/studio-library.json","details":{"id":"PMC13433293","title":"Predicting schistosome transmission in rural Uganda using water contact data from wearable GPS devices.","journal":"Nature health","year":"2026","doi":"10.1038/s44360-026-00118-w","url":"https://doi.org/10.1038/s44360-026-00118-w","text":"Granular spatial data and models of human behaviour are currently lacking for schistosomiasis. We collected ten days of wearable GPS logger data from 452 individuals in rural Uganda to model water contact as a proxy indicator of usage at 69 georeferenced open-water sites and 32 public taps or boreholes. Among participants, 63.9 and 33.2% visited at least one water site and tap or borehole, respectively. Exponential spatial decay models accurately predicted site-specific open-water contact (area under the receiver operating characteristic curve = 0.87) and tap or borehole usage (0.92). There was no evidence of tap or borehole usage influencing open-water contact. Incorporating mobility terciles did not improve simple spatial decay models. Integrating spatial decay-based estimates of open-water-site usage into an individual-based transmission model produced realistic estimates of one-year Schistosoma mansoni reinfection and provided a ranking of water sites contributing to transmission. Our spatial decay models offer scalable tools for focal interventions for schistosomiasis.","kind":"期刊文献","locator":"摘要","license":"Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise ","verified":"2026-10-23","authors":"Reitzug F, Iacovidou MA, Kabatereine NB, Nabatte B, Eyre MT, Tinkitina B, Byaruhanga AM, Lambiotte R, Chami GF.","fulltext":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13433293/","licenseUrl":"","openTextChunks":0}},{"id":"studio-library:ZGSHKX2026008006","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"数智时代历史学的新进展","summary":" New Advances in Historiography in the Digital-Intelligence 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建议","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-academic-copyediting-skill","title":"Skill：学术写作规范润色（中文学术文本）","journal":"方法 Skill","year":"","url":"skills/academic-copyediting-skill.md","text":"# Skill：学术写作规范润色（中文学术文本）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT\n\n## 用途\n\n对中文学术文本做**写作质量层面**的规范与润色：标点、句式、冗余、结构与术语。适用于人类写作，也适用于 AI 辅助写作的质量提升。可直接作为提示词（Prompt）喂给任何大语言模型使用。\n\n## 适用范围（明确边界）\n\n允许：\n- 全半角标点规范化（中文语境下统一为全角标点，数字、英文、网址、代码除外）\n- 破折号 / 连字符统一（`--`、`—`、`–` 按中文规范处理）\n- 去冗余表达（「进行了」「通过……的方式」「值得注意的是」等套话）\n- 长句拆分（单句超过约 60 字时提示拆分）\n- 句首重复与句式单一化调整\n- 段落结构与过渡、术语一致性\n- 「学术腔」压缩——以清晰、准确为导向\n\n**不允许：**\n- 针对任何 AI 检测器做优化（即不以“降低 AI 检测分数”为目标）\n- 承诺或尝试让 AI 生成的内容“通过编辑/审稿审查”\n- 把 AI 生成内容伪装为人类原创\n- 去除、篡改来源信息或元数据（如 C2PA 标签）\n- 在报告或稿件中隐瞒 AI 使用情况\n\n## 工作流程\n\n1. **诊断**：通读全文，按下方清单逐项检查并记录问题位置。\n2. **规范化**：逐项修改，保持原意不变，不做事实层面的增删（需要增删事实时留待作者本人处理）。\n3. **输出**：给出「修改后全文 + 修改说明 + 建议作者人工复核的事项」。\n\n## 诊断清单\n\n- [ ] 全半角标点混用（如 `,` 应为 `，`；中文里的半角括号、冒号、问号）\n- [ ] 全角字母 / 数字混入（应转半角，如 `ＡＢＣ` → `ABC`、`２０２６` → `2026`）\n- [ ] 破折号不规范（`--`、单个 `—`、`–` 混用）\n- [ ] 冗余套话：进行了、通过……的方式、在……的情况下、值得注意的是、需要指出的是、综上所述、总而言之等\n- [ ] 长句（超过约 60 字的单句，提示拆分）\n- [ ] 句首重复（相邻句以相同词语开头）\n- [ ] 术语前后不一致\n- [ ] 段落过长或主题混杂\n\n## 输出格式\n\n```\n【修改后全文】\n……\n\n【修改说明】\n1. 第 X 段：……（原因：……）\n2. ……\n\n【请作者人工复核】\n- 事实与数据（润色不改事实，需作者核对）\n- 引用出处\n- AI 使用披露是否已写入稿件\n```\n\n## 使用提示\n\n本 Skill 是写作质量工具。它不保证、也不尝试让文本“看起来不像 AI 写的”。如果你的稿件使用了 AI 辅助，请在稿件中如实披露（可配合普贤的披露声明生成器使用）。\n\n---\n\n## 引用 / Citation\n\n中文：杜思娴. 学术写作规范润色 Skill [EB/OL]. (2026). https://naturedu.github.io/socialmedicine/.\n\nEnglish: Du, S. (2026). *Academic writing copyediting skill* [Skill definition]. https://naturedu.github.io/socialmedicine/.\n","kind":"方法 Skill","locator":"方法手册","license":"MIT（仓库原有技能）","verified":"原有公开仓库"}},{"id":"studio-library:skill-active-health-hiap-skill","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Skill：主动健康与健康融入万策（Health in All Policies，总纲旗舰）","summary":"# Skill：主动健康与健康融入万策（Health in All Policies，总纲旗舰）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n这是「普贤——社医卫管科研助手」方法学套件的**总纲旗舰 Skill**，承载整套工具的理念与整合逻辑：\n\n> **就是为了每一个人的健康。**\n\n以「主动健康」为策略主轴，以「健康融入所有政策（Health in All Policies, HiAP）」为治理框架，把本网站的理论方法、数据源、选题与院校四个板块蒸馏成一个可用于**学科建设、课题设计、政策评估与教学**的工作流。既有普度众生式的普惠愿景（健康不止于治病，而是让政策、环境与日常都向健康倾斜），又把愿景落到可算、可评、可复现的方法与代码上。每个环节都给出「方法 + 代码」，Stata / R 双轨。\n\n## 适用范围（明确边界）\n\n允许：\n- 以 HiAP 框架组织跨部门健康政策研究与健康影响评估（HIA）\n- 以主动健康视角设计行为与体系层面的干预与评估方案\n- 用定量方法（归因负担、公平性、成本效果）为「健康融入万策」提供证据\n- 指导选题、基金申报与学科方向的凝练\n\n**不允许：**\n- 把健康价值凌驾于其他正当政策目标之上做单边裁断（HiAP 是权衡与协同，不是否决权）\n- 以模型结果冒充已发生的健康结局；归因是模型估计，需显式说明\n- 用愿景口号替代证据；本","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-active-health-hiap-skill","title":"Skill：主动健康与健康融入万策（Health in All Policies，总纲旗舰）","journal":"方法 Skill","year":"","url":"skills/active-health-hiap-skill.md","text":"# Skill：主动健康与健康融入万策（Health in All Policies，总纲旗舰）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n这是「普贤——社医卫管科研助手」方法学套件的**总纲旗舰 Skill**，承载整套工具的理念与整合逻辑：\n\n> **就是为了每一个人的健康。**\n\n以「主动健康」为策略主轴，以「健康融入所有政策（Health in All Policies, HiAP）」为治理框架，把本网站的理论方法、数据源、选题与院校四个板块蒸馏成一个可用于**学科建设、课题设计、政策评估与教学**的工作流。既有普度众生式的普惠愿景（健康不止于治病，而是让政策、环境与日常都向健康倾斜），又把愿景落到可算、可评、可复现的方法与代码上。每个环节都给出「方法 + 代码」，Stata / R 双轨。\n\n## 适用范围（明确边界）\n\n允许：\n- 以 HiAP 框架组织跨部门健康政策研究与健康影响评估（HIA）\n- 以主动健康视角设计行为与体系层面的干预与评估方案\n- 用定量方法（归因负担、公平性、成本效果）为「健康融入万策」提供证据\n- 指导选题、基金申报与学科方向的凝练\n\n**不允许：**\n- 把健康价值凌驾于其他正当政策目标之上做单边裁断（HiAP 是权衡与协同，不是否决权）\n- 以模型结果冒充已发生的健康结局；归因是模型估计，需显式说明\n- 用愿景口号替代证据；本 Skill 要求每个主张都有方法学支撑\n\n## 理念：从「普度众生」到「健康普惠」\n\n- **普贤愿景的现代转译**：菩萨度人是个体修为；卫生治理的健康普惠是制度修为。普贤的品牌底色由此而来——像海浪一样温柔地渗透每个领域。\n- **主动健康**：健康的第一责任人是个体，但个体选择被环境塑造。主动健康 = 个体主动（运动、营养、心理、睡眠、社交等健康行为）+ 体系主动（环境、政策、服务与支付方式让健康选择成为默认选项）。\n- **健康融入万策（HiAP）**：WHO 赫尔辛基声明（2013）的核心主张——健康与福祉由卫生部门之外的因素决定，因此所有政策都应系统考虑健康后果。八项原则：考虑健康与健康公平、共同受益、多部门参与、利益相关者参与、政策一致性、问责与透明度、基于证据、能力建设。中国的政策表达是「将健康融入所有政策」与「健康中国 2030」战略。\n\n## 工作流程总览（五大板块）\n\n1. 理论框架（主动健康 + HiAP + 健康社会决定因素）→ 2. 健康影响评估 HIA 方法 → 3. 数据源（全球健康数据库）→ 4. 选题与基金 → 5. 院校与学科建设。每个板块「方法 + 代码」。\n\n---\n\n### 板块 1：理论框架（怎么写进论文与标书）\n\n**方法**：\n\n- **立论三件套**：① 健康社会决定因素（WHO CSDH：日常生活条件 + 权力、金钱与资源的分配）说明「为什么健康问题出在卫生部门之外」；② HiAP 说明「政策杠杆在哪里」（教育、住房、交通、环境、社保、医保、食品）；③ 主动健康说明「个体与体系如何共同发力」。\n- **论文中的用法**：理论框架部分按「决定因素 → 政策杠杆 → 主动健康机制」三段展开；讨论部分回扣「该政策是否把健康带进了决策」。\n- **标书中的用法**：立项依据写「健康融入万策」的政策窗口（如《“健康中国 2030”规划纲要》把健康融入所有政策列为基本原则），研究目标写清楚要评估哪一类政策的健康与公平后果。\n\n**代码**（政策文本快速摸底：关键词与主题结构，辅助而非替代人工阅读）：\n\n```r\nlibrary(quanteda)\ndocs <- readtext::readtext(\"policy_texts/*.txt\")          # 政策文件目录\ncorp <- corpus(docs)\ntoks <- tokens(corp, remove_punct = TRUE)\ndfmat <- dfm(toks)\nkw <- c(\"健康\", \"预防\", \"公平\", \"环境\", \"医疗\", \"保障\")\ndfm_select(dfmat, pattern = kw) |> textstat_frequency()    # 政策文本中的健康话语频次\n```\n\n---\n\n### 板块 2：健康影响评估（HIA）方法\n\n**方法**：HIA 六步——① 筛选（是否值得做 HIA）；② 界定（范围、人群、时间、评估者）；③ 评估（基线、影响识别、定量/定性分析）；④ 建议（可执行的政策修改）；⑤ 报告；⑥ 监测与评价。\n\n**定量核心公式**：\n\n- 暴露—反应归因：人群归因分数 PAF = p(RR−1) / [p(RR−1) + 1]，p 为暴露率，RR 为暴露—反应相对危险度（来自系统综述/GBD 比较风险评估）。\n- 归因负担 = 基线疾病负担 × PAF；干预后负担 = 基线负担 × (1 − 干预后暴露变化 × 归因系数)，据此算「可避免的死亡/DALY」。\n- 公平性：集中指数 CI = 2·cov(y, r)/ȳ（r 为按社会经济排序的分数秩），并用分解说明政策收益分布。\n\n**代码**：\n\n```stata\n* Stata：PAF 与可避免负担的情景计算\nlocal p  0.35            // 基线暴露率\nlocal RR 1.45            // 暴露-反应 RR（示例，来自 Meta 分析）\nlocal paf = (`p'*(`RR'-1))/(`p'*(`RR'-1)+1)\ndi \"PAF = \" %5.3f `paf'\nlocal B 12000            // 基线年死亡数（示例）\ndi \"基线归因死亡 = \" %9.0f (`B'*`paf')\n* 干预使暴露率降至 0.25\nlocal p2 0.25\nlocal paf2 = (`p2'*(`RR'-1))/(`p2'*(`RR'-1)+1)\ndi \"干预后可避免死亡 = \" %9.0f (`B'*(`paf'-`paf2'))\n\n* 公平性：集中指数（协方差法）\negen r = rank(y)         // y 为健康结局，按经济状况排序后的秩\nsum y\ngen lhs = 2*y*r/r(mean)\nquietly reg lhs r\ndi \"CI = \" %5.3f _b[r]\n```\n\n```r\n# R：同一计算 + 集中指数 + 情景比较\npaf <- function(p, RR) p*(RR-1)/(p*(RR-1)+1)\nB <- 12000\nbase  <- B * paf(0.35, 1.45)\nafter <- B * paf(0.25, 1.45)\navoid <- base - after\ncat(sprintf(\"基线归因死亡 %.0f；干预后可避免死亡 %.0f\\n\", base, avoid))\n\n# 集中指数\nci <- function(y, x) 2 * cov(y, rank(x)) / mean(y)\nci(health_y, income_x)\n```\n\n---\n\n### 板块 3：数据源（健康融入万策的证据底座）\n\n**方法**：按「问题 → 数据」配表。疾病负担用 GBD（IHME）；暴露与政策用 WHO GHO / 世界银行 HNP / 中国卫生健康统计年鉴；行为与主动健康用 CHARLS / CFPS / 中国居民营养与慢性病状况报告；环境暴露用中国 CDC 公卫数据中心与卫星产品；政策文本用各级政府公开文件。\n\n**代码**（R 抓取与整理公开数据的习惯写法）：\n\n```r\nlibrary(readxl)\ndf <- read_excel(\"GBD_results.xlsx\")     # 从 GBD Results Tool 导出的 CSV/XLSX\nagg <- aggregate(deaths ~ year + location, data = df, sum)\n# 与政策时点对齐：政策前后负担变化（配合 ITS/DID skill 做因果评估）\n```\n\n> 完整数据库导航见网站「数据源」模块（25 个数据源，含链接与口径提示）。\n\n---\n\n### 板块 4：选题与基金（把愿景变成课题）\n\n**方法**：主动健康 × HiAP 的选题生成规则——「政策杠杆 × 健康结局 × 公平维度」三维交叉。示例选题（直接可用）：\n\n1. 医保支付方式改革（DRG/DIP）对预防服务供给与慢病并发症的影响（DID/ITS）\n2. 「体医融合」运动处方纳入医保对慢病患者健康行为的因果效应（RCT/准实验）\n3. 城市更新与 15 分钟生活圈对居民体力活动与肥胖的准实验评估（DID）\n4. 学校供餐营养政策对儿童超重与学业表现的长期影响（队列/自然实验）\n5. 气候政策（碳氮协同治理）的健康协同收益测算（CEA + PAF 归因）\n6. 主动健康干预（五处方：运动/营养/心理/睡眠/社交）的社区整群随机试验\n7. 健康影响评估制度化的国际比较与本土路径（政策文本分析 + 比较案例）\n8. 远程医疗与数字健康工具对基层慢病管理的公平性影响（集中指数 + 分解）\n9. 健康城市指标体系构建与考核的德尔菲研究\n10. 食品标签/含糖饮料政策对健康不平等的异质性影响（fsQCA/分层分析）\n\n**基金申报**：国自然（管理科学部 G04 健康管理与政策；医学部 H30）、国社科（社会学/应用经济/管理学）、教育部人文社科、卫健委委托课题——申报导航见网站「选题基金」模块；方法学技术路线直接引用本套件对应 Skill（DCE/Markov/DID/ITS/Meta 等）。\n\n---\n\n### 板块 5：院校与学科建设\n\n**方法**：学科建设三件事——方向凝练（用本 Skill 五板块做教研室/课题组的方向地图）、人才培养（把 socialmedicineskills 速查手册作为研究生方法学课程大纲）、院际合作（按「院校科普」模块的 12 所院系匹配合作对象与报考方向）。代表学者路径的启示：把本地证据连接到全球数据与政策问题（借鉴 Science 卫生政策样本中作者路径一节）。\n\n> 院校科普（北大公卫、复旦公卫、人大公管、人大人口与健康、浙大公卫、华科医管、武大政管、安医大等 12 所）见网站「院校」模块。\n\n---\n\n## 常见错误清单\n\n- [ ] 把 HiAP 写成「卫生部门否决其他部门」，忽略共同受益与权衡\n- [ ] PAF 计算用错暴露率口径（点暴露率 vs 累积暴露）或 RR 来源不明\n- [ ] 把模型归因结果写成已发生的健康结局\n- [ ] 只算平均效果，不报公平性分布（集中指数/分人群）\n- [ ] 数据源口径混用（GBD 归因 vs 本地监测）\n- [ ] 政策文本分析用词频代替人工阅读与制度分析\n- [ ] 选题只有愿景没有可检验的因果问题\n- [ ] 引用 GBD/WHO 数据未注明版本与年份\n\n## 输出格式（政策健康影响评估报告模板）\n\n```\n【政策与背景】\n政策名称、出台部门、目标与「健康融入万策」的关联点。\n\n【HIA 界定】\n范围、人群（含易受影响人群）、时间范围、评估者与利益相关者。\n\n【基线】\n暴露率、疾病负担、公平性现状（附数据源与版本）。\n\n【影响评估】\n暴露-反应证据（RR 与来源）；PAF 与可避免负担的情景计算；公平性影响（CI 与分解）；不确定性说明。\n\n【建议】\n可执行的政策修改建议（按证据强度分级）。\n\n【监测与评价】\n指标、数据来源与复查时点。\n```\n\n## 使用提示\n\n- 本 Skill 是总纲：理论用「板块 1」、定量用「板块 2」、数据用「板块 3」、选题用「板块 4」、学科建设用「板块 5」；深度方法到对应 Skill（DCE 设计助手、Markov 模型助手、DID/ITS/DDD、Meta 分析、机器学习、卫生经济学教程）。\n- 主动健康干预评估优先考虑整群随机试验与准实验设计；因果估计与模型归因分开表述。\n- 愿景要有边界：健康融入万策是权衡的艺术，不是健康的一票否决。\n\n---\n\n## 引用 / Citation\n\n中文：杜思娴. 主动健康与健康融入万策（HiAP）总纲 Skill [EB/OL]. (2026). https://naturedu.github.io/socialmedicine/.\n\nEnglish: Du, S. (2026). *Proactive health and Health in All Policies (HiAP): A flagship skill* [Skill definition]. https://naturedu.github.io/socialmedicine/.\n","kind":"方法 Skill","locator":"方法手册","license":"MIT（仓库原有技能）","verified":"原有公开仓库"}},{"id":"studio-library:skill-dce-design-assistant-skill","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Skill：DCE 设计助手（离散选择实验设计与分析，Stata / R）","summary":"# Skill：DCE 设计助手（离散选择实验设计与分析，Stata / R）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n一份可复用的「离散选择实验（Discrete Choice Experiment, DCE）」设计—分析工作流：从属性与水平设定、正交 / D-效率实验设计、选择集构造与分块，到 Stata（`clogit` / `mixlogit` / `lclogit2`）与 R（`mlogit` / `gmnl`）的模型估计、支付意愿（WTP）计算与结果报告。**每一步都给出「方法 + 代码」**，可直接作为提示词喂给大语言模型，也可作为操作手册照做。\n\n## 适用范围（明确边界）\n\n允许：\n- 帮助研究者完成 DCE 全流程的设计与分析（健康经济学、环境经济学、交通、市场营销等陈述性偏好研究）\n- 给出正交主效应设计、部分因子设计、D-效率设计的方法与 Stata/R 代码\n- 用条件 logit（clogit）、混合 logit（mixlogit）、潜类别模型（latent class）估计偏好与 WTP\n\n**不允许：**\n- 替研究者做属性与水平的最终专业判断（需领域专家意见与利益相关者访谈支撑）\n- 承诺设计或结果一定可发表；不替代计量经济学教科书与期刊审稿要求\n- 在未检验 IIA、未说明分布假设的情况下武断下结论\n\n## 软件准备\n\n方法：St","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-dce-design-assistant-skill","title":"Skill：DCE 设计助手（离散选择实验设计与分析，Stata / R）","journal":"方法 Skill","year":"","url":"skills/dce-design-assistant-skill.md","text":"# Skill：DCE 设计助手（离散选择实验设计与分析，Stata / R）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n一份可复用的「离散选择实验（Discrete Choice Experiment, DCE）」设计—分析工作流：从属性与水平设定、正交 / D-效率实验设计、选择集构造与分块，到 Stata（`clogit` / `mixlogit` / `lclogit2`）与 R（`mlogit` / `gmnl`）的模型估计、支付意愿（WTP）计算与结果报告。**每一步都给出「方法 + 代码」**，可直接作为提示词喂给大语言模型，也可作为操作手册照做。\n\n## 适用范围（明确边界）\n\n允许：\n- 帮助研究者完成 DCE 全流程的设计与分析（健康经济学、环境经济学、交通、市场营销等陈述性偏好研究）\n- 给出正交主效应设计、部分因子设计、D-效率设计的方法与 Stata/R 代码\n- 用条件 logit（clogit）、混合 logit（mixlogit）、潜类别模型（latent class）估计偏好与 WTP\n\n**不允许：**\n- 替研究者做属性与水平的最终专业判断（需领域专家意见与利益相关者访谈支撑）\n- 承诺设计或结果一定可发表；不替代计量经济学教科书与期刊审稿要求\n- 在未检验 IIA、未说明分布假设的情况下武断下结论\n\n## 软件准备\n\n方法：Stata 17 及以上、R ≥ 4.2。分析命令 `mixlogit`、`lclogit2`、`dcreate` 来自 SSC（作者 Arne Hole）；R 侧用 `DoE.base`（正交表）、`AlgDesign`（D-效率设计）、`mlogit` / `gmnl`（分析）。\n\n```stata\n* Stata（联网安装一次）\nssc install mixlogit, replace\nssc install lclogit2, replace\nssc install dcreate,  replace\n```\n\n```r\n# R\ninstall.packages(c(\"DoE.base\", \"AlgDesign\", \"support.CEs\", \"mlogit\", \"gmnl\"))\n```\n\n## 工作流程总览（8 步）\n\n1. 确定属性与水平 → 2. 正交 / D-效率实验设计 → 3. 设计质量检查 → 4. 选择集构造与分块 → 5. 问卷与预测试 → 6. 样本量与 long 数据整理 → 7. clogit / mixlogit / 潜类别估计 → 8. 解读与报告。以下每一步均为「方法 + 代码」。\n\n---\n\n### 第 1 步：确定属性与水平\n\n**方法**：文献综述 + 定性访谈（专家、患者/消费者、决策者）确定候选属性；一般 4–7 个属性、每个 2–4 个水平。水平需可实现、可理解、覆盖现实政策范围；「价格 / 费用」属性建议连续或至少 3 个水平，便于计算 WTP。全因子规模 = 各属性水平数相乘，用于判断是否需要缩减设计。\n\n**代码**：\n\n```stata\n* Stata：生成 2^4 全因子（16 行），用于观察所有组合\nclear\nset obs 16\ngen a = mod(_n-1, 2)              // 0/1\ngen b = mod(int((_n-1)/2), 2)\ngen c = mod(int((_n-1)/4), 2)\ngen d = mod(int((_n-1)/8), 2)\nlist\n```\n\n```r\n# R：属性水平表与全因子规模\nlevels <- list(费用 = c(10, 30, 50),   # 元/次\n               候诊 = c(1, 3, 7),      # 天\n               连续性 = c(0, 1),       # 0=不同医生 1=同一医生\n               目录 = c(0, 1))         # 0=基础目录 1=扩展目录\nsapply(levels, length)                 # 各属性水平数\nfull <- expand.grid(levels)            # 全因子组合\nnrow(full)                             # 3*3*2*2 = 36\n```\n\n---\n\n### 第 2 步：正交设计与 D-效率设计\n\n**方法**：\n\n- **正交主效应设计（orthogonal main-effects plan）**：在「主效应可估」前提下，把全因子压缩进正交表 Ln(m^k)（如 L8(2^7)、L9(3^4)、L36(2^11×3^12)）。要求：每列各水平出现次数相等（水平平衡），任意两列的水平组合出现次数相等（相关 = 0）。\n- **部分因子设计 2^(k-p)**：用生成元压缩。例：4 个两水平属性取 8 行、生成元 d = a×b×c（模 2），得到分辨率 IV 设计——主效应相互正交，且不与任何两因子交互混杂。\n- **D-效率设计（推荐，可充分利用先验、能排除不可行组合）**：极大化信息矩阵行列式。Stata 用 `dcreate`，R 用 `AlgDesign::optFederov` / `idefix`，商业软件可用 Ngene 或 SAS PROC OPTEX。\n\n**代码**：\n\n```stata\n* Stata：2^(4-1) 分辨率 IV 正交主效应设计（8 行，生成元 d = a*b*c 模 2）\nclear\nset obs 16\ngen a = mod(_n-1, 2)\ngen b = mod(int((_n-1)/2), 2)\ngen c = mod(int((_n-1)/4), 2)\ngen d = mod(int((_n-1)/8), 2)\nkeep if mod(a+b+c+d, 2) == 0\nlist, sep(0)\n\n* Stata：3^4 的 L9 正交设计（9 行）\nclear\nset obs 27\ngen a = mod(_n-1, 3)\ngen b = mod(int((_n-1)/3), 3)\ngen c = mod(int((_n-1)/9), 3)\nkeep if mod(a+b+c, 3) == 0\n\n* Stata：D-效率设计（dcreate，SSC）。示例：3 个属性、每选择集 2 个方案、12 个选择集\nmatrix b0 = J(1, 3, 0)                    // 先验参数（默认 0）\ndcreate i.x1 i.x2 i.x3, nalt(2) nset(12) bmat(b0) seed(2026)\n* 注：dcreate 要求因子变量写法；详细选项以 help dcreate 为准\n```\n\n```r\n# R：正交主效应设计（DoE.base）\nlibrary(DoE.base)\ndes24 <- oa.design(nlevels = c(2, 2, 2, 2))    # 2^4 → 8 行 L8\ndes33 <- oa.design(nlevels = c(3, 3, 3, 3))    # 3^4 → 9 行 L9\nas.data.frame(des24)\n\n# R：D-效率设计（AlgDesign）\nlibrary(AlgDesign)\nfull36 <- expand.grid(x1 = c(0, 1, 2), x2 = c(0, 1, 2), x3 = c(0, 1), x4 = c(0, 1))\ndopt <- optFederov(~ ., data = full36, nTrials = 16, criterion = \"D\")\ndopt$design                                  # 选中的 16 个 profile\ndopt$D                                       # D-效率值\n```\n\n---\n\n### 第 3 步：检查设计质量\n\n**方法**：四项检查——① 正交性：任意两列相关 ≈ 0；② 水平平衡：每列各水平出现次数相等；③ 无主导（dominance）：同一选择集内不存在「在所有属性上都不差于另一方案」的方案；④ 效率：D-效率尽可能高（可与全因子对照）。不通过则换正交表、换生成元或增加行数。\n\n**代码**：\n\n```stata\n* Stata：水平平衡 + 两两相关\ntab1 a b c d\npwcorr a b c d\n```\n\n```r\n# R：水平平衡、相关矩阵与设计效率\napply(des24, 2, table)                       # 水平平衡\nround(cor(as.data.frame(des24)), 3)          # 正交性：非对角应 ≈ 0\neval.design(~ ., dopt$design)                # AlgDesign：方差膨胀、D/G 效率\n```\n\n---\n\n### 第 4 步：构造选择集与分块\n\n**方法**：把设计行**两两随机配对**组成选择集（典型：2 个方案 + 1 个 opt-out「都不选」）；配对时避免重复组合与主导对。每名受访者作答 6–10 个选择集；超过问卷容量则**分块（block）**，块间随机分配给受访者。选择集顺序、方案左右位置均随机化，并生成多个问卷版本。\n\n**代码**：\n\n```r\n# R：随机配对、分块\nlibrary(dplyr)\nset.seed(2026)\nprof  <- as.data.frame(des24) |> sample_frac(1)      # 随机打乱\npairs <- matrix(seq_len(nrow(prof)), ncol = 2, byrow = TRUE)\nnset  <- nrow(pairs)                                  # 选择集数\nnblock <- 2; per <- 2                                 # 2 个块、每块 2 题\nblock  <- rep(1:nblock, each = per, length.out = nset)\nout <- data.frame(choice_set = rep(1:nset, each = 2),\n                  block = rep(block, each = 2),\n                  alt = rep(1:2, times = nset),\n                  prof[as.vector(t(pairs)), ])\n# opt-out 在问卷中额外提供“都不选”；数据里记 alt == 3、各属性 = 0\n```\n\n```stata\n* Stata：为每个选择集追加 opt-out 行（alt == 3，属性全为 0）\nlocal nset 4\nforvalues s = 1/`nset' {\n    set obs `=_N+1'\n    replace choice_set = `s' if _n == _N\n    replace alt = 3 if _n == _N\n    foreach v in a b c d {\n        replace `v' = 0 if _n == _N\n    }\n}\n```\n\n---\n\n### 第 5 步：问卷与预测试\n\n**方法**：预测试分两轮——① think-aloud 认知访谈 5–10 人，检查属性表述是否被正确理解；② 小样本 30–50 份，检查：是否存在「永远选同一方案 / 永远选 opt-out」的受访者、是否有主导选项、任务负担与完成时间是否合理。据结果修正水平、措辞或题数。\n\n**代码**：\n\n```stata\n* Stata：预测试数据体检——受访者选择 opt-out 的次数分布\negen n_optout = total(choice == 1 & alt == 3), by(id)\ntab n_optout\n* 提示：某受访者所有题都选 opt-out，说明属性或任务设计可能有问题\n```\n\n```r\n# R：同一检查\nlibrary(dplyr)\ndat |> group_by(id) |>\n  summarise(n_optout = sum(choice == 1 & alt == 3),\n            n_A      = sum(choice == 1 & alt == 1),\n            n_total  = n_distinct(choice_set))\n```\n\n---\n\n### 第 6 步：样本量与 long 数据整理\n\n**方法**：\n\n- **样本量**（Orme 经验法则）：n ≥ 500 × c / (t × a)，其中 c = 水平数最多的属性的水平数，t = 每受访者选择集数，a = 每选择集方案数（不含 opt-out）。例：c = 3、t = 8、a = 2 → n ≥ 500×3/16 ≈ 94；正式研究按预算与检验力要求上调。\n- **数据格式**：long（长表），每行 = 受访者 × 选择集 × 方案；必需变量 `id`、`choice_set`、`alt`、`choice`（1/0）与各属性取值；opt-out 行属性全为 0。\n- **编码**：连续属性用原始数值；分类属性用 effects coding（-1/0/1）或虚拟变量，全流程保持一致，报告时注明。\n\n**代码**：\n\n```stata\n* Stata：示例 long 数据（1 名受访者、2 个选择集、各 3 个方案）\nclear\ninput id choice_set alt choice a b c d\n1 1 1 1 0 0 1 1\n1 1 2 0 1 1 0 0\n1 1 3 0 0 0 0 0\n1 2 1 0 1 0 1 0\n1 2 2 1 0 1 0 1\n1 2 3 0 0 0 0 0\nend\n\n* 宽表 → 长表（宽表每行一个选择集，选项变量带后缀 _1/_2/_3）\nreshape long choice_ a_ b_ c_ d_, i(id choice_set) j(alt)\nrename (choice_ a_ b_ c_ d_) (choice a b c d)\n\n* effects coding 示例：三水平属性 a（0/1/2 → -1/0/1）\nrecode a (0 = -1) (1 = 0) (2 = 1)\n```\n\n```r\n# R：dfidx 构造选择数据（mlogit 用）\nlibrary(mlogit)\nmd <- dfidx(dat, idx = c(\"id\", \"alt\"), choice = \"choice\")\n```\n\n---\n\n### 第 7 步：模型估计（Stata 与 R 对应）\n\n#### 7.1 条件 logit（`clogit`）\n\n**方法**：随机效用模型 U = β'x + ε，ε 为独立同分布 I 型极值分布，则方案选择概率 P = exp(V)/Σexp(V)。适用前提是 IIA（无关方案独立性）；可用「子集删除」的系数稳定性直觉检验。违反 IIA → 改用 mixlogit 或潜类别模型。同一受访者的多个选择相关，标准误需 `vce(cluster id)`。**WTP = −β_属性 / β_费用**（费用系数为负）。\n\n**代码**：\n\n```stata\nclogit choice a b c d, group(choice_set) vce(cluster id)\nest store clogit\n\n* WTP：d 为费用属性时的属性 b 的 WTP（delta 方法，含 95% CI）\nnlcom wtp_b: -_b[b] / _b[d]\n\n* IIA 直觉检验：删除 opt-out 后系数应大体稳定\nclogit choice a b c d if alt != 3, group(choice_set) vce(cluster id)\n```\n\n```r\n# R：clogit（survival）与 mlogit 等价实现\nlibrary(survival)\nclogit(choice ~ a + b + c + d + strata(choice_set), data = dat)\n\nlibrary(mlogit)\nm1 <- mlogit(choice ~ a + b + c + d | 0, data = md)\nsummary(m1)\n```\n\n#### 7.2 混合 logit（`mixlogit`）\n\n**方法**：允许系数在个体间服从分布（正态 / 对数正态等），捕捉**偏好异质性**；随机系数 SD 显著即存在异质性。费用属性常用对数正态（保持负号），抽样用 Halton 序列 `nrep(500)`。与 clogit 比较 AIC/BIC（`fitstat`）判断是否需要异质性设定。\n\n**代码**：\n\n```stata\n* 随机系数：a、b、c 正态，d 固定\nmixlogit choice a b c d, group(choice_set) id(id) rand(a b c) nrep(500)\nest store mixl\nfitstat                                     // AIC/BIC，与 clogit 比较\n\n* 对数正态费用 + 相关随机参数（ln(1) = rand() 中第 1 个随机参数取对数正态）\nmixlogit choice a b c d, group(choice_set) id(id) rand(a b c) ln(1) corr nrep(500)\n\n* WTP：mixlwtp（SSC）或 delta 方法；具体语法以 help mixlwtp 为准\n```\n\n```r\n# R：混合 logit（mlogit rpar）\nm2 <- mlogit(choice ~ a + b + c + d | 0, data = md,\n             rpar = c(a = \"n\", b = \"n\", c = \"n\"),\n             R = 500, halton = NA, panel = TRUE)\nsummary(m2)\n```\n\n#### 7.3 潜类别 logit（latent class，`lclogit2`）\n\n**方法**：假设存在 K 个不可观测的偏好类别，各类别内系数不同（类别内同质、类别间异质）。**类别数 K 用 BIC/AIC 选择**（`lclogit2` 默认输出）。类成员平均概率用 `lclogitprob`；刻画「谁属于哪类」可用类成员协变量模型 `lclogitml`（成员方程放入年龄、收入等），或事后与个体特征交叉描述。\n\n**代码**：\n\n```stata\nlclogit2 choice a b c d, group(choice_set) id(id) nclasses(2) seed(2026)\nlclogit2, nclasses(3) seed(2026)            // 比较 BIC/AIC 选类别数\nest store lc3\nlclogitprob, cpprob(3)                      // 各类别平均成员概率\nestat lcmean, post                          // 各类别协变量均值（刻画类别特征）\n\n* 类成员协变量模型（membership 方程放入 age、income）\nlclogitml choice a b c d, group(choice_set) id(id) nclasses(3) membership(age income)\n\n* WTP：lcwtp（lclogit2 附带），语法以 help lcwtp 为准；\n* 或手工取各类别系数比值并配合 delta 方法\n```\n\n```r\n# R：潜类别 logit（gmnl）\nlibrary(gmnl)\nlc <- gmnl(choice ~ a + b + c + d | 0 | 0 | 0 | 1, data = md,\n           model = \"lc\", panel = TRUE, Q = 3)\nsummary(lc)\n```\n\n---\n\n### 第 8 步：结果解读与报告\n\n**方法**：\n\n- **主表**：clogit / mixlogit / 潜类别三模型并列，报系数（SE）；mixlogit 报均值与 SD；潜类别报各类别系数与成员概率。\n- **WTP 表**：WTP = −β_x / β_cost，95% CI 用 delta 方法（`nlcom`）或 Krinsky-Robb（`mixlwtp` 等），报告所用方法。\n- **文字结论**：系数方向与显著性 → 政策含义；SD 显著或类别差异 → 细分人群偏好。\n- **报告规范**：建议对照 ISPOR conjoint checklist：属性与水平依据、设计类型与效率、预测试、估计方法与分布假设、WTP 区间。\n\n**代码**：\n\n```stata\n* WTP 与置信区间（clogit，delta 方法）\nnlcom wtp_b: -_b[b] / _b[d], post\nestimates store wtp_b\n\n* 三模型并列输出\nestimates table clogit mixl lc3, b(%9.3f) se stats(N aic bic)\n```\n\n```r\n# R：delta 方法 WTP（car::deltaMethod）\nlibrary(car)\ndeltaMethod(coef(m1), \"-b/d\")               # 按实际系数名替换 b、d\n```\n\n---\n\n## 常见错误清单\n\n- [ ] 用 `mlogit` 当 `clogit` 用（未指定 `group()` / `strata()`）→ 系数有偏\n- [ ] 忘记 `vce(cluster id)` → 标准误低估\n- [ ] opt-out 编码不一致（有的 0、有的缺失）\n- [ ] effects coding 与虚拟变量混用、编码前后不一致\n- [ ] 不检验 IIA 就下结论\n- [ ] 把分类水平的 1/2/3 直接当连续变量解释\n- [ ] 未报告设计类型、效率与预测试信息\n- [ ] mixlogit 随机分布选择无依据（如费用却用对称正态）\n\n## 输出格式（分析报告模板）\n\n```\n【设计与数据】\n属性/水平表；设计类型（正交 / D-效率）与效率值；选择集与块数；预测试结果；样本量与法则依据。\n\n【模型估计】\n表 1：clogit 主结果；表 2：mixlogit 均值 + SD；表 3：潜类别各类别系数与成员概率（附 AIC/BIC 选 K 的依据）。\n\n【WTP】\nWTP = -β_x / β_cost 及 95% CI（注明 delta / Krinsky-Robb 方法）。\n\n【结论与局限】\n方向性结论 + 偏好异质性 + 局限（IIA、分布假设、设计假设、外部效度）。\n```\n\n## 使用提示\n\n- 命令与选项随版本更新，运行 `help clogit` / `help mixlogit` / `help lclogit2` / `help dcreate` 核对。\n- 设计阶段建议流程：先全因子/正交表做基线，再用 `dcreate` 或 `AlgDesign` 做 D-效率设计，比较 D 值后定稿。\n- 本 Skill 提供方法与代码模板；研究背景、属性取舍与最终结论由研究者负责。\n\n---\n\n## 引用 / Citation\n\n中文：杜思娴. DCE 设计助手：离散选择实验设计与 Stata/R 分析 Skill [EB/OL]. (2026). https://naturedu.github.io/socialmedicine/.\n\nEnglish: Du, S. (2026). *DCE Design Assistant: Discrete choice experiment design and Stata/R analysis skill* [Skill definition]. https://naturedu.github.io/socialmedicine/.\n","kind":"方法 Skill","locator":"方法手册","license":"MIT（仓库原有技能）","verified":"原有公开仓库"}},{"id":"studio-library:skill-ddd-skill","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Skill：三重差分助手（DDD 政策效果评估，Stata / R）","summary":"# Skill：三重差分助手（DDD 政策效果评估，Stata / R）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n一份可复用的「三重差分（Difference-in-Difference-in-Differences, DDD）」政策效果评估工作流：当 DID 的平行趋势在某一维度不成立时，引入第三个维度做双重剔除，用三重交互项识别政策净效应。贯穿示例：「国家药品集中带量采购对公立医院（vs 民营医院）集采品种（vs 非集采品种）药品费用的影响」，三维 = 时间 × 医院类型 × 品种类型。**每一步都给出「方法 + 代码」**（Stata `reg` / `reghdfe` / `permute` 与 R `fixest::feols`），并内置一套**自洽的模拟面板数据**（60 家医院 × 2 类药品 × 2 期，真实 DDD 效应 δ = −8 万元/月）贯穿全流程，可直接作为提示词喂给大语言模型，也可作为操作手册照做。\n\n## 适用范围（明确边界）\n\n允许：\n- 帮助卫生政策评估研究者完成 DDD 的设定、估计、分解验证、稳健性检验与报告（社会医学、卫生经济学、药物经济学等）\n- 用 Stata / R 生成模拟面板数据、估计三重交互模型、做两个 2×2 DID 的分解表与安慰剂检验\n- 给出 DDD 识别假设（偏误同质性）、第三维度选择标准与事件研究式动","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-ddd-skill","title":"Skill：三重差分助手（DDD 政策效果评估，Stata / R）","journal":"方法 Skill","year":"","url":"skills/ddd-skill.md","text":"# Skill：三重差分助手（DDD 政策效果评估，Stata / R）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n一份可复用的「三重差分（Difference-in-Difference-in-Differences, DDD）」政策效果评估工作流：当 DID 的平行趋势在某一维度不成立时，引入第三个维度做双重剔除，用三重交互项识别政策净效应。贯穿示例：「国家药品集中带量采购对公立医院（vs 民营医院）集采品种（vs 非集采品种）药品费用的影响」，三维 = 时间 × 医院类型 × 品种类型。**每一步都给出「方法 + 代码」**（Stata `reg` / `reghdfe` / `permute` 与 R `fixest::feols`），并内置一套**自洽的模拟面板数据**（60 家医院 × 2 类药品 × 2 期，真实 DDD 效应 δ = −8 万元/月）贯穿全流程，可直接作为提示词喂给大语言模型，也可作为操作手册照做。\n\n## 适用范围（明确边界）\n\n允许：\n- 帮助卫生政策评估研究者完成 DDD 的设定、估计、分解验证、稳健性检验与报告（社会医学、卫生经济学、药物经济学等）\n- 用 Stata / R 生成模拟面板数据、估计三重交互模型、做两个 2×2 DID 的分解表与安慰剂检验\n- 给出 DDD 识别假设（偏误同质性）、第三维度选择标准与事件研究式动态检验思路\n\n**不允许：**\n- 替研究者做「第三维度是否外生」「政策时点如何界定」的最终专业判断（需政策文件与领域知识支撑）\n- 承诺 DDD 一定优于 DID 或结果一定可发表；不替代计量经济学教科书（Wooldridge、Angrist & Pischke 等）与期刊审稿要求\n- 在未交代识别假设、未做分解表与稳健性检验的情况下武断下结论\n\n## 软件准备\n\n方法：Stata 17 及以上、R ≥ 4.2。Stata 侧 `reghdfe`（高维固定效应，作者 Sergio Correia）来自 SSC，依赖包 `ftools` 会随之自动安装；R 侧用 `fixest`（作者 Laurent Bergé，`feols` 支持固定效应 + 聚类稳健标准误）。所有命令与选项随版本更新，不确定处以 `help` / 帮助文档为准。\n\n```stata\n* Stata（联网安装一次）\nssc install reghdfe, replace\n* 若 ftools 未被自动装上：ssc install ftools, replace\n```\n\n```r\n# R\ninstall.packages(\"fixest\")\n```\n\n## 工作流程总览（7 步）\n\n0. 构造贯穿全程的模拟面板数据（60 医院 × 2 品种 × 2 期，内置真实 DDD 效应）→ 1. 从 DID 到 DDD：动机 → 2. 模型设定与估计 → 3. 核心系数 δ 的解读与分解验证 → 4. 平行趋势与稳健性 → 5. 多期扩展简述 → 6. 报告清单。以下每一步均为「方法 + 代码」。\n\n---\n\n### 第 0 步：构造模拟面板数据（贯穿全流程）\n\n**方法**：60 家医院（id 1–30 公立、31–60 民营）× 2 类药品（集采品种 / 非集采品种）× 2 期（政策前 / 政策后），共 240 个观测；结局 y = 月均药品费用（万元）。数据生成过程（DGP）为真实线性模型，**内置真实 DDD 效应 δ = −8**，并内置两个「偏误源」：γ12 = −6（公立医院特有趋势，如 DRG 付费、取消药品加成等同期政策）、γ23 = −4（集采品种特有趋势，如专利到期、临床替代）。每家医院加医院层面随机效应 u_i ~ N(0, 4²)，制造院内相关（这正是聚类的依据），再加个体噪声 ε ~ N(0, 2²)。另外生成两个稳健性结局（第 4 步用）：y_qty = 集采品种采购数量（相关结局，真实效应 δ = −3）、y_dev = 与药品无关的耗材费用（安慰剂结局，真实效应 δ = 0）。\n\n**代码**：\n\n```stata\n* ============ 第 0 步：模拟面板数据（贯穿全流程） ============\nclear all\nset seed 2026\n\n* 60 家医院：id 1-30 公立（treated = 1），31-60 民营（treated = 0）\nset obs 60\ngen id = _n\ngen treated = (id <= 30)          // 处理维度：1 = 公立医院（受集采约束）\ngen u_i = rnormal(0, 4)           // 医院层面随机效应（制造院内相关，聚类依据）\n\n* 每家医院 2 类药品：group = 1 集采品种，0 非集采品种\nexpand 2\nbysort id: gen group = _n - 1\n\n* 每个（医院 × 品种）2 期：post = 0 政策前，1 政策后\nexpand 2\nbysort id group: gen post = _n - 1\ngen time = post                   // 供 reghdfe absorb() 使用\n\n* ---------- 真实数据生成过程（DGP，内置真实 DDD 效应 δ = -8） ----------\n* y = γ0 + γ1·treated + γ2·post + γ3·group\n*     + γ12·treated×post + γ13·treated×group + γ23·post×group\n*     + δ·treated×post×group + u_i + ε\nlocal g0 100                        // 基组均值（民营 × 非集采 × 政策前）\nlocal g1 10                         // 公立医院整体费用水平更高\nlocal g2 -5                         // 全体时间趋势（普遍控费）\nlocal g3 20                         // 集采品种用量大、费用更高\nlocal g12 -6                        // 偏误 1：公立医院特有趋势（DRG / 取消加成等）\nlocal g13 5                         // 公立医院对集采品种的固定偏好\nlocal g23 -4                        // 偏误 2：集采品种特有趋势（专利到期等）\nlocal delta -8                      // 真实 DDD 效应：集采使公立医院集采品种额外下降 8 万元/月\n\ngen y = `g0' + `g1'*treated + `g2'*post + `g3'*group + ///\n        `g12'*treated*post + `g13'*treated*group + `g23'*post*group + ///\n        `delta'*treated*post*group + u_i + rnormal(0, 2)\n\n* ---------- 稳健性结局（第 4 步用） ----------\n* y_qty：集采品种采购数量（相关结局，真实效应较小 δ = -3）\ngen y_qty = 60 + 8*treated - 4*post + 18*group - 3*treated*post + ///\n            4*treated*group - 2*post*group - 3*treated*post*group + ///\n            u_i + rnormal(0, 3)\n* y_dev：与药品无关的耗材费用（安慰剂结局，真实 δ = 0）\ngen y_dev = 40 + 5*treated - 2*post + 2*group + 1*treated*post - ///\n            1*treated*group + 0.5*post*group + 0*treated*post*group + ///\n            0.5*u_i + rnormal(0, 5)\n\n* 数据体检：8 组均值表（Stata 17+；16 及更早用 bysort treated post group: summarize y）\ntable treated post group, stat(mean y) stat(sd y) nototals\n* 期望组均值（噪声平均掉后）：民营 非集采 前 100 / 后 95；民营 集采 前 120 / 后 111；\n*                             公立 非集采 前 110 / 后 99；公立 集采 前 135 / 后 112\n```\n\n```r\n# ============ 第 0 步：模拟面板数据（贯穿全流程） ============\nset.seed(2026)\nN <- 60                                     # 60 家医院\ndat <- expand.grid(id = 1:N, group = c(0, 1), post = c(0, 1))\ndat$treated <- as.integer(dat$id <= 30)     # 处理维度：1 = 公立医院\ndat$u_i     <- rnorm(N, 0, 4)[dat$id]       # 医院层面随机效应（聚类依据）\ndat$time    <- dat$post                     # 供 fixest 吸收固定效应\n\n# 真实参数（内置真实 DDD 效应 delta = -8）\ng0 <- 100; g1 <- 10; g2 <- -5; g3 <- 20\ng12 <- -6; g13 <- 5; g23 <- -4; delta <- -8\n\ndat$y <- with(dat, g0 + g1*treated + g2*post + g3*group +\n                   g12*treated*post + g13*treated*group + g23*post*group +\n                   delta*treated*post*group + u_i + rnorm(nrow(dat), 0, 2))\n\n# 稳健性结局：y_qty（相关结局，真实 δ = -3）；y_dev（安慰剂结局，真实 δ = 0）\ndat$y_qty <- with(dat, 60 + 8*treated - 4*post + 18*group - 3*treated*post +\n                      4*treated*group - 2*post*group - 3*treated*post*group +\n                      u_i + rnorm(nrow(dat), 0, 3))\ndat$y_dev <- with(dat, 40 + 5*treated - 2*post + 2*group + 1*treated*post -\n                      1*treated*group + 0.5*post*group + 0*treated*post*group +\n                      0.5*u_i + rnorm(nrow(dat), 0, 5))\n\n# 8 组均值表（应与下方期望值接近）\naggregate(y ~ treated + post + group, data = dat, FUN = mean)\n```\n\n---\n\n### 第 1 步：从 DID 到 DDD：动机\n\n**方法**：\n\n- **两个潜在偏误需要双重剔除**。本例若做单维 DID，任一维度都面临威胁：① 用「公立 vs 民营」做 DID（只看集采品种）——公立医院同时经历 DRG 付费改革、取消药品加成等**只作用于公立医院、不区分品种**的政策，其费用趋势与民营医院本来就不同（DGP 里 γ12 = −6）；② 用「集采品种 vs 非集采品种」做 DID（只看公立医院）——集采品种因专利到期、临床替代等**与医院类型无关**的原因费用下行（DGP 里 γ23 = −4）。两个威胁方向一致、大小不明，单维 DID 都会把它们混进政策效应里。\n- **三重交互 δ 的经济含义**：δ = 政策在「处理维度 × 第三维度」上的**增量**效果——公立医院集采品种费用在政策后的额外变化，同时剔除 (a) 公立医院所有品种的共同变化、(b) 集采品种在所有医院的共同变化。δ 不等于任何一组的前后差：公立·集采的前后差 −23 万元里混着 γ2 + γ12 + γ23 + δ 四项。\n- **图形直觉**：画四组均值线（公立·集采、公立·非集采、民营·集采、民营·非集采），DDD 就是「两条实线之间斜率差」减「两条虚线之间斜率差」。期望 8 组均值（噪声平均掉后）：\n\n| 单元格 | 政策前 | 政策后 | 前后差 |\n|---|---|---|---|\n| 公立 · 集采 | 135 | 112 | −23 |\n| 公立 · 非集采 | 110 | 99 | −11 |\n| 民营 · 集采 | 120 | 111 | −9 |\n| 民营 · 非集采 | 100 | 95 | −5 |\n\n品种 DID（公立切片）＝ −23 − (−11) ＝ −12（把 δ = −8 与 γ23 = −4 混在一起）；品种 DID（民营切片）＝ −9 − (−5) ＝ −4（纯 γ23）；DDD ＝ −12 − (−4) ＝ −8，正好剔掉 γ23。镜像读法：医院 DID（集采切片）＝ −23 − (−9) ＝ −14（混着 γ12 = −6）；医院 DID（非集采切片）＝ −11 − (−5) ＝ −6；DDD ＝ −14 − (−6) ＝ −8。\n- **什么时候用 DDD 而不是两个 DID**：两个维度的平行趋势都明显不可信、但可论证「威胁不随第三维度变化」时，用 DDD；只要某一维平行趋势（或偏误同质性）明显更可信，就优先用简单 DID——假设更少、效力更高、更好解释。第三维度必须分层清晰、尽量外生、8 个单元格样本均衡。\n\n**代码**：\n\n```stata\n* 图形直觉：四组均值线（先平均到 8 个单元格）\npreserve\ncollapse (mean) y, by(treated post group)\ntwoway (connected y post if treated==1 & group==1, sort) ///\n       (connected y post if treated==1 & group==0, sort) ///\n       (connected y post if treated==0 & group==1, sort) ///\n       (connected y post if treated==0 & group==0, sort), ///\n       legend(order(1 \"公立·集采\" 2 \"公立·非集采\" 3 \"民营·集采\" 4 \"民营·非集采\")) ///\n       xtitle(\"post（0 政策前 / 1 政策后）\") ytitle(\"月均药品费用（万元）\")\nrestore\n```\n\n```r\n# 图形直觉：四组均值线（两条 DID 线的斜率差之差 = DDD）\ncell <- aggregate(y ~ treated + post + group, data = dat, FUN = mean)\nplot(NA, xlim = c(0, 1), ylim = range(cell$y),\n     xlab = \"post（0 政策前 / 1 政策后）\", ylab = \"月均药品费用 y（万元）\",\n     main = \"四组均值线：DDD = 实线斜率差 - 虚线斜率差\")\nlines(y ~ post, data = cell, subset = treated == 1 & group == 1, type = \"b\", col = \"red\",  lty = 1)\nlines(y ~ post, data = cell, subset = treated == 1 & group == 0, type = \"b\", col = \"blue\", lty = 1)\nlines(y ~ post, data = cell, subset = treated == 0 & group == 1, type = \"b\", col = \"red\",  lty = 2)\nlines(y ~ post, data = cell, subset = treated == 0 & group == 0, type = \"b\", col = \"blue\", lty = 2)\nlegend(\"topright\", c(\"公立·集采\", \"公立·非集采\", \"民营·集采\", \"民营·非集采\"),\n       col = c(\"red\", \"blue\", \"red\", \"blue\"), lty = c(1, 1, 2, 2), bty = \"n\")\n```\n\n---\n\n### 第 2 步：模型设定与估计（Stata / R 对应）\n\n**方法**：饱和模型（所有主效应 + 全部两两交互 + 三重交互）：\n\nY = γ0 + γ1·treated + γ2·post + γ3·group + γ12·(treated×post) + γ13·(treated×group) + γ23·(post×group) + δ·(treated×post×group) + ε\n\n逐项解释（基组 = 民营 × 政策前 × 非集采品种）：\n\n- γ1（treated）：处理组与对照组的固定水平差（公立 vs 民营，品种平均）；\n- γ2（post）：政策前后全体平均变化（时间主效应）；\n- γ3（group）：第三维度两切片的固定差（集采 vs 非集采品种）；\n- γ12（treated×post）：**偏误 1**——处理组特有趋势（公立医院所有品种的共同变化）；\n- γ13（treated×group）：处理组内品种结构的固定差异（公立医院更依赖集采品种）；\n- γ23（post×group）：**偏误 2**——第三维度特有趋势（集采品种在所有医院的共同变化）；\n- δ（treated×post×group）：三重交互 = **政策净效应**，本 Skill 的核心系数。\n\n**两两交互项一个都不能漏**：漏掉 γ12 或 γ23，δ 会把偏误吞进去；漏掉主效应则模型不饱和。\n\n**Stata**：\n\n- `i.treated##i.post##i.group` 中 `##` =「主效应 + 交互」，`#` =「仅交互」。三者连写展开为 7 项：`i.treated`、`i.post`、`i.group`（3 个主效应）＋ `i.treated#i.post`、`i.treated#i.group`、`i.post#i.group`（3 个两两交互）＋ `i.treated#i.post#i.group`（1 个三重交互）。\n- 模型 A：`reg y i.treated##i.post##i.group, vce(cluster id)`（pooled OLS + 医院聚类稳健标准误）。\n- 模型 B：`reghdfe y i.treated##i.post##i.group, absorb(id time) vce(cluster id)`。`absorb(id)` 吸收医院固定效应后 `treated` 主效应与 id 共线、`absorb(time)` 吸收时间固定效应后 `post` 主效应与 time 共线，这两个主效应会被 reghdfe 自动剔除并在输出中标注（collinearity）；其余交互项仍可估，δ 与模型 A 相同（饱和交互设定下等价）。若希望保留全部主效应，用模型 A 或不吸收 time。\n- 以 help reghdfe 为准。\n\n**R（fixest）**：\n\n- `feols(y ~ treated*post*group | id + time, cluster = ~id)`。fixest 里 `*` 展开为主效应 + 全部交互（对应 Stata 的 `##`），`:` 只写单个交互（对应 Stata 的 `#`）；`(treated + post + group)^3` 也展开为全部 7 项。`|` 后为固定效应，`cluster = ~id` 为聚类稳健标准误。\n- 变量保持 0/1 数值型时，系数 `treated:post:group` 就是 δ；若改用 factor，fixest 会生成水平虚拟变量（需确认参照水平），因此建议保持 0/1 数值型。吸收 id、time 后 treated、post 因共线被自动移除（输出会提示 collinearity）。以 fixest 帮助文档为准。\n\n**代码**：\n\n```stata\n* ---------- 模型 A：pooled OLS + 医院聚类稳健标准误 ----------\nreg y i.treated##i.post##i.group, vce(cluster id)\nest store ddd_ols\n\n* ---------- 模型 B：医院 + 时间固定效应（reghdfe） ----------\nreghdfe y i.treated##i.post##i.group, absorb(id time) vce(cluster id)\nest store ddd_fe\n* 说明：treated、post 主效应被吸收（与 id / time 固定效应共线，输出中标注）；\n*       两两交互与三重交互仍可估，δ 与模型 A 相同\n```\n\n```r\nlibrary(fixest)\n\n# 模型 A：pooled OLS + 医院聚类稳健标准误（不吸收固定效应）\nm_ols <- feols(y ~ treated*post*group, cluster = ~id, data = dat)\nsummary(m_ols)\n\n# 模型 B：医院 + 时间固定效应（treated、post 因共线被自动移除）\nm_fe <- feols(y ~ treated*post*group | id + time, cluster = ~id, data = dat)\nsummary(m_fe)\n\n# 三重交互系数 δ（fixest 中交互用 \":\" 连接，对应 Stata 的 #）\ncoef(m_fe)[\"treated:post:group\"]\n```\n\n---\n\n### 第 3 步：核心系数 δ 的解读与分解验证\n\n**方法**：\n\n- **δ 的边际含义（两种双重差分之差）**：\n  δ ＝ [E(Y|公立,集采,后) − E(Y|公立,集采,前) − E(Y|公立,非集采,后) + E(Y|公立,非集采,前)] − [民营对应项] ＝ **品种 DID（公立切片）− 品种 DID（民营切片）**；镜像等价于 **医院 DID（集采切片）− 医院 DID（非集采切片）**。\n- **验证性分解表**：先跑两个 2×2 DID（第三维度各取一个切片），再跑完整 DDD，展示「DDD ＝ DID1 − DID2」。用本模拟数据：DID1（品种|公立）≈ −12、DID2（品种|民营）≈ −4、DDD ≈ −12 − (−4) ＝ −8 ＝ 主回归三重交互系数；镜像分解 −14 − (−6) ＝ −8。若分解表与三重交互系数对不上，先查模型设定（漏交互项、参照组、编码）而不是查数据。\n- **报告口径**：δ 的单位与 y 一致（本例为「万元/月」）；若 y 取对数，δ ≈ 半弹性（百分点的近似）。分解表同时暴露两个偏误的方向与大小（本例：−6 与 −4，均使单维 DID 高估政策效果）。\n\n**代码**：\n\n```stata\n* ---------- 3.1 两个 2×2 DID（品种类型 DID，在公立/民营切片上） ----------\nreg y i.post##i.group if treated == 1, vce(cluster id)\nlocal did1 = _b[1.post#1.group]          // 期望 ≈ -12（= γ23 + δ）\nreg y i.post##i.group if treated == 0, vce(cluster id)\nlocal did2 = _b[1.post#1.group]          // 期望 ≈ -4（= γ23）\n\n* ---------- 3.2 分解：DDD = DID1 - DID2 ----------\ndisplay \"DID1(品种|公立) = \" %5.2f `did1'\ndisplay \"DID2(品种|民营) = \" %5.2f `did2'\ndisplay \"DDD = DID1 - DID2 = \" %5.2f (`did1' - `did2')\n\n* ---------- 3.3 镜像分解（医院类型 DID，在集采/非集采切片上） ----------\nreg y i.treated##i.post if group == 1, vce(cluster id)\nlocal did1b = _b[1.treated#1.post]       // 期望 ≈ -14（= γ12 + δ）\nreg y i.treated##i.post if group == 0, vce(cluster id)\nlocal did2b = _b[1.treated#1.post]       // 期望 ≈ -6（= γ12）\ndisplay \"镜像分解：DID(医院|集采) - DID(医院|非集采) = \" %5.2f (`did1b' - `did2b')\n\n* ---------- 3.4 与主回归三重交互系数对照（期望 ≈ -8） ----------\nreg y i.treated##i.post##i.group, vce(cluster id)\ndisplay \"主回归三重交互系数 δ = \" %5.2f _b[1.treated#1.post#1.group]\n```\n\n```r\n# 3.1 两个 2×2 DID（品种类型 DID 在公立/民营切片上）\ndid1 <- coef(feols(y ~ post*group | id, cluster = ~id, data = dat[dat$treated == 1, ]))[\"post:group\"]\ndid2 <- coef(feols(y ~ post*group | id, cluster = ~id, data = dat[dat$treated == 0, ]))[\"post:group\"]\nc(DID1 = unname(did1), DID2 = unname(did2), DDD = unname(did1 - did2))\n# 期望：DID1 ≈ -12，DID2 ≈ -4，DDD ≈ -8 = δ\n\n# 3.2 镜像分解（医院类型 DID 在集采/非集采切片上），期望 ≈ -8\nd1 <- coef(feols(y ~ treated*post | id, cluster = ~id, data = dat[dat$group == 1, ]))[\"treated:post\"]\nd2 <- coef(feols(y ~ treated*post | id, cluster = ~id, data = dat[dat$group == 0, ]))[\"treated:post\"]\nunname(d1 - d2)\n\n# 3.3 与主回归三重交互系数对照（期望 ≈ -8）\ncoef(m_fe)[\"treated:post:group\"]\n```\n\n---\n\n### 第 4 步：平行趋势与稳健性（识别假设、动态检验、安慰剂、替换结局）\n\n**方法**：\n\n- **DDD 的识别假设（比 DID 更弱）**：\n  1. **偏误同质性（核心，替代平行趋势）**：不要求任一单维的平行趋势成立，只要求「一个维度 DID 的偏误不随第三维度变化」——即公立医院特有趋势对集采与非集采品种相同（γ12 不再 ×group），或集采品种特有趋势对公立与民营相同（γ23 不再 ×treated）。写作假设：E[ε|treated, post, group] 中所有「×group」的项只经 γ13 与 δ 进入。\n  2. 无政策预期（anticipation）：政策落地前医院、厂商、医保未提前调整行为。\n  3. 无跨单元溢出（SUTVA）：集采不改变民营医院对应品种的定价/用量，非集采品种不因替代效应被波及；若溢出存在，需建模或换第三维度。\n  4. 政策时点无其他与「医院类型 × 品种类型」交互的同期冲击。\n- **第三维度选择三原则**：① 第三维度尽量外生（不受政策影响，如目录固定于政策前版本）；② 两个切片「共享同一时间趋势威胁」更可信（偏误同质才站得住）；③ 8 个单元格样本不能太空（决定效力）。\n- **事件研究式动态检验**：两期数据无法做动态图（只有一期政策后）。多期数据下把 post 换成事件时间虚拟变量，看三重交互系数在政策前是否 ≈ 0（偏误同质的动态版本）、政策后如何演化；政策前一期为基准。\n- **安慰剂 1（伪第三维度）**：随机重排 group，破坏「品种维度」结构后重估 δ，置换分布应围绕 0，真实 δ = −8 应落在尾部。Stata 用 `permute`（注意：它置换的是 group 的全部效应，检验的是「group 与 y 无关」的整体零假设，比纯三重交互零假设更严格）；R 用医院内重排（保留医院结构）。\n- **安慰剂 2（伪时点）**：需 ≥ 3 期数据，把政策时点前移，用「伪政策」前数据估计 δ 应 ≈ 0；本模拟仅两期，只能给示意代码。\n- **替换结局**：相关结局（采购数量 y_qty，δ ≈ −3）方向一致、量级不同属正常；无关结局（耗材费用 y_dev，δ ≈ 0）是安慰剂结局，显著则提示混杂。\n- **聚类层级敏感性**：主结果按处理分配层级（医院）聚类；敏感性可做多向聚类（reghdfe `vce(cluster id time)`；fixest 的 `vcov` / `cluster` 多变量写法以帮助文档为准）。\n\n**代码**：\n\n```stata\n* ---------- 动态检验思路（示意，需多期数据；本模拟两期无法执行） ----------\n* 假设 time = -2..2，政策时点 = 0，基准期 = -1：\n* reghdfe y ib(-1).time##i.treated##i.group, absorb(id) vce(cluster id)\n* 关注各期 1.time#1.treated#1.group 系数（政策前应 ≈ 0）\n* 画图：ssc install coefplot; coefplot, keep(*.time#1.treated#1.group) vertical yline(0)\n* 具体 keep 匹配规则以 help coefplot 为准\n\n* ---------- 安慰剂 1：伪第三维度（Stata permute，200 次） ----------\n* 随机置换 group，破坏品种维度结构；真实 δ = -8 应落在置换分布左尾之外\npermute group delta = _b[1.treated#1.post#1.group], ///\n        reps(200) seed(2026): reg y i.treated##i.post##i.group, vce(cluster id)\n\n* ---------- 安慰剂 2：伪时点（示意，需 time = 0..3 的多期数据） ----------\n* gen post_fake = (time == 1)        // 把政策提前到 t=1，只用真政策前数据\n* reg y i.treated##i.post_fake##i.group if time < 2, vce(cluster id)  // 期望 δ ≈ 0\n\n* ---------- 替换结局 ----------\nreghdfe y_qty i.treated##i.post##i.group, absorb(id time) vce(cluster id)  // 相关结局：期望 δ ≈ -3\nreghdfe y_dev i.treated##i.post##i.group, absorb(id time) vce(cluster id)  // 安慰剂结局：期望 δ ≈ 0\n\n* ---------- 聚类层级敏感性：多向聚类（reghdfe 支持） ----------\nreghdfe y i.treated##i.post##i.group, absorb(id time) vce(cluster id time)\n```\n\n```r\n# ---------- 安慰剂 1（R 版）：伪第三维度，医院内随机交换品种标签，200 次 ----------\nset.seed(2026)\nperm_delta <- replicate(200, {\n  d <- dat\n  d$group_p <- ave(d$group, d$id, FUN = sample)      # 每家医院内部重排，保留医院结构\n  coef(feols(y ~ treated*post*group_p | id + time, data = d))[\"treated:post:group_p\"]\n})\nhist(perm_delta, main = \"伪第三维度：δ 的置换分布（200 次）\")\nabline(v = -8, col = \"red\", lwd = 2)                 # 真实 δ = -8 应落在左尾之外\nmean(perm_delta <= -8)                               # 置换单侧 p 值（期望接近 0）\n\n# ---------- 替换结局 ----------\ncoef(feols(y_qty ~ treated*post*group | id + time, cluster = ~id, data = dat))[\"treated:post:group\"]  # 期望 ≈ -3\ncoef(feols(y_dev ~ treated*post*group | id + time, cluster = ~id, data = dat))[\"treated:post:group\"]  # 期望 ≈ 0\n\n# 伪时点（示意，需 ≥3 期数据）：把政策时点前移后用真政策前数据估计 δ，期望 ≈ 0；\n# 写法同上，仅替换 post 变量与子样本（以 fixest 帮助文档为准）\n```\n\n---\n\n### 第 5 步：多期扩展简述（交错 DDD）\n\n**方法（点到为止）**：真实政策常多批落地（如各省集采执行时间不同），此时静态「前后」二分会把异质时点压平，且交错处理下的两两比较存在负权重问题。现状：① 传统做法仍是静态 DDD + 三重事件研究（triple event study，见第 4 步动态检验）；② Callaway & Sant'Anna (2021)、Sun & Abraham (2021)、de Chaisemartin & D'Haultfœuille (2020)、Borusyak et al. (2022) 等交错 DID 估计器主要针对两维设定，三重差分扩展可先在第三维切片上做「差之差」再套用 CS 类估计器，或使用将 CS 框架推广到 DDD 的新文献；Olden & Møen (2022) 系统梳理了 DDD 的识别条件，建议先读。**交错 DID 与 CS 类估计器的完整工作流（含代码）见同仓库的《DID 助手》Skill**，本 Skill 不再展开。\n\n---\n\n### 第 6 步：报告清单\n\n**方法**：正式报告至少覆盖六块——\n\n1. **三维定义**：时间维度（政策时点 + 政策文件依据）、处理维度（受政策约束 vs 不受约束）、第三维度（分层依据：如集采目录 vs 非集采），并论证第三维度选择（外生性、偏误同质性的理由）；\n2. **各组均值表**：2×2×2 八个单元格的均值、SD、N（第 0 步输出）；\n3. **主回归表**：逐步回归——只放主效应 → 加两两交互 → 加三重交互；报 δ、聚类稳健 SE、95% CI、N 与聚类数；\n4. **DDD 分解表**：两个 2×2 DID 系数 + 差值（第 3 步），展示 DDD ＝ DID1 − DID2 与主回归 δ 一致，并标注两个偏误的方向与大小；\n5. **稳健性**：伪第三维度、伪时点（若多期）、替换结局、事件研究图（若多期）、聚类层级敏感性；\n6. **局限**：偏误同质假设的论证强度、第三维度与政策的相关性、溢出可能（集采价格联动民营 / 非集采品种）、8 单元格样本均衡性与外部效度。\n\n**代码**：\n\n```stata\n* 主回归表：逐步（仅主效应 → +两两交互 → +三重交互）\nreg y i.treated i.post i.group, vce(cluster id)\nest store m1\nreg y i.treated i.post i.group i.treated#i.post i.treated#i.group i.post#i.group, vce(cluster id)\nest store m2\nreg y i.treated##i.post##i.group, vce(cluster id)\nest store m3\nestimates table m1 m2 m3, b(%9.2f) se stats(N)\n* 报告时另附 95% CI（reg 输出自带）与聚类数（60 家医院）\n```\n\n```r\n# 主回归表：逐步（fixest 的 etable；模型 SE 已在估计时按 id 聚类）\nm1 <- feols(y ~ treated + post + group, cluster = ~id, data = dat)\nm2 <- feols(y ~ treated*post + treated*group + post*group, cluster = ~id, data = dat)\nm3 <- feols(y ~ treated*post*group, cluster = ~id, data = dat)\netable(m1, m2, m3)          # 选项以 ?etable 为准\n```\n\n---\n\n## 常见错误清单\n\n- [ ] 只放三重交互、漏掉两两交互项（尤其 treated×post 与 post×group）→ δ 吞入偏误，模型不饱和\n- [ ] 把三重交互系数直接当因果、不交代识别假设（偏误同质性、无预期、SUTVA）\n- [ ] 混淆 DDD 与三因素 ANOVA：统计设定相同，但 ANOVA 无「政策」与因果承诺；DDD 的全部含义在识别假设与分解表\n- [ ] 聚类层级错误：按「医院 × 品种 × 时点」聚类（过度聚类）或忽略医院层面相关；应按处理分配层级（医院）聚类，再做多向聚类敏感性\n- [ ] 第三维度与处理相关：第三维度本身受政策影响（如目录因集采扩容），或第三维度选择未论证外生性\n- [ ] 仍要求两个维度各自满足平行趋势——要求过强；DDD 只需「偏误不随第三维度变化」，报告里要写清楚这一点\n- [ ] 分解表方向写反：DID1 − DID2 的减数与被减数弄反，正负号错且与主回归 δ 对不上\n- [ ] 两期数据硬做动态事件研究或伪时点（不可行）；两期只能做伪第三维度 + 替换结局 + 假设论证\n- [ ] permute / 随机化安慰剂不设种子、置换时打乱医院内部结构（R 版应先按 id 分组再重排）\n- [ ] reghdfe 吸收 id 后 treated 主效应被 drop（共线标注），却仍强行解读其主效应\n- [ ] 固定效应 / 对数变换下不交代 δ 的解释单位（万元/月 vs 百分点近似）\n- [ ] 不报分解表与 8 组均值表，读者无法核对三重交互的构成\n\n## 输出格式（DDD 报告模板）\n\n```\n【三维定义】\n时间维度（政策时点及文件依据）；处理维度（受政策约束 vs 不受约束）；第三维度（分层依据）；\n第三维度选择论证（外生性、偏误同质性理由）。\n\n【描述统计】\n表 1：2×2×2 八个单元格的均值、SD、N（公立/民营 × 集采/非集采 × 政策前/后）。\n\n【主回归】\n表 2：逐步回归（主效应 → +两两交互 → +三重交互），报 δ、聚类稳健 SE、95% CI、N、聚类数；\n固定效应版本（reghdfe / feols）与 pooled OLS 并列。\n\n【DDD 分解】\n表 3：DID1（维度 A 的 2×2 DID，第三维度切片 1）− DID2（第三维度切片 0）＝ δ；\n标注两个偏误的方向与大小；附镜像分解。\n\n【稳健性】\n表 4：伪第三维度（置换 p 值）、伪时点（若多期）、替换结局、事件研究图（若多期）、\n聚类层级敏感性（多向聚类）。\n\n【局限】\n识别假设（偏误同质）论证；第三维度与政策相关性；溢出可能（价格联动、替代效应）；\n8 单元格样本均衡性；外部效度。\n```\n\n## 使用提示\n\n- 命令与选项随版本更新：运行 `help reghdfe`、`help permute`、R 侧 `help(feols)` 核对；不确定的语法一律以官方帮助文档为准。\n- 建议流程：先用第 0 步模拟数据跑通全部代码（分解表应精确还原 δ = −8），再套用到真实数据；真实数据先跑两个 2×2 DID 与 8 组均值表，判断是否需要 DDD。\n- 聚类层级以「处理分配的层级」为准（本例：医院）；敏感性用多向聚类（reghdfe `vce(cluster id time)`）。\n- 两期数据无法做动态检验与伪时点，务必如实说明，不要硬造趋势图。\n- 本 Skill 提供方法与代码模板；政策背景判断、第三维度选择与最终结论由研究者负责。\n\n---\n\n## 引用 / Citation\n\n中文：杜思娴. 三重差分助手：DDD 卫生政策效果评估 Skill [EB/OL]. (2026). https://naturedu.github.io/socialmedicine/.\n\nEnglish: Du, S. (2026). *Triple Differences Assistant: DDD for health policy evaluation skill* [Skill definition]. https://naturedu.github.io/socialmedicine/.\n","kind":"方法 Skill","locator":"方法手册","license":"MIT（仓库原有技能）","verified":"原有公开仓库"}},{"id":"studio-library:skill-did-skill","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Skill：DID 双重差分助手（卫生政策效果评估，Stata / R）","summary":"# Skill：DID 双重差分助手（卫生政策效果评估，Stata / R）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n一份可复用的「双重差分（Difference-in-Differences, DID）」卫生政策评估工作流：从研究设计与识别假设、经典 2×2 DID 估计、平行趋势检验，到多期 / 交错处理 DID（Callaway & Sant'Anna, 2021）与事件研究图，再到安慰剂检验、PSM-DID 等稳健性分析与政策评估报告。适用于医保目录调整、DRG/DIP 支付改革、药品集中采购、分级诊疗试点、慢病管理项目等卫生政策因果效应评估。**每一步都给出「方法 + 代码」**，可直接作为提示词喂给大语言模型，也可作为操作手册照做。\n\n**贯穿全文的示例场景**：某地级市药品集中采购试点对**次均药品费用（y）与住院率（adm）**的影响。全文使用**同一套自洽的模拟面板数据**：60 个地区 × 4 期、30 个处理组（交错入组：10 个在 t=2、10 个在 t=3、10 个在 t=4 执行集采）、30 个永不受理对照组；数据生成过程内置**真实 ATT = −150 元/期**（次均药品费用）与 −0.02（住院率）。Stata 与 R 代码可直接复制运行。\n\n## 适用范围（明确边界）\n\n允许：\n- 帮助研究者完成卫生政策评估的 DID 全流程（","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-did-skill","title":"Skill：DID 双重差分助手（卫生政策效果评估，Stata / R）","journal":"方法 Skill","year":"","url":"skills/did-skill.md","text":"# Skill：DID 双重差分助手（卫生政策效果评估，Stata / R）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n一份可复用的「双重差分（Difference-in-Differences, DID）」卫生政策评估工作流：从研究设计与识别假设、经典 2×2 DID 估计、平行趋势检验，到多期 / 交错处理 DID（Callaway & Sant'Anna, 2021）与事件研究图，再到安慰剂检验、PSM-DID 等稳健性分析与政策评估报告。适用于医保目录调整、DRG/DIP 支付改革、药品集中采购、分级诊疗试点、慢病管理项目等卫生政策因果效应评估。**每一步都给出「方法 + 代码」**，可直接作为提示词喂给大语言模型，也可作为操作手册照做。\n\n**贯穿全文的示例场景**：某地级市药品集中采购试点对**次均药品费用（y）与住院率（adm）**的影响。全文使用**同一套自洽的模拟面板数据**：60 个地区 × 4 期、30 个处理组（交错入组：10 个在 t=2、10 个在 t=3、10 个在 t=4 执行集采）、30 个永不受理对照组；数据生成过程内置**真实 ATT = −150 元/期**（次均药品费用）与 −0.02（住院率）。Stata 与 R 代码可直接复制运行。\n\n## 适用范围（明确边界）\n\n允许：\n- 帮助研究者完成卫生政策评估的 DID 全流程（医保目录调整、DRG/DIP 支付改革、药品集中采购、分级诊疗试点、慢病管理项目等）\n- 给出 2×2 DID、多期 DID、交错处理 DID（Callaway–Sant'Anna）的估计、检验与作图代码\n- 平行趋势检验、安慰剂检验（伪处理组 / 伪时点）、剔除样本、PSM-DID、更换结局与聚类层级等稳健性分析\n\n**不允许：**\n- 替研究者下「政策因果效应成立」的最终结论（DID 是基于假设的识别策略，需结合制度背景、数据与检验功效综合论证）\n- 承诺结果一定可发表；不替代计量经济学教科书与期刊审稿要求\n- 在未讨论平行趋势、共同冲击与溢出、未做稳健性检验的情况下武断下结论\n\n## 软件准备\n\n方法：Stata 17 及以上（`didregress` / `xtdidregress` 需 17+；官方 `csdid` / `csdid_plot` / `xthdidregress` 需 18+）、R ≥ 4.2（`fixest`、`did`、`MatchIt`、`ggplot2` 等）。SSC 安装 `csdid`、`drdid`、`reghdfe`、`coefplot`、`estout`、`ritest`、`psmatch2`；Stata 18+ 自带官方 `csdid`，若另装 SSC 版，运行 `which csdid` 核对实际调用的版本。\n\n```stata\n* Stata（联网安装一次）\nssc install csdid,    replace    // 交错处理 DID（Callaway-Sant'Anna）\nssc install drdid,    replace    // 双重稳健 DID（csdid 的依赖）\nssc install reghdfe,  replace    // 高维固定效应（事件研究回归用）\nssc install ftools,   replace    // reghdfe 的依赖\nssc install coefplot, replace    // 系数图\nssc install estout,   replace    // 结果表\nssc install ritest,   replace    // 随机化推断（安慰剂检验备选）\nssc install psmatch2, replace    // 倾向得分匹配\n```\n\n```r\n# R\ninstall.packages(c(\"fixest\", \"did\", \"MatchIt\", \"ggplot2\", \"cobalt\", \"modelsummary\"))\n```\n\n## 工作流程总览（数据准备 + 7 步）\n\n0. 生成模拟面板数据 → 1. 研究设计与识别假设 → 2. 经典 2×2 DID → 3. 平行趋势检验 → 4. 多期 / 交错处理 DID（Callaway–Sant'Anna）→ 5. 事件研究图 → 6. 稳健性检验 → 7. 报告清单。以下每一步均为「方法 + 代码」。\n\n---\n\n### 第 0 步：生成贯穿全文的模拟面板数据\n\n**方法**：60 个地区（id = 1…60）、4 期（t = 1…4）。处理组 30 个、交错入组（队列 g：10 个 g=2、10 个 g=3、10 个 g=4），对照组 30 个永不受理（g=0）。数据生成过程（DGP）：\n\ny_it = α_i + β_i·(t−1) + λ_t + (−150)·D_it + ε_it\n\n- α_i ~ N(900, 50)：地区固定效应（地区平均次均药品费用水平）\n- β_i ~ N(−8, 4)：地区自身线性趋势\n- λ_t = −30·(t−1)：共同时间趋势（如集采目录外药价下行、全国性控费）\n- D_it = 1[t ≥ g_i]：处理指示（g_i = 0 为永不受理）\n- ε_it ~ N(0, 40)\n\n**内置真实 ATT = −150**：处理后每期次均药品费用下降 150 元，且各队列、各期效应同质——2×2、事件研究、Callaway–Sant'Anna 估计量都应回收 ≈ −150。次要结局 adm（住院率）真实效应 −0.02。协变量 x1、x2 为时不变地区特征（x1 与入选相关，供 PSM-DID 使用）。\n\n**代码**：\n\n```stata\n* ============================================================\n* 模拟面板：60 地区 × 4 期；30 个处理组交错入组；真实 ATT = -150\n* ============================================================\nclear all\nset seed 2026\nset obs 60\ngen id = _n\n* 处理时点 g（0 = 永不受理）\ngen g = 0\nreplace g = 2 if inrange(id, 1, 10)\nreplace g = 3 if inrange(id, 11, 20)\nreplace g = 4 if inrange(id, 21, 30)\ngen treated = (g > 0)\n* 地区特征（时不变）：固定效应、自身趋势、可观测协变量\ngen alpha = rnormal(900, 50)        // 地区平均次均费用水平\ngen beta  = rnormal(-8, 4)          // 地区自身趋势（元/期）\ngen x1 = rnormal(0.3*treated, 1)    // 协变量 1：与入选相关\ngen x2 = rnormal(0, 1)              // 协变量 2\n* 4 期面板\nexpand 4\nbysort id: gen t = _n\ngen lambda = -30 * (t - 1)          // 共同时间趋势\ngen D = (t >= g & g > 0)            // 处理指示\ngen y   = alpha + beta*(t-1) + lambda + (-150)*D + rnormal(0, 40)           // 次均药品费用\ngen adm = 0.20 - 0.01*(t-1) + (alpha-900)/3000 + (-0.02)*D + rnormal(0, 0.04)  // 住院率\nxtset id t\nsave \"did_demo.dta\", replace\n* 数据体检\nxtdes                               // 面板结构：60 个地区 × 4 期、无缺失\ntab g                               // 队列规模：g=2/3/4 各 10，g=0 共 30\ntabstat y if t == 1, by(treated) stat(n mean sd)   // 干预前基线比较\n```\n\n```r\n# ============================================================\n# 模拟面板：60 地区 × 4 期；30 个处理组交错入组；真实 ATT = -150\n# ============================================================\nset.seed(2026)\nN <- 60; Tmax <- 4\nid  <- 1:N\ng   <- c(rep(2, 10), rep(3, 10), rep(4, 10), rep(0, 30))   # 处理时点；0 = 永不受理\ntreated <- as.integer(g > 0)\nalpha <- rnorm(N, 900, 50)          # 地区固定效应\nbeta  <- rnorm(N, -8, 4)            # 地区自身趋势\nx1 <- rnorm(N, 0.3 * treated, 1)    # 协变量 1：与入选相关\nx2 <- rnorm(N, 0, 1)                # 协变量 2\n\ndat <- expand.grid(id = id, t = 1:Tmax)\ndat$g <- g[dat$id]; dat$treated <- treated[dat$id]\ndat$alpha <- alpha[dat$id]; dat$beta <- beta[dat$id]\ndat$x1 <- x1[dat$id]; dat$x2 <- x2[dat$id]\ndat$D <- as.integer(dat$t >= dat$g & dat$g > 0)\ndat$y <- dat$alpha + dat$beta * (dat$t - 1) - 30 * (dat$t - 1) +\n         (-150) * dat$D + rnorm(N * Tmax, 0, 40)                       # 次均药品费用\ndat$adm <- 0.20 - 0.01 * (dat$t - 1) + (dat$alpha - 900) / 3000 +\n           (-0.02) * dat$D + rnorm(N * Tmax, 0, 0.04)                  # 住院率\n\ntable(dat$g)                        # 队列规模\naggregate(y ~ treated + t, data = dat, FUN = mean)     # 各期均值（干预前应接近）\n```\n\n---\n\n### 第 1 步：研究设计与识别假设\n\n**方法**：\n\n- **处理组 / 对照组**：政策覆盖的地区、机构或人群为处理组，未覆盖且不受影响者为对照组。明确「谁在何时受处理」：处理变量 = 组指示 × 时点指示。对照组要求「若未受政策，其结局路径与处理组平行」。\n- **干预时点**：以政策**实际生效**时点为准（如集采落地执行月份、DRG 实际结算月份）；注意 phase-in（分批执行）→ 用交错处理设定；注意预期效应（政策公告早于执行）→ 把公告期单列检验。\n- **平行趋势假设（概念与直觉）**：反事实是「若无干预，处理组的结局变化应等于对照组的变化」。直觉：处理组与对照组在干预前有共同趋势，则干预后二者差异中「超出既有趋势的部分」可归因于政策。DID 识别的正是「处理组实际变化 − 处理组若无干预的变化（用对照组变化替代）」。\n- **共同冲击假设**：除所评估政策外，同期没有其他只影响处理组（或只影响对照组）的冲击（如其他政策、疫情、支付规则变动）；若有，需讨论其对估计的偏误方向。\n- **何时不能用 DID**：① 政策覆盖全国或全人群、找不到可比对照；② 处理组与对照组干预前趋势明显不平行且无修正办法；③ 处理组的选择与随时间变化的结局冲击直接相关（DID 无法消除时变选择）；④ 溢出 / 一般均衡效应强（对照组被政策间接影响）；⑤ 结局定义或测量方式在两组间随时间发生不同变化（测量不一致）；⑥ 只有一期数据（DID 至少需要干预前后各一期）。\n\n**代码**（数据体检与基线图）：\n\n```stata\n* 干预前基线平衡表（t=1 期）\ntabstat y adm x1 x2 if t == 1, by(treated) stat(n mean sd)\n* 各期结局均值图（第一道识别直觉：干预前两组路径应平行）\negen ybar = mean(y), by(treated t)\ntwoway (line ybar t if treated == 1) (line ybar t if treated == 0), ///\n    legend(order(1 \"处理组\" 2 \"对照组\")) xline(2.5, lp(dash)) ///\n    title(\"各期次均药品费用：处理组 vs 对照组（干预前应平行）\")\ndrop ybar\n```\n\n```r\n# 干预前基线平衡表\naggregate(cbind(y, adm, x1, x2) ~ treated, data = subset(dat, t == 1), FUN = mean)\n# 各期均值图\nmeans <- aggregate(y ~ treated + t, data = dat, FUN = mean)\nwith(means, {\n    plot(t, y, type = \"n\", xlab = \"t\", ylab = \"次均药品费用\")\n    lines(t[treated == 1], y[treated == 1], col = \"red\",  lwd = 2)\n    lines(t[treated == 0], y[treated == 0], col = \"blue\", lwd = 2)\n    abline(v = 2.5, lty = 2)\n    legend(\"topright\", c(\"处理组\", \"对照组\"), col = c(\"red\", \"blue\"), lwd = 2)\n})\n```\n\n---\n\n### 第 2 步：经典 2×2 DID\n\n**方法**：两期面板（干预前一期 t=1、干预后一期 t=2）。估计式：\n\ny_it = β0 + β1·treated_i + β2·post_t + β3·(treated_i × post_t) + ε_it\n\n`reg y i.treated##i.post` 的系数含义：`_cons` = 对照组干预前均值；`1.treated` = 干预前处理组 − 对照组的基线差异；`1.post` = 对照组的共同时间变化；**`1.treated#1.post` = DID = ATT**（处理组变化 − 对照组变化）。标准误在**干预分配层级**（地区 id）聚类。演示中处理组 = 第 2 期入组队列（treated2 = g=2）；对照组 = 其余地区（含 g=3/4 的「尚未处理」者——t=1、2 时它们同样未受处理，是合法对照，这正是 not-yet-treated 思想）。预期系数 ≈ −150。等价写法是双向固定效应（只保留交互项）；官方命令 `didregress`（Stata 17+）自动完成 FE 并报告 ATET。\n\n**代码**：\n\n```stata\n* 两期子样本：t=1 干预前、t=2 干预后；处理组 = t=2 期入组队列\npreserve\nkeep if inlist(t, 1, 2)\ngen post = (t == 2)\ngen treated2 = (g == 2)                // 10 个处理组；g=3/4 在 t<=2 尚未处理，作为对照\n* 经典 2×2（OLS + 交互项）\nreg y i.treated2##i.post, vce(cluster id)\nest store m_2x2\n* 系数解释：1.treated2 = 基线差异；1.post = 共同时间变化；\n*           1.treated2#1.post = ATT ≈ -150（集采使次均药品费用下降约 150 元）\n\n* 等价：双向固定效应（系数相同）\nreg y i.treated2#i.post i.id i.t, vce(cluster id)\n\n* 官方命令（Stata 17+）：didregress（自动 FE 与 ATET 报告）\ndidregress (y) (treated2), group(id) time(t) vce(cluster id)\n* 注：didregress 假设处理组单一入组时点；交错处理请用第 4 步的 csdid\nrestore\n```\n\n```r\nlibrary(fixest)\nd2 <- subset(dat, t %in% 1:2)\nd2$post <- as.integer(d2$t == 2)\nd2$treated2 <- as.integer(d2$g == 2)\n\n# 双向固定效应 DID：组/期主效应被 FE 吸收，交互项 = ATT\nm_2x2 <- feols(y ~ treated2 * post | id + t, data = d2, cluster = ~id)\nsummary(m_2x2)                       # treated2:post ≈ -150\n\n# 等价：不含 FE 的经典 2×2（两期时系数相同，方便逐项解释）\nm_ols <- feols(y ~ treated2 * post, data = d2, cluster = ~id)\nsummary(m_ols)\n# 系数：treated2 = 基线差异；post = 共同时间变化；treated2:post = ATT\n```\n\n**结果解读**：`treated2:post`（或 `1.treated2#1.post`）≈ −150，解释为「集采试点使处理地区的次均药品费用平均下降约 150 元」；95% CI 不含 0 即为统计显著。模拟数据每次重跑略有噪声；如需更精确回收 −150，可调小 ε 方差或增大样本。同时报告聚类数与观测数（本例 60 个聚类、120 条观测）。\n\n---\n\n### 第 3 步：平行趋势检验\n\n**方法**：\n\n- **图示（事件研究）**：以**干预前一期**为基准（rel = −1），估计各相对时期处理组 − 对照组的差异；干预前各期系数应 ≈ 0 且在 0 附近（图上无趋势性漂移）。\n- **正式检验**：联合检验**仅干预前**各期系数是否为 0（Stata `test` / `estat ptrends`；R `wald` / `did::pre.test`）。**只能检验干预前趋势**——干预后系数是动态效应，不是平行趋势的证据。\n- **检验的局限**：检验功效有限，「不拒绝」≠「平行趋势成立」；应结合制度背景论证，并报告干预前系数的置信区间（看是否接近 0，而非只看显著性）；基准期选干预前一期（选最早一期功效差）。Roth (2022) 建议做 pre-trend 检验的功效分析。\n\n**代码**（用 g=3 队列演示：t=1、2 为干预前，t=3、4 为干预后）：\n\n```stata\n* 事件研究（手工交互虚拟变量，基准 = 干预前一期 rel = -1）\npreserve\nkeep if inlist(g, 0, 3)\ngen treated3 = (g == 3)\ngen rel = t - 3                         // rel = -2,-1,0,1\ngen ev_m2 = (rel == -2 & treated3 == 1)\ngen ev_m1 = (rel == -1 & treated3 == 1) // 基准（回归中省略）\ngen ev_0  = (rel ==  0 & treated3 == 1)\ngen ev_1  = (rel ==  1 & treated3 == 1)\nreghdfe y ev_m2 ev_0 ev_1, absorb(id t) vce(cluster id)\ntest ev_m2 = 0                          // 正式检验：干预前系数 = 0\ncoefplot, keep(ev_*) vertical yline(0, lcolor(red)) xline(1.5, lcolor(gs8) lp(dash))\n* 图：ev_m2（干预前）应 ≈ 0；ev_0、ev_1（干预后）应 ≈ -150\n* 注：本例受 4 期数据限制只有 1 个前趋势系数；真实研究应有 3 期以上干预前数据，\n*     并联合检验全部前趋势系数（test ev_m2 ev_m3 ... = 0）\n\n* 官方替代（Stata 17+）：didregress + 预趋势检验\ndidregress (y) (treated3), group(id) time(t) vce(cluster id)\nestat trendplots                        // 各期处理-对照差异图（含置信区间）\nestat ptrends                           // 平行趋势联合检验（H0：干预前各期系数均为 0）\nrestore\n```\n\n```r\n# 事件研究（fixest 的 i() 交互；基准 rel = -1）\nlibrary(fixest)\nes <- subset(dat, g %in% c(0, 3))\nes$treated3 <- as.integer(es$g == 3)\nes$rel <- es$t - 3\nm_es <- feols(y ~ i(rel, treated3, ref = -1) | id + t, data = es, cluster = ~id)\nsummary(m_es)\niplot(m_es)                             # 事件研究图：干预前系数应 ≈ 0\n# 正式检验：干预前系数（本例为 rel = -2）\ncoef(m_es)                              # 系数名形如 rel::-2:treated3（以实际输出为准）\nwald(m_es, \"rel::-2:treated3\")          # 名称以 coef(m_es) 输出为准\n# 亦可用 coefplot(m_es)（fixest 自带）；选项以 help(coefplot.fixest) 为准\n# 交错处理时可用 did::pre.test()（对 CS 估计做干预前联合检验，以 help(pre.test) 为准）\n```\n\n---\n\n### 第 4 步：多期 / 交错处理 DID（Callaway & Sant'Anna, 2021）\n\n**方法**：交错处理（staggered adoption）下，把「不同时点入组的处理组」混在单一 post 的 TWFE 估计量，是各 2×2 比较的加权平均，效应异质时可能出现**负权重**而偏误（Goodman-Bacon, 2021；de Chaisemartin & D'Haultfœuille, 2020；Sun & Abraham, 2021）。Callaway & Sant'Anna (2021) 定义**组×期处理效应 ATT(g,t) = E[Y_t(g) − Y_t(0) | G = g]**（队列 g 在日历期 t 的效应，仅 t ≥ g 有定义），对每个 (g,t) 用「尚未处理 / 永不受理」队列作干净对照分别估计，再按研究问题聚合：\n\n- `simple`：总体 ATT（各 ATT(g,t) 的加权平均，权重与组规模、暴露期数相关）\n- `dynamic` / `event`：按事件时间 e = t − g 聚合 → 事件研究（第 5 步作图）\n- `group`：按队列 g 聚合\n- `calendar`：按日历期 t 聚合\n\n识别假设：处理不可逆（吸收处理）、无预期效应、按队列的平行趋势、有限重叠（每个 (g,t) 有可用对照）。Stata 18+ 有官方 `csdid` / `xthdidregress`；Stata 17 及更早用 SSC 版 `csdid`（依赖 `drdid`）。R 用 `did`（Callaway & Sant'Anna 官方实现）。\n\n**代码**：\n\n```stata\n* Stata 18+：官方 csdid；Stata 17 及更早：ssc install csdid, replace + ssc install drdid, replace\n* 全样本：30 个处理组在 t=2/3/4 交错入组（g=2/3/4），30 个永不受理（g=0）\ncsdid y, ivar(id) time(t) gvar(g)\n* 聚合（官方 18：estat simple/event/group/calendar；SSC 版：csdid_estat simple ...）\nestat simple                  // 总体 ATT ≈ -150\nestat event                   // 事件时间聚合（供第 5 步作图）\nestat group                   // 按队列聚合\n* 选项（method、对照组选择、权重自举等）以 help csdid 为准\n\n* 官方替代（Stata 18+）：xthdidregress，处理时点由 D 自动识别\nxthdidregress ra (y) (D), group(id) time(t)\n* 聚合与作图：estat aggregation / estat atetplot；语法以 help xthdidregress postestimation 为准\n```\n\n```r\n# Callaway-Sant'Anna（官方 R 实现）\nlibrary(did)\nout <- att_gt(yname = \"y\", tname = \"t\", idname = \"id\", gname = \"g\",\n              data = dat, control_group = \"notyettreated\")\nsummary(out)                            # ATT(g,t) 表：队列 g × 时期 t\nagg_simple <- aggte(out, type = \"simple\")    # 总体 ATT ≈ -150\nsummary(agg_simple)\nagg_group  <- aggte(out, type = \"group\")     # 按队列\nagg_cal    <- aggte(out, type = \"calendar\")  # 按日历期\n# 对照组设定、估计方法（est_method = \"dr\"/\"reg\"/\"ipw\"）等选项以 help(att_gt) 为准\n\n# 替代：Sun & Abraham（fixest）\nfeols(y ~ sunab(g, t) | id + t, data = dat, cluster = ~id)\n```\n\n**结果解读**：ATT(g,t) 表中处理前（t < g）应 ≈ 0，t ≥ g 应 ≈ −150；`agg_simple` ≈ −150，与 2×2 结果一致。报告时注明**聚合类型**与**对照组设定**（not-yet-treated vs never-treated，二者识别假设不同）。\n\n---\n\n### 第 5 步：事件研究图\n\n**方法**：把第 4 步的 `event` / `dynamic` 聚合画成图：横轴 = 相对处理期 e = t − g，纵轴 = 平均处理效应；干预前（e < 0）应 ≈ 0（平行趋势的可视证据），干预后（e ≥ 0）为动态效应。报告置信区间与基准期（e = −1）。图中干预前系数的置信带不应明显偏离 0；干预后曲线走势即「政策效果的时间路径」（如集采效果逐步释放、延迟生效）。相对期区间由数据决定（本例 t = 1…4、g = 2…4 → e ∈ [−3, 2]）。单一入组时点下，第 3 步的手工事件研究图与 CS 动态图一致；交错处理时以 CS 动态图为准。\n\n**代码**：\n\n```stata\n* 接第 4 步 csdid\nestat event                   // 官方 18；SSC 版为 csdid_estat event\ncsdid_plot                    // 事件研究图（官方 18 与 SSC 版均有）\n* 若版本支持，亦可用 marginsplot：estat event 后 marginsplot\n* （以 help csdid postestimation / help csdid_plot 为准）\n```\n\n```r\n# 接第 4 步\nagg_dyn <- aggte(out, type = \"dynamic\", min_e = -3, max_e = 2)\nsummary(agg_dyn)                    # 事件研究系数表（含各期置信区间）\nggdid(agg_dyn)                      # 事件研究图（需 ggplot2）\nggdid(agg_dyn, ylim = c(-300, 60), xlab = \"相对处理期\",\n      title = \"动态处理效应：集采对次均药品费用\")\n# ggdid 的图形选项以 help(ggdid) 为准\n```\n\n---\n\n### 第 6 步：稳健性检验\n\n**方法**：\n\n- **安慰剂检验——伪处理组**：把「处理组身份」随机重抽（保持处理组规模与真实一致），重复 ≥ 数百次，得到伪 DID 系数分布；真实系数应位于分布尾部（经验 p 值 < 0.05）。**单次安慰剂不足以说明问题**。\n- **安慰剂检验——伪时点（预期效应检验）**：把干预时点人为提前到真实时点之前、只用干预前数据估计；系数应 ≈ 0，显著非零提示预期效应或前趋势。\n- **剔除部分样本**：剔除极端值（缩尾）、剔除个别地区 / 时期后重估，结论应稳定。\n- **PSM-DID**：若处理组选择与可观测协变量相关，先按干预前（时不变）协变量做倾向得分匹配，再在匹配样本上做 DID——匹配消除可观测基线差异，DID 消除共同趋势。注意：协变量必须是干预前变量；PSM 不能替代平行趋势论证。\n- **更换结局 / 聚类层级**：换用次要结局（如住院率）；把聚类层级从地区改为更保守的层级（如上级地市 / 医院集团），标准误结论应稳定。\n\n**代码**：\n\n```stata\n* ---------- 6.1 伪处理组（随机重抽处理组身份，500 次） ----------\npreserve\nkeep if inlist(t, 1, 2)\ngen post = (t == 2)\ntempname pf\ntempfile pfres\npostfile `pf' b_did using `pfres', replace\nset seed 2026\nforvalues r = 1/500 {\n    * 在地区层面抽一个伪处理身份（同一地区两期身份一致）\n    bysort id: gen rnd_id = runiform() if _n == 1\n    bysort id (rnd_id): replace rnd_id = rnd_id[1]\n    egen prank = rank(rnd_id), unique\n    gen treatedF = (prank <= 10)          // 随机 10 个伪处理地区\n    quietly reg y i.treatedF##i.post, vce(cluster id)\n    post `pf' (_b[1.treatedF#1.post])\n    drop rnd_id prank treatedF\n}\npostclose `pf'\nuse `pfres', clear\nsummarize b_did, detail                  // 伪系数分布：中心应 ≈ 0\ncount if b_did <= -150                   // 伪系数 ≤ 真实系数（-150）的次数\ndi (r(N) + 1) / 501                      // 经验 p 值（单侧）\nhistogram b_did, normal xline(-150, lcolor(red))\n* 备选：ritest（SSC）做随机化推断，支持聚类层级（选项以 help ritest 为准）\nrestore\n\n* ---------- 6.2 伪时点（预期效应检验） ----------\n* 用 g=3 队列的干预前两期（t=1,2），人为假设 t=2 已干预\npreserve\nkeep if inlist(g, 0, 3) & inlist(t, 1, 2)\ngen post = (t == 2)\ngen treated3 = (g == 3)\nreg y i.treated3##i.post, vce(cluster id)     // 预期系数 ≈ 0\nrestore\n\n* ---------- 6.3 剔除部分样本 ----------\n* (a) 剔除 y 上下 1% 极端值后重估 2×2\npreserve\nkeep if inlist(t, 1, 2)\ngen post = (t == 2)\ngen treated2 = (g == 2)\nquietly summarize y, detail\ndrop if y < r(p1) | y > r(p99)\nreg y i.treated2##i.post, vce(cluster id)\nrestore\n* (b) 对照只用永不受理组（排除尚未处理队列），多期 FE 估计\npreserve\nkeep if inlist(g, 0, 2)\ngen post = (t >= 2)\ngen treated2 = (g == 2)\nreghdfe y i.treated2#i.post, absorb(id t) vce(cluster id)\nrestore\n\n* ---------- 6.4 PSM-DID ----------\npreserve\nkeep if inlist(t, 1, 2)\ngen post = (t == 2)\ngen treated2 = (g == 2)\n* 方案 A（官方 teffects）：横截面 ATT 演示（把两期分别视为观测）\nteffects psmatch (y) (treated2 x1 x2), atet\n* 方案 B（psmatch2 + DID，SSC；实务常用）：按干预前协变量匹配后做 DID\npsmatch2 treated2 x1 x2, out(y) logit common\npstest x1 x2, both                        // 匹配后平衡性：标准化差异应 < 0.1\nreg y i.treated2##i.post [pw = _weight], vce(cluster id)\nest store m_psm\n* 注：psmatch2 生成的 _weight 为匹配权重；以 help psmatch2 为准\nrestore\n\n* ---------- 6.5 更换结局与聚类层级 ----------\npreserve\nkeep if inlist(t, 1, 2)\ngen post = (t == 2)\ngen treated2 = (g == 2)\nreg adm i.treated2##i.post, vce(cluster id)      // 结局换为住院率：预期 ≈ -0.02\nest store m_adm\n* 聚类层级敏感性：地区 → 更保守的层级（示例：每 3 个地区并为一组）\negen group3 = group(ceil(id/3))\nreg y i.treated2##i.post, vce(cluster group3)    // 聚类数变少，标准误通常变大\nrestore\n```\n\n```r\n# 接第 2 步的 d2（t ∈ {1,2}；post、treated2 已定义）\nlibrary(fixest)\n\n# ---------- 6.1 伪处理组（500 次随机重抽） ----------\nset.seed(2026)\nids <- unique(d2$id)\nb_null <- replicate(500, {\n    fake <- sample(ids, 10)                      # 随机 10 个伪处理地区\n    d2$treatedF <- as.integer(d2$id %in% fake)\n    m <- feols(y ~ treatedF * post | id + t, data = d2, cluster = ~id)\n    coef(m)[\"treatedF:post\"]                     # 系数名以 coef(m) 输出为准\n})\npval <- (sum(b_null <= -150) + 1) / 501          # 经验 p 值（单侧）\nhist(b_null, breaks = 30, main = \"伪处理组 DID 系数分布\")\nabline(v = -150, col = \"red\", lwd = 2)\n\n# ---------- 6.2 伪时点 ----------\nd_pre <- subset(dat, g %in% c(0, 3) & t %in% 1:2)\nd_pre$post <- as.integer(d_pre$t == 2)\nd_pre$treated3 <- as.integer(d_pre$g == 3)\nfeols(y ~ treated3 * post | id + t, data = d_pre, cluster = ~id)   # 预期 ≈ 0\n\n# ---------- 6.3 剔除极端值 ----------\nq <- quantile(d2$y, c(0.01, 0.99))\nd2t <- subset(d2, y > q[1] & y < q[2])\nfeols(y ~ treated2 * post | id + t, data = d2t, cluster = ~id)\n\n# ---------- 6.4 PSM-DID ----------\nlibrary(MatchIt)\nd2pre <- subset(d2, t == 1)\nm.out <- matchit(treated2 ~ x1 + x2, data = d2pre, method = \"nearest\", ratio = 1)\nsummary(m.out)                       # 匹配后标准化均值差应 < 0.1\n# library(cobalt); love.plot(m.out)  # 平衡性图\nmd  <- match.data(m.out)             # 匹配样本（含 weights）\nd2m <- subset(d2, id %in% unique(md$id))\nfeols(y ~ treated2 * post | id + t, data = d2m, cluster = ~id)\n\n# ---------- 6.5 更换结局与聚类层级 ----------\nfeols(adm ~ treated2 * post | id + t, data = d2, cluster = ~id)     # 住院率 ≈ -0.02\nd2$group3 <- ceiling(d2$id / 3)\nfeols(y ~ treated2 * post | id + t, data = d2, cluster = ~group3)   # 更保守聚类\n```\n\n---\n\n### 第 7 步：报告清单\n\n**方法**：政策评估报告至少包含以下内容，逐项核对：\n\n1. **处理与对照定义**：谁在处理组、谁在对照；处理变量构造（组指示 × 时点指示）；队列规模表。\n2. **干预时点**：政策生效时点、phase-in 说明、预期效应讨论。\n3. **平行趋势证据**：事件研究图（含置信区间）+ 干预前系数联合检验 p 值；结合制度背景论证。\n4. **主要 ATT 估计表**：系数、聚类稳健 SE、95% CI、观测数与聚类数；交错处理报聚合类型（simple / dynamic / group）。\n5. **聚类标准误层级说明**：干预在哪个层级分配就在哪个层级聚类（本例：地区）；说明理由并报告层级敏感性。\n6. **稳健性汇总**：伪处理组分布与经验 p 值、伪时点、剔除样本、PSM-DID、更换结局、更换聚类层级的结果一览。\n7. **局限**：平行趋势不可完全检验、溢出 / 一般均衡、同期政策干扰、外部效度。\n\n**代码**（结果表输出）：\n\n```stata\n* 结果表（esttab，SSC；官方 etable 亦可）\nesttab m_2x2 m_psm m_adm, b(%9.2f) se(%9.2f) star(* 0.10 ** 0.05 *** 0.01) ///\n    mtitles(\"2x2 DID\" \"PSM-DID\" \"结局=住院率\") ///\n    stats(N N_clust, labels(\"观测数\" \"聚类数\"))\n* CS 聚合结果用 estat simple / estat event 直接导出；\n* esttab 与 etable 选项以 help esttab / help etable 为准\n```\n\n```r\n# 结果表（fixest::etable 或 modelsummary）\netable(list(m_2x2, m_psm, m_adm), cluster = ~id, fitstat = ~n)\n# library(modelsummary)\n# modelsummary(list(\"2x2\" = m_2x2, \"PSM-DID\" = m_psm, \"住院率\" = m_adm), stars = TRUE)\n```\n\n---\n\n## 常见错误清单\n\n- [ ] 用干预后年份的系数「检验」平行趋势（干预后系数是动态效应，不是平行趋势的证据）\n- [ ] 两期数据硬做事件研究 / 交错处理估计（两期只能做 2×2；交错处理需要多期 + 多个入组时点）\n- [ ] 交错处理仍用单一 post 的 TWFE，不报告 Callaway–Sant'Anna 等稳健估计量（效应异质时可能有负权重偏误）\n- [ ] 忽略聚类层级：地区层面干预却在个体 / 人次层面聚类（标准误过小、过度拒绝）\n- [ ] 处理组内部生效时点不同却统一用单一 post（把尚未处理的地区算成已处理）\n- [ ] 把相关当因果：不讨论平行趋势、共同冲击与溢出，只做政策前后比较就下因果结论\n- [ ] 安慰剂检验只做一次（单次随机化可能是偶然，需 ≥ 数百次置换并报告分布与经验 p 值）\n- [ ] 平行趋势检验只看 p 值：不画图、不报置信区间、不讨论检验功效与基准期选择\n- [ ] 把「前趋势检验不显著」直接当作「平行趋势成立」的证明\n- [ ] 不报告处理组 / 对照组的事前基线平衡表与「谁在何时受处理」的构造\n- [ ] 在 Stata 17 用 didregress 评估交错处理（didregress 假设单一处理时点；交错用 csdid / xthdidregress）\n- [ ] 用干预后协变量做 PSM（匹配变量必须是干预前或时不变变量）\n- [ ] 事件研究基准期取最早一期而非干预前一期（功效差、图难读）\n\n## 输出格式（政策评估报告模板）\n\n```\n【研究设计与数据】\n- 政策背景与干预时点；处理组/对照组定义、规模与队列分布（表 0）\n- 数据来源、时期、结局定义（次均药品费用、住院率）；干预前基线平衡表\n\n【识别假设】\n- 平行趋势论证：图 1 事件研究图 + 干预前系数联合检验 p 值 + 制度背景\n- 共同冲击、无预期效应、无溢出的讨论；不满足时的处理策略\n\n【主要结果】\n- 表 1：2×2 DID（或 didregress ATET）：系数、聚类稳健 SE、95% CI、观测数与聚类数\n- 表 2（交错处理时）：Callaway-Sant'Anna 聚合 ATT（simple/dynamic/group，注明聚合类型与对照组）\n- 图 2：动态处理效应（事件研究）图，注明基准期与置信区间\n\n【稳健性】\n- 表 3：伪处理组系数分布与经验 p 值（附直方图）；伪时点系数\n- 表 4：剔除极端值/部分样本、PSM-DID、更换结局、更换聚类层级结果汇总\n\n【结论与局限】\n- 效应大小与政策含义（如集采使次均药品费用下降约 X 元、住院率变化）\n- 局限：平行趋势不可完全检验、溢出/一般均衡、同期政策干扰、外部效度\n```\n\n## 使用提示\n\n- 命令与选项随版本变化：Stata 18+ 有官方 `csdid` / `csdid_plot` / `xthdidregress`；Stata 17 用 `didregress`；更早版本用 SSC `csdid`。运行 `which csdid`、`help csdid`、`help didregress` 核对实际调用的版本与选项；凡标注「以 help / 帮助文档为准」处，运行前先核对本机文档。\n- 聚类层级：干预在哪个层级分配就在哪个层级聚类；不确定时做多层级敏感性分析。\n- 模拟数据用于演示完整流程（内置真实 ATT = −150）；真实研究需按政策实际设计处理组与干预时点，并核对数据质量、样本量与检验功效。\n- 估计前先画「处理组 vs 对照组」各期均值图（第 1 步代码），图是识别策略的第一道检验。\n- 本 Skill 提供方法与代码模板；平行趋势等识别假设的论证与最终结论由研究者负责。\n\n---\n\n## 引用 / Citation\n\n中文：杜思娴. DID 双重差分助手：卫生政策效果评估 Skill [EB/OL]. (2026). https://naturedu.github.io/socialmedicine/.\n\nEnglish: Du, S. (2026). *DID Assistant: Difference-in-differences for health policy evaluation skill* [Skill definition]. https://naturedu.github.io/socialmedicine/.\n","kind":"方法 Skill","locator":"方法手册","license":"MIT（仓库原有技能）","verified":"原有公开仓库"}},{"id":"studio-library:skill-health-economics-tutorial-skill","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Skill：卫生经济学评价教程（CEA/CUA 总纲，Stata / R）","summary":"# Skill：卫生经济学评价教程（CEA/CUA 总纲，Stata / R）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n一份可复用的「卫生经济学评价（Health Economic Evaluation）」入门教程型 Skill：从研究问题与评价类型选择（CEA / CUA / CMA / CBA）、研究视角与时间范围、成本测算、效果与效用指标（QALY / DALY）、贴现、增量分析与 ICER、不确定性分析，到 CHEERS 2022 报告规范与配套工具箱指引，覆盖完整研究流程。面向社会医学与卫生事业管理、卫生经济学、公共卫生专业的硕士生、博士生与研究者。**每一步都给出「方法 + 代码」**（Stata / R，数值自洽的教学算例可直接运行），可直接作为提示词喂给大语言模型，也可作为操作手册照做；结尾指引读者按需使用同一网站的其他配套 Skill（DCE 设计助手、Markov 模型助手、机器学习、DID 双重差分）。\n\n## 适用范围（明确边界）\n\n允许：\n- 帮助研究者完成卫生经济学评价全流程的方案设计与计算（CEA、CUA、CMA、CBA）\n- 给出成本测算、QALY / DALY 计算、贴现、ICER、单因素敏感性分析与 PSA 的 Stata/R 代码\n- 用作研究生入门教学、课题方法部分写作与自查清单\n\n**不允许：**\n- 替代研究者的专业判断（","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-health-economics-tutorial-skill","title":"Skill：卫生经济学评价教程（CEA/CUA 总纲，Stata / R）","journal":"方法 Skill","year":"","url":"skills/health-economics-tutorial-skill.md","text":"# Skill：卫生经济学评价教程（CEA/CUA 总纲，Stata / R）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n一份可复用的「卫生经济学评价（Health Economic Evaluation）」入门教程型 Skill：从研究问题与评价类型选择（CEA / CUA / CMA / CBA）、研究视角与时间范围、成本测算、效果与效用指标（QALY / DALY）、贴现、增量分析与 ICER、不确定性分析，到 CHEERS 2022 报告规范与配套工具箱指引，覆盖完整研究流程。面向社会医学与卫生事业管理、卫生经济学、公共卫生专业的硕士生、博士生与研究者。**每一步都给出「方法 + 代码」**（Stata / R，数值自洽的教学算例可直接运行），可直接作为提示词喂给大语言模型，也可作为操作手册照做；结尾指引读者按需使用同一网站的其他配套 Skill（DCE 设计助手、Markov 模型助手、机器学习、DID 双重差分）。\n\n## 适用范围（明确边界）\n\n允许：\n- 帮助研究者完成卫生经济学评价全流程的方案设计与计算（CEA、CUA、CMA、CBA）\n- 给出成本测算、QALY / DALY 计算、贴现、ICER、单因素敏感性分析与 PSA 的 Stata/R 代码\n- 用作研究生入门教学、课题方法部分写作与自查清单\n\n**不允许：**\n- 替代研究者的专业判断（研究视角、时间范围、参数取值与阈值结论需结合课题背景与领域专家意见）\n- 承诺研究结果一定可发表；不替代教科书、《中国药物经济学评价指南（2020）》与期刊审稿要求\n- 在未报告不确定性、未说明假设的情况下武断给出「具有成本-效果」的结论\n\n## 软件准备\n\n方法：Stata 17 及以上、R ≥ 4.2。本教程的基础代码只用 Stata 官方命令与 R 基础包，`boot`（bootstrap 置信区间）是唯一需额外安装的 R 包。涉及偏好测量见《DCE 设计助手》，动态建模与完整 PSA 见《Markov 模型助手》。\n\n```stata\n* Stata（本教程主要用官方命令；结果表导出可选用 estout，putexcel 为官方命令）\nssc install estout, replace\n```\n\n```r\n# R\ninstall.packages(\"boot\")      # bootstrap 置信区间；其余均为基础 R\n```\n\n## 工作流程总览（9 步）\n\n1. 提出研究问题与评价类型选择（CEA / CUA / CMA / CBA）→ 2. 研究视角与时间范围 → 3. 成本测算 → 4. 效果与效用指标（QALY / DALY）→ 5. 贴现 → 6. 增量分析与 ICER → 7. 不确定性分析 → 8. 报告规范（CHEERS 2022）→ 9. 研究工具箱指引（配套 Skill）。以下每一步均为「方法 + 代码」。\n\n贯穿第 3–7 步的教学算例（数值自洽，可逐字运行）：某慢性病患者人群，比较**新方案（干预）**与**常规方案（对照）**，社会视角，时间范围 10 年，贴现率 5%。年总成本：干预 12,000 元（直接医疗 9,180 + 直接非医疗 600 + 间接 2,220）、对照 8,000 元（直接医疗 6,040 + 直接非医疗 500 + 间接 1,460）；年健康效用：干预 0.78、对照 0.70。\n\n---\n\n### 第 1 步：提出研究问题与评价类型选择\n\n**方法**：\n\n- **四种评价类型**：\n  - **CMA（最小成本分析）**：结局已被证明（或有充分证据）等效，只比较成本。前提严格，实践中极少能完全满足，慎用。\n  - **CEA（成本-效果分析）**：效果用自然单位（血压 mmHg、避免的发作次数、生命年等），适合结局单一的研究；不同自然单位之间不可直接比较。\n  - **CUA（成本-效用分析）**：CEA 的特例，效果用 QALY / DALY 等偏好加权指标，可跨疾病比较，是药品经济学评价与医保准入最常用的类型。\n  - **CBA（成本-效益分析）**：成本与效果都货币化（支付意愿 WTP），用净货币收益（NMB）或效益-成本比判断；当需要与教育、环境等非健康领域比较时使用。\n- **选择决策树（按顺序判断）**：① 结局是否等效？是 → CMA；② 是否需要跨部门 / 跨领域比较？是 → CBA；③ 效果能否且需要转换为偏好加权指标（QALY）？是 → CUA；④ 其余情况 → CEA。国内指南默认推荐 CEA/CUA。\n- **PICO-T 研究问题模板**（在 P 中，与 C 相比，I 在 T 内对 O 的增量成本与效果如何）：\n\n```\nP（人群）：某慢性病患者（示例：2 型糖尿病、病程 ≥ 5 年、无严重并发症）\nI（干预）：新综合管理方案\nC（对照）：常规治疗\nO（结局）：QALY（及并发症发生率等自然单位结局）\nT（时间范围）：10 年\n研究问题：在 P 中，与 C 相比，I 在 T 年内对 O 产生的增量成本与增量效果如何？\n           ICER 是否低于支付意愿阈值 λ？\n```\n\n**代码**：\n\n```stata\n* Stata：评价类型选择示意（把结局类型写入 local 后运行）\nlocal outcome \"qaly\"    // 可选：equivalent / natural / qaly / monetary\nif \"`outcome'\" == \"equivalent\" {\n    display \"结局已证明等效 → 用 CMA（最小成本分析）\"\n}\nelse if \"`outcome'\" == \"natural\" {\n    display \"结局为自然单位（血压、生命年等）→ 用 CEA（成本-效果分析）\"\n}\nelse if \"`outcome'\" == \"qaly\" {\n    display \"结局为偏好加权指标（QALY/DALY）→ 用 CUA（成本-效用分析）\"\n}\nelse {\n    display \"结局已货币化或需跨领域比较 → 用 CBA（成本-效益分析）\"\n}\n```\n\n```r\n# R：评价类型选择决策函数\npick_type <- function(outcome = c(\"equivalent\", \"natural\", \"qaly\", \"monetary\")) {\n  outcome <- match.arg(outcome)\n  switch(outcome,\n    equivalent = \"CMA：最小成本分析（结局等效时只比较成本）\",\n    natural    = \"CEA：成本-效果分析（自然单位结局）\",\n    qaly       = \"CUA：成本-效用分析（QALY/DALY 等偏好加权结局）\",\n    monetary   = \"CBA：成本-效益分析（成本与效果均货币化）\")\n}\npick_type(\"qaly\")     # 本算例用 QALY → CUA\n```\n\n---\n\n### 第 2 步：研究视角与时间范围\n\n**方法**：\n\n- **研究视角决定纳入哪些成本**：\n  - **医保支付方视角**：只纳入医保基金支付的项目（报销的药品、检查、住院等）。\n  - **卫生体系视角**：医保基金 + 政府财政投入 + 患者自付医疗费用等所有卫生体系内资源。\n  - **社会视角**：卫生体系成本 + 患者与家庭的直接非医疗成本（交通、陪护、食宿）+ 生产力损失（间接成本）。信息最全、最接近全社会的真实代价，但数据要求最高；《中国药物经济学评价指南（2020）》建议以全社会或卫生体系视角为主，至少明确报告视角。\n- **时间范围**：必须**足够长以覆盖干预引起的全部重要成本与效果差异**——慢性病常需 10 年以上乃至终身，疫苗与急性感染可短至数月到数年。时间范围过短会系统性遗漏后期收益（尤其预防、筛查类「成本前置、收益后置」的干预），此时贴现率的影响也更大。本算例取 10 年。\n- **提示**：视角、时间范围在研究方案中先定，并在结果表中注明；换视角或换时间范围必须重新测算，不能事后拼凑。\n\n**代码**：\n\n```stata\n* Stata：成本条目按研究视角纳入的示意见表（1 = 纳入，0 = 不纳入）\n* 注：中文值需 Stata 17+（UTF-8）\nclear\ninput byte payer byte system byte society str50 item\n1 1 1 \"药品费用（医保报销部分）\"\n1 1 1 \"检查与住院（医保报销部分）\"\n0 1 1 \"政府财政补助与公共卫生投入\"\n0 1 1 \"患者自付医疗费用\"\n0 0 1 \"交通、陪护与食宿（患者家庭）\"\n0 0 1 \"因病损失的生产力\"\nend\nlabel var payer   \"医保支付方视角\"\nlabel var system  \"卫生体系视角\"\nlabel var society \"社会视角\"\nlist, sep(0) noobs\n```\n\n```r\n# R：不同研究视角纳入的成本条目（示例矩阵）\nitems <- data.frame(\n  条目 = c(\"药品费用（医保报销部分）\", \"检查与住院（医保报销部分）\", \"政府财政补助与公共卫生投入\",\n           \"患者自付医疗费用\", \"交通、陪护与食宿（患者家庭）\", \"因病损失的生产力\"),\n  医保支付方 = c(\"√\", \"√\", \"—\", \"—\", \"—\", \"—\"),\n  卫生体系   = c(\"√\", \"√\", \"√\", \"√\", \"—\", \"—\"),\n  社会视角   = c(\"√\", \"√\", \"√\", \"√\", \"√\", \"√\"))\nitems\n```\n\n---\n\n### 第 3 步：成本测算\n\n**方法**：\n\n- **三类成本**：\n  - **直接医疗成本**：药品、检查、手术、住院、康复等卫生资源消耗，按卫生服务量 × 单位价格估值。\n  - **直接非医疗成本**：患者与家庭为就医发生的交通、食宿、陪护等支出。\n  - **间接成本（生产力损失）**：因病休工或早亡损失的生产时间。**人力资本法（HCA）**按工资（或人均 GDP）乘以损失时间；**摩擦成本法（FCM）**只计入「岗位空缺到恢复生产」的摩擦期（通常 3–6 个月），更保守。指南 2020 建议社会视角下报告生产力损失，二法择一并说明理由。\n- **成本测算三步**：① **识别**——列出与干预相关的资源条目（依据指南、临床路径、专家咨询）；② **测量**——记录资源消耗数量（人次、天数、剂量）；③ **估值**——数量 × 单位成本。\n- **中国情境提示**：单位成本来源常用——各省市医疗服务项目价格规范、国家/省级药品耗材集中采购中标价、《国家基本医疗保险药品目录》报销规则；**必须注明价格年份与地区**，跨年价格用 CPI 或医疗 CPI 调整到基年（如 2026 年）。\n- **统计**：成本数据通常右偏，除正态近似 CI 外建议报告 bootstrap CI；组间比较看成本差而非只看各组均值。\n\n**代码**：\n\n```stata\n* Stata：直接医疗成本示例数据（元/人/年；与贯穿算例一致：对照均值 6040、干预均值 9180）\nclear\ninput byte group int cost\n0 6000\n0 5500\n0 6800\n0 5900\n0 6200\n0 6100\n0 5700\n0 6400\n0 5800\n0 6000\n1 9000\n1 8800\n1 9500\n1 9200\n1 8900\n1 9600\n1 9100\n1 9300\n1 9000\n1 9400\nend\nlabel define grp 0 \"对照\" 1 \"干预\"\nlabel values group grp\n\n* 均数与 95% CI（正态近似）\nmean cost, over(group)\nci means cost if group == 0\nci means cost if group == 1\n\n* bootstrap：两组成本之差的 95% 百分位区间与偏差校正（BC）区间\ncapture program drop diff_cost\nprogram define diff_cost, rclass\n    quietly summarize cost if group == 0\n    local m0 = r(mean)\n    quietly summarize cost if group == 1\n    local m1 = r(mean)\n    return scalar diff = `m1' - `m0'\nend\nbootstrap r(diff), reps(1000) seed(2026) nodots: diff_cost\nestat bootstrap, percentile bc\n```\n\n```r\n# R：直接医疗成本示例数据（贯穿算例：对照组均值 6040、干预组均值 9180）\ncost0 <- c(6000, 5500, 6800, 5900, 6200, 6100, 5700, 6400, 5800, 6000)\ncost1 <- c(9000, 8800, 9500, 9200, 8900, 9600, 9100, 9300, 9000, 9400)\nc(mean0 = mean(cost0), mean1 = mean(cost1))\nt.test(cost0)$conf.int          # 对照组 95% CI（正态近似）\nt.test(cost1)$conf.int          # 干预组 95% CI\n\n# bootstrap：两组成本之差的 95% 百分位 CI\nlibrary(boot)\nd <- data.frame(cost = c(cost0, cost1), group = rep(c(0, 1), each = 10))\ndiff_mean <- function(data, i) {\n  g <- data[i, ]\n  mean(g$cost[g$group == 1]) - mean(g$cost[g$group == 0])\n}\nset.seed(2026)\nb <- boot(d, diff_mean, R = 1000)\nboot.ci(b, type = \"perc\")       # 差值点估计 ≈ 9180 - 6040 = 3140 元\n```\n\n---\n\n### 第 4 步：效果与效用指标\n\n**方法**：\n\n- **QALY（质量调整生命年）** = 生存时间 × 健康效用权重（0 = 死亡，1 = 完全健康；可为负，表示比死亡更差的健康状态）。多状态时逐段累加：QALY = Σ 效用_i × 时长_i。\n- **DALY（伤残调整生命年）** = YLL（早亡损失生命年）+ YLD（伤残状态生存年 × 伤残权重）。QALY 与 DALY 概念互补，数值不可直接互换，选一个并说明理由。\n- **效用值的测量**：\n  - 直接法：标准博弈（SG）、时间权衡（TTO），测量负担重；\n  - 间接法（多属性效用量表）：**EQ-5D** 最常用——EQ-5D-3L 用中国人群 TTO 积分体系（Liu 等，2014）；EQ-5D-5L 用已发表的中国积分体系（存在多个版本，**报告时必须注明所用体系**）；\n  - 陈述性偏好法：**DCE 也可测量偏好权重与支付意愿**，作为效用值的补充或替代——设计、估计与 WTP 换算见《DCE 设计助手》skill。\n- **效果证据来源**：RCT、系统评价/Meta 分析；相对效果结合基线风险外推长期结局时，见《Markov 模型助手》skill。\n- 本算例：对照年效用 0.70、干预 0.78，持续 10 年（未贴现）。\n\n**代码**：\n\n```stata\n* Stata：QALY 与增量 QALY（效用 × 生存年数，未贴现）\nscalar u0 = 0.70       // 对照年效用\nscalar u1 = 0.78       // 干预年效用\nscalar years = 10\ndisplay \"对照 QALY = \" u0 * years          // 7.0\ndisplay \"干预 QALY = \" u1 * years          // 7.8\ndisplay \"ΔQALY     = \" (u1 - u0) * years   // 0.8\n```\n\n```r\n# R：QALY 与增量 QALY（未贴现）\nu0 <- 0.70; u1 <- 0.78; years <- 10\nQ0 <- u0 * years        # 7.0\nQ1 <- u1 * years        # 7.8\ndQ <- Q1 - Q0           # 0.8 QALY\nlist(QALY_control = Q0, QALY_treat = Q1, dQALY = dQ)\n\n# R：DALY 简算示意：DALY = YLL + YLD\nYLL <- 10               # 早亡损失的生命年（示例）\nYLD <- 2.5              # 伤残状态生存年 × 伤残权重（示例）\nYLL + YLD               # 12.5 DALY\n\n# 提示：EQ-5D 状态（如 21121）须用已发表的中国积分体系系数换算效用值，\n# 勿自造系数；换算公式形如 U = 1 −（常数项 + 各维度离差项 + 交互/极端项）\n```\n\n---\n\n### 第 5 步：贴现\n\n**方法**：\n\n- **成本与效果用同一贴现率**贴现到基年：PV = C_t / (1 + r)^t（t 为相对基年的年数）。理论上有主张对健康效果用更低贴现率的讨论，但实践与指南要求同一贴现率（可另作敏感性分析分别检验）。\n- **《中国药物经济学评价指南（2020）》**：贴现率建议 **5%**，敏感性分析取 **0–8%**。时间范围超过 1 年必须贴现；基年（如 2026 年）与价格年份须注明。\n- **重要提示**：若各年增量成本与增量效果恒定，分子分母同乘同一贴现因子，ICER 不随贴现率改变（本算例即如此）；当成本与效果**时间分布不同**（成本前置、收益后置的预防与筛查类干预）时，贴现率会显著改变 ICER——这正是必须做贴现率敏感性分析的原因。\n- 本算例：10 年、5% 的年金贴现因子 ≈ 7.7217；贴现后 ΔC = 4,000 × 7.7217 ≈ 30,886.9 元，ΔE = 0.08 × 7.7217 ≈ 0.6177 QALY。\n\n**代码**：\n\n```stata\n* Stata：贴现因子与 10 年年金贴现因子（r = 5%）\nscalar r = 0.05\nscalar df = 0\nforvalues t = 1/10 {\n    scalar df = df + 1 / (1 + r)^`t'\n}\ndisplay \"10 年、5% 的年金贴现因子 = \" %9.4f df      // 7.7217\n\n* 贴现后的增量成本与增量效果\nscalar dC = (12000 - 8000) * df\nscalar dE = (0.78 - 0.70) * df\ndisplay \"贴现后 ΔC = \" %9.2f dC                      // 30886.93 元\ndisplay \"贴现后 ΔE = \" %9.4f dE                      // 0.6177 QALY\n\n* 贴现率敏感性（0–8%）\nforeach rr in 0 0.03 0.05 0.08 {\n    scalar df = 0\n    forvalues t = 1/10 {\n        scalar df = df + 1 / (1 + `rr')^`t'\n    }\n    display \"r = \" `rr' \"：10 年年金贴现因子 = \" %9.4f df\n}\n```\n\n```r\n# R：贴现与贴现率敏感性\nr <- 0.05; t <- 1:10\ndisc <- 1 / (1 + r)^t\nsum(disc)                                   # 7.7217（10 年、5% 年金贴现因子）\ndC <- (12000 - 8000) * sum(disc)            # 30886.9（贴现后增量成本，元）\ndE <- (0.78 - 0.70) * sum(disc)             # 0.6177（贴现后增量 QALY）\n\n# 贴现率敏感性（0–8%）\nsapply(c(0, 0.03, 0.05, 0.08), function(rr) sum(1 / (1 + rr)^(1:10)))\n# 本算例年增量恒定 → 贴现率不改变 ICER（= 4000 / 0.08 = 50000）；\n# 成本与效果时间分布不同时，ICER 会随贴现率变化（见第 7 步）\n```\n\n---\n\n### 第 6 步：增量分析与 ICER\n\n**方法**：\n\n- **只报增量，不报平均**：ICER = ΔC / ΔE = (C1 − C0) / (E1 − E0)。平均成本-效果比（ACER）会误导决策，禁止作为主要结论。\n- **成本-效果平面四象限解读**：\n  - 东北（ΔC > 0，ΔE > 0）：更贵更有效 → 与阈值 λ 比较，ICER < λ 则有成本-效果；\n  - 东南（ΔC < 0，ΔE > 0）：更便宜更有效 → **优势（dominant）**，直接推荐；\n  - 西北（ΔC > 0，ΔE < 0）：更贵更差 → **劣势（dominated）**，直接拒绝；\n  - 西南（ΔC < 0，ΔE < 0）：更便宜更差 → 权衡：节省的成本是否值得损失的效果（用 ICER 或净收益判断）。\n- **与阈值比较**：支付意愿阈值 λ 以 **1–3 倍人均 GDP 为参考区间**（国内常用口径；《中国药物经济学评价指南（2020）》以 1 倍人均 GDP 为主要参考，应用时以最新版指南与决策机构口径为准，并对 λ 做敏感性分析）。本算例取人均 GDP = 100,000 元（示例，以官方公布为准），λ = 100,000–300,000 元/QALY。\n- 本算例：ICER = 30,886.9 / 0.6177 ≈ **50,003 元/QALY** < 100,000 → 具有成本-效果。净货币收益 NB(λ) = λ·ΔE − ΔC = 100,000 × 0.6177 − 30,886.9 ≈ 30,887 元 > 0，结论一致。\n\n**代码**：\n\n```stata\n* Stata：ICER、净收益与四象限判断\nscalar dC = 30886.9\nscalar dE = 0.6177\nscalar lambda = 100000\ndisplay \"ICER = \" %9.1f dC / dE                    // ≈ 50003 元/QALY\ndisplay \"NB(λ=100000) = \" %9.1f lambda * dE - dC   // ≈ 30887 元\n\nif dC > 0 & dE > 0 {\n    display \"东北象限：更贵更有效 → 比较 ICER 与 λ\"\n    display cond(dC/dE < lambda, \"ICER < λ → 具有成本-效果\", \"ICER ≥ λ → 不具有成本-效果\")\n}\nelse if dC < 0 & dE > 0 {\n    display \"东南象限：更便宜更有效 → 优势策略，直接推荐\"\n}\nelse if dC > 0 & dE < 0 {\n    display \"西北象限：更贵更差 → 劣势策略，拒绝\"\n}\nelse {\n    display \"西南象限：更便宜更差 → 权衡节省与效果损失\"\n}\n```\n\n```r\n# R：ICER、净收益与四象限判断\ndC <- 30886.9; dE <- 0.6177\nlambda <- 100000\nICER <- dC / dE                                    # ≈ 50003 元/QALY\nNB   <- lambda * dE - dC                            # ≈ 30887 元（净货币收益）\nquadrant <- if (dC > 0 && dE > 0) \"NE：更贵更有效（与 λ 比较）\" else\n            if (dC < 0 && dE > 0) \"SE：更便宜更有效（优势）\" else\n            if (dC > 0 && dE < 0) \"NW：更贵更差（劣势）\" else\n            \"SW：更便宜更差（权衡）\"\nverdict <- ifelse(ICER < lambda, \"ICER < λ，具有成本-效果\", \"ICER ≥ λ，不具有成本-效果\")\nlist(ICER = ICER, NB = NB, quadrant = quadrant, verdict = verdict)\n```\n\n---\n\n### 第 7 步：不确定性分析\n\n**方法**：\n\n- **单因素敏感性分析（one-way / tornado）**：每次只变动一个参数（如 ±20%、极值、95% CI 上下界），其余固定，按对 ICER 的影响大小排序，画 tornado 图；影响最大的参数是结论稳健性的关键。\n- **多因素 / 场景分析**：同时变动多个参数的极端组合（如最优情形、最差情形）。\n- **概率敏感性分析（PSA）**：所有参数同时从各自分布抽样——成本常取 gamma / lognormal（保证非负），概率取 beta，相对风险取 lognormal，效用取 beta——蒙特卡洛迭代 ≥ 1000 次，输出成本-效果平面散点图与成本-效果可接受曲线（CEAC）。从参数层面抽样的完整实现（含 Markov 状态转移与参数相关结构）见《Markov 模型助手》skill；本步骤 R 代码给「直接从 ΔC、ΔE 分布抽样」的示意版本。\n- 本算例：ΔC ~ N(30,886.9, 3,200²)、ΔE ~ N(0.6177, 0.05²)（示意；正式研究应从参数层面抽样）。tornado 参数：ΔE ±20%、ΔC ±20%、干预年效用 0.73/0.83。\n\n**代码**：\n\n```stata\n* Stata：单因素敏感性分析（基线 ICER ≈ 50003 元/QALY）\nscalar dC = 30886.9\nscalar dE = 0.6177\ndisplay \"基线 ICER            = \" %9.1f dC / dE\ndisplay \"ΔE +20% 的 ICER      = \" %9.1f dC / (dE * 1.2)\ndisplay \"ΔE −20% 的 ICER      = \" %9.1f dC / (dE * 0.8)\ndisplay \"ΔC +20% 的 ICER      = \" %9.1f (dC * 1.2) / dE\ndisplay \"ΔC −20% 的 ICER      = \" %9.1f (dC * 0.8) / dE\n\n* 干预组年效用取 0.73 / 0.83（其余不变；贴现后 ΔE = Δu × 7.7217）\ndisplay \"干预效用 0.73 → ICER = \" %9.1f dC / (0.03 * 7.7217)\ndisplay \"干预效用 0.83 → ICER = \" %9.1f dC / (0.13 * 7.7217)\n* 解读：ICER 范围约 30769–133333 元/QALY，跨过 λ = 100000 →\n* 结论对效用参数敏感，报告中须突出说明\n```\n\n```r\n# R：概率敏感性分析（PSA）示意——直接从 ΔC、ΔE 的分布抽样；\n# 正式研究应从参数层面抽样（成本 gamma、概率 beta、RR lognormal），\n# 完整实现见《Markov 模型助手》skill。\nset.seed(2026)\nn <- 1000\ndC <- rnorm(n, mean = 30886.9, sd = 3200)     # 增量成本（元）\ndE <- rnorm(n, mean = 0.6177, sd = 0.05)      # 增量 QALY\n\n# 成本-效果平面散点图（λ = 100000 元/QALY 的斜率线）\nplot(dE, dC, xlab = \"ΔE（QALY）\", ylab = \"ΔC（元）\", pch = 20,\n     cex = 0.5, col = rgb(0, 0, 0, 0.3), main = \"PSA 成本-效果平面散点图\")\nabline(h = 0, v = 0, lty = 2)\nabline(a = 0, b = 100000, col = \"red\")        # 斜率 = λ\n\n# 各象限模拟点占比\nquad <- ifelse(dE > 0, ifelse(dC > 0, \"NE\", \"SE\"),\n               ifelse(dC > 0, \"NW\", \"SW\"))\ntable(quad) / n\n\n# 成本-效果可接受曲线（CEAC）：P(NMB(λ) > 0)\nlam  <- seq(0, 300000, by = 5000)\nceac <- sapply(lam, function(x) mean(x * dE - dC > 0))\nplot(lam, ceac, type = \"l\", xlab = \"支付意愿阈值 λ（元/QALY）\",\n     ylab = \"具有成本-效果的概率\", ylim = c(0, 1))\nabline(h = 0.5, lty = 3)\n```\n\n---\n\n### 第 8 步：报告规范（CHEERS 2022）\n\n**方法**：报告应逐条对照 **CHEERS 2022 清单（28 条）**。核心条目按报告结构对照如下（节选，完整清单以 CHEERS 2022 官方文件为准）：\n\n| 报告部分 | CHEERS 2022 核心条目 | 要点 |\n| --- | --- | --- |\n| 标题与摘要 | 1 标题、2 摘要 | 标题注明评价类型（成本-效果/成本-效用分析）；摘要含视角、时间范围、贴现率、ΔC、ΔE、ICER 与结论 |\n| 方法 | 3–4 背景目的与分析计划；5–8 人群、背景地点、对照、视角；9–10 时间范围、贴现率；11–13 结局选择、测量与估值；14–15 资源与成本、货币与价格日期；16–17 模型与分析假设；20 不确定性 | 逐项交代：PICO、视角与理由、时间范围与理由、贴现率与敏感性范围、QALY 及效用值来源（如 EQ-5D 中国积分体系版本）、成本条目与单位成本来源年份、模型结构、单因素/场景/PSA 计划与参数分布 |\n| 结果 | 23 主要结果、24 不确定性影响 | 成本表、效果表、ICER、tornado 图、PSA 散点图与 CEAC；说明不确定性是否改变结论 |\n| 讨论 | 25–28 利益相关者影响、发现与局限、资助、利益冲突 | 与现有证据比较；异质性与分布效应；外推假设局限；资助与利益冲突声明 |\n\n**代码**（把主要结果导出到结果表，供报告引用）：\n\n```stata\n* Stata：把主要结果写入 Excel（putexcel，Stata 14+）\nputexcel set results.xlsx, replace\nputexcel A1 = \"指标\" B1 = \"数值\"\nputexcel A2 = \"贴现后 ΔC（元）\"        B2 = 30886.9\nputexcel A3 = \"贴现后 ΔE（QALY）\"      B3 = 0.6177\nputexcel A4 = \"ICER（元/QALY）\"        B4 = 50003\nputexcel A5 = \"阈值 λ（元/QALY）\"      B5 = 100000\nputexcel A6 = \"净收益 NB（元）\"        B6 = 30887\n```\n\n```r\n# R：导出主要结果表\nres <- data.frame(\n  指标 = c(\"贴现后 ΔC（元）\", \"贴现后 ΔE（QALY）\", \"ICER（元/QALY）\",\n           \"阈值 λ（元/QALY）\", \"净收益 NB（元）\"),\n  数值 = c(30886.9, 0.6177, 50003, 100000, 30887))\nres\nwrite.csv(res, \"results.csv\", row.names = FALSE)\n```\n\n---\n\n### 第 9 步：研究工具箱指引（本仓库配套 Skill）\n\n**方法**：先按本总纲走完全流程，再按需调用同一网站的四个配套 Skill（文件名以仓库 `skills/` 目录为准）：\n\n| 配套 Skill | 定位 | 什么时候用它 |\n| --- | --- | --- |\n| DCE 设计助手 | 离散选择实验设计与分析（正交/D-效率设计、clogit/mixlogit/潜类别、WTP） | 需要用陈述性偏好测量偏好权重、效用值或支付意愿（作为 EQ-5D 的补充或替代）时 |\n| Markov 模型助手 | 状态转移模型（队列/微模拟）与完整概率敏感性分析 | 慢性病动态建模、外推长期成本效果、实现参数层面的完整 PSA/CEAC 时 |\n| 机器学习 | 费用预测、风险分层、成本数据挖掘 | 用大样本真实世界数据预测费用、识别高费用人群时 |\n| DID 双重差分 | 政策/自然实验的因果效应估计 | 用面板数据评估医保目录调整、价格改革等政策的因果效果时 |\n\n**代码**：\n\n```r\n# R：本仓库配套 Skill 一览（可复制到 README / 网页）\ntoolbox <- data.frame(\n  Skill = c(\"DCE 设计助手\", \"Markov 模型助手\", \"机器学习\", \"DID 双重差分\"),\n  什么时候用 = c(\n    \"测量偏好、效用值与支付意愿（EQ-5D 的补充或替代）\",\n    \"慢性病动态建模、时间外推与完整 PSA/CEAC\",\n    \"真实世界大数据下的费用预测与风险分层\",\n    \"用面板数据评估医保与支付改革政策的因果效果\"))\ntoolbox\n```\n\n---\n\n## 常见错误清单\n\n- [ ] 成本与效果使用**不同的贴现率**（必须同一贴现率，可另做敏感性分析分别检验）\n- [ ] 用**平均成本-效果比（ACER）**代替增量比（ICER）\n- [ ] 把**统计显著当作经济显著**（P < 0.05 ≠ ICER < λ）\n- [ ] **忘记报告不确定性**（无单因素敏感性、无 PSA，直接下结论）\n- [ ] **阈值误用**（套用别国或过期的阈值；把 1–3 倍人均 GDP 当绝对标准而不对 λ 做敏感性分析）\n- [ ] 时间范围过短，漏掉后期成本/效果（预防、筛查类干预尤甚）\n- [ ] 成本与效果**基年 / 价格年份不一致**，或未按 CPI 调整\n- [ ] 效用值套用**外国人群的积分体系**直接用于中国人群（EQ-5D 应使用中国积分体系并注明版本）\n- [ ] 用中间结局（血压、生化指标）代替最终结局（死亡、QALY）\n- [ ] 未说明研究视角，却混用不同视角下的成本条目\n- [ ] 成本数据明显右偏仍只用正态近似，不报告 bootstrap CI\n- [ ] PSA 参数分布选择无依据（如成本用对称正态导致抽样出现负成本）\n- [ ] 未按 CHEERS 2022 逐条自查，方法细节缺失导致不可复现\n\n## 输出格式（研究方案 / 报告模板）\n\n```\n【研究方案模板】\n1. 研究问题（PICO-T）与评价类型（CEA/CUA/CMA/CBA 及选择理由）\n2. 研究视角（社会/卫生体系/医保支付方）与时间范围（含理由）\n3. 成本：条目清单（识别→测量→估值）、单位成本来源与年份、间接成本方法（HCA/FCM）\n4. 效果/效用：结局指标、数据来源、效用积分体系（如 EQ-5D 中国积分体系及版本）\n5. 贴现：贴现率（指南 2020：5%，敏感性 0–8%）与基年\n6. 分析计划：ICER、阈值 λ 与依据、敏感性分析（单因素/场景/PSA 与参数分布）\n7. 自查：按 CHEERS 2022 清单逐条对照\n\n【报告模板】\n标题（注明评价类型，如「……的成本-效用分析」）\n摘要（视角、时间范围、贴现率、ΔC、ΔE、ICER、结论）\n1 背景与目的 2 方法（视角、人群、时间范围、成本、效果、贴现、分析计划）\n3 结果（成本表、效果表、ICER、tornado 图、PSA 散点图与 CEAC）\n4 讨论（与文献比较、阈值与不确定性、异质性与公平性、局限）\n5 结论（附：CHEERS 2022 对照自查表）\n```\n\n## 使用提示\n\n- 命令与选项随版本更新，运行 `help bootstrap` / `help display` / `help putexcel` 核对。\n- 流程建议：先定 PICO 与研究视角，再动手测算；每一步记录参数取值、来源与年份，方便复现与修改。\n- 本教程的算例数字为教学演示（数值自洽）；正式研究请替换为真实数据，并对全部关键参数做敏感性分析。\n- 本 Skill 是总纲：偏好与效用测量用《DCE 设计助手》，动态建模与完整 PSA 用《Markov 模型助手》，费用预测与风险分层用《机器学习》，政策因果评估用《DID 双重差分》。\n- 研究背景、参数取舍与最终结论由研究者负责；本 Skill 不替代教科书、指南与期刊审稿要求。\n\n---\n\n## 引用 / Citation\n\n中文：杜思娴. 卫生经济学评价教程 Skill [EB/OL]. (2026). https://naturedu.github.io/socialmedicine/.\n\nEnglish: Du, S. (2026). *Health economics evaluation tutorial* [Skill definition]. https://naturedu.github.io/socialmedicine/.\n","kind":"方法 Skill","locator":"方法手册","license":"MIT（仓库原有技能）","verified":"原有公开仓库"}},{"id":"studio-library:skill-its-skill","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Skill：间断时间序列助手（ITS 政策效果评估，Stata / R）","summary":"# Skill：间断时间序列助手（ITS 政策效果评估，Stata / R）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n一份可复用的「间断时间序列分析（Interrupted Time Series, ITS）」工作流：用于评估**单一地区/机构在某个明确时点实施的政策**（如医保支付方式改革、药品集中带量采购落地、抗菌药物专项整治「限抗令」等）对结局时间序列的**即时水平变化与趋势变化**。内容覆盖研究设计与数据要求、分段回归模型、自相关处理、Stata（`newey` / `itsa`）与 R（`lm` + `NeweyWest`）的估计对照、拟合与反事实作图、稳健性检验（安慰剂时点、季节调整、控制组 ITS、异常值敏感性）与报告清单。**每一步都给出「方法 + 代码」**，可直接作为提示词喂给大语言模型，也可作为操作手册照做。\n\n**贯穿全篇的示例场景**：「某院 DRG 付费改革（第 25 个月起实施）对次均住院费用（月度序列）的影响」。第 1 步给出 Stata 与 R 两套完全可运行的模拟数据代码，数据为 48 个月 = 干预前 24 + 干预后 24，内置真实干预效应：**即时水平变化 β2 = −600 元、斜率变化 β3 = −30 元/月**（干预前趋势 β1 = −50 元/月、干预时点基线 β0 = 12000 元、AR(1) 误差 ρ = 0","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-its-skill","title":"Skill：间断时间序列助手（ITS 政策效果评估，Stata / R）","journal":"方法 Skill","year":"","url":"skills/its-skill.md","text":"# Skill：间断时间序列助手（ITS 政策效果评估，Stata / R）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n一份可复用的「间断时间序列分析（Interrupted Time Series, ITS）」工作流：用于评估**单一地区/机构在某个明确时点实施的政策**（如医保支付方式改革、药品集中带量采购落地、抗菌药物专项整治「限抗令」等）对结局时间序列的**即时水平变化与趋势变化**。内容覆盖研究设计与数据要求、分段回归模型、自相关处理、Stata（`newey` / `itsa`）与 R（`lm` + `NeweyWest`）的估计对照、拟合与反事实作图、稳健性检验（安慰剂时点、季节调整、控制组 ITS、异常值敏感性）与报告清单。**每一步都给出「方法 + 代码」**，可直接作为提示词喂给大语言模型，也可作为操作手册照做。\n\n**贯穿全篇的示例场景**：「某院 DRG 付费改革（第 25 个月起实施）对次均住院费用（月度序列）的影响」。第 1 步给出 Stata 与 R 两套完全可运行的模拟数据代码，数据为 48 个月 = 干预前 24 + 干预后 24，内置真实干预效应：**即时水平变化 β2 = −600 元、斜率变化 β3 = −30 元/月**（干预前趋势 β1 = −50 元/月、干预时点基线 β0 = 12000 元、AR(1) 误差 ρ = 0.3）。后续各步全部沿用这份数据。\n\n## 适用范围（明确边界）\n\n允许：\n- 评估单一地区/机构在**明确时点**实施的政策对连续结局时间序列的效应（卫生政策评估、药物利用研究、医院管理、医保改革等）\n- 单组 ITS 与对照（双组）ITS（CITS）的分段回归设定、估计与作图\n- 自相关诊断（ACF / Durbin-Watson / Cumby-Huizinga / Breusch-Godfrey）与 Newey-West（HAC）标准误修正\n- 安慰剂时点、季节调整、异常值敏感性等稳健性检验\n\n**不允许：**\n- 在干预**无明确时点**（政策渐进落地、长期渐变）时硬性切割时间轴冒充 ITS\n- 把单组 ITS 的关联结论直接当成强因果结论（无对照无法排除同期事件）\n- 本 Skill 聚焦**连续结局的线性 ITS**；计数结局（如月感染例数）需 Poisson / 负二项 ITS，方法同理、链接函数不同，需另查对应文献\n- 承诺结果一定可发表；不替代计量经济学教科书与期刊审稿要求（如要求 CITS、检验力分析等）\n\n## 软件准备\n\n方法：Stata 17 及以上、R ≥ 4.2。Stata 侧 `newey` 为官方命令；`actest`、`itsa`、`estout` 来自 SSC；R 侧 `lmtest`（`coeftest`、`dwtest`、`bgtest`）、`sandwich`（`NeweyWest`）、`car`（`linearHypothesis`、`deltaMethod`）为核心包，`ggplot2`、`texreg` 为作图与出表备选。\n\n```stata\n* Stata（联网安装一次）\nssc install actest, replace\nssc install itsa,   replace\nssc install estout, replace\n```\n\n```r\n# R\ninstall.packages(c(\"lmtest\", \"sandwich\", \"car\", \"ggplot2\", \"texreg\"))\n```\n\n## 工作流程总览（7 步）\n\n1. 适用条件与研究设计（含数据要求与模拟数据）→ 2. 分段回归模型 → 3. 自相关处理 → 4. 模型估计（Stata `newey`/`itsa` 与 R `lm`+`NeweyWest` 对照）→ 5. 图形呈现（拟合 + 反事实）→ 6. 稳健性与检验 → 7. 报告清单。以下每一步均为「方法 + 代码」。\n\n---\n\n### 第 1 步：适用条件与研究设计（含数据要求与模拟数据）\n\n**方法**：\n\n- **何时用 ITS**：① 干预在**明确的单一（或少数）时点**实施；② 干预前后各有**连续、等间隔**的时间序列（月度/季度/周度）；③ 结局在干预前已有可测的基线趋势；④ 有**足够长的序列**：经验上干预前后**每段至少 8–12 个观测点**（Wagner 等建议每段 ≥ 8 点才能可靠估计趋势；24 个点/段更稳）；⑤ 干预时点可精确对应序列中的某一期（本例：第 25 个月）。\n- **数据要求**：结局变量（次均费用，元）、时间变量（月）、干预指示变量；序列无重大缺失（缺失期需插补并说明）；异常值记录在案。\n- **分段回归思想**：把序列按干预时点切成两段，用一条带「断点」的回归同时估计——干预前的水平与趋势（β0、β1）、干预时的**即时水平跳变**（β2）与干预后的**趋势改变**（β3）。反事实（无干预会发生什么）由干预前趋势外推得到。\n- **识别前提**：单组 ITS 的主要威胁是**同期混杂**（同一时点发生的其他政策/事件），故第 6 步必做安慰剂检验；有对照地区/机构时应升级为 CITS。\n\n**代码**（贯穿全篇的模拟数据；Stata 与 R 各一份，参数相同）：\n\n```stata\n* =====================================================================\n* 模拟数据：48 个月 = 干预前 24（第 1–24 月）+ 干预后 24（第 25–48 月）\n* 场景：某院 DRG 付费改革（第 25 个月实施）对次均住院费用（元）的影响\n* 内置真实干预效应：\n*   β0 = 12000（干预时点基线，元）  β1 = -50（干预前趋势，元/月）\n*   β2 = -600 （即时水平变化，元）  β3 = -30（斜率变化，元/月）\n* 误差：AR(1)，rho = 0.3，扰动标准差 200 元\n* =====================================================================\nclear all\nset seed 2026\nset obs 48\ngen time        = _n\ngen time_c      = time - 25               // 中心化：干预时点 = 0\ngen level       = time >= 25              // 干预指示：第 25 个月起 = 1\ngen trend_after = time_c * level          // 干预后斜率增量（干预前恒为 0）\n\n* AR(1) 误差\ngen e = rnormal(0, 200)\nforvalues t = 2/48 {\n    replace e = 0.3 * e[_n-1] + rnormal(0, 200) if time == `t'\n}\n\n* 结局：次均住院费用（元）\ngen y = 12000 - 50*time_c - 600*level - 30*trend_after + e\n\ntsset time\ntwoway (line y time), xline(24.5, lpattern(dash)) ///\n    title(\"模拟月度序列：次均住院费用\") ytitle(\"元\") xtitle(\"月份\")\n```\n\n```r\n# =====================================================================\n# 模拟数据（与 Stata 版同参数；随机数实现不同，不影响示例）\n# =====================================================================\nset.seed(2026)\nn           <- 48\ntime        <- 1:n\ntime_c      <- time - 25                 # 中心化：干预时点 = 0\nlevel       <- as.numeric(time >= 25)    # 干预指示：第 25 个月起 = 1\ntrend_after <- time_c * level            # 干预后斜率增量\n\n# AR(1) 误差：rho = 0.3，扰动 sd = 200\ne <- as.numeric(arima.sim(model = list(ar = 0.3), n = n, sd = 200))\n\n# 结局：次均住院费用（元），内置真实效应\ny <- 12000 - 50*time_c - 600*level - 30*trend_after + e\n\ndat <- data.frame(time, time_c, level, trend_after, y)\nplot(y ~ time, data = dat, type = \"o\", pch = 16, cex = 0.7,\n     main = \"模拟月度序列：次均住院费用\", xlab = \"月份\", ylab = \"元\")\nabline(v = 24.5, lty = 2, col = \"gray50\")\n```\n\n---\n\n### 第 2 步：分段回归模型\n\n**方法**：\n\n分段回归（segmented regression）模型：\n\nY_t = β0 + β1·time_c + β2·level + β3·trend_after + ε_t\n\n- **β0**：干预时点处（time_c = 0）的反事实基线——若无干预，干预时点的期望结局水平；\n- **β1**：**干预前斜率**（干预前每月平均变化，元/月）；\n- **β2**：**即时水平变化**（level change）——干预时点的一次性跃变（元）；\n- **β3**：**斜率变化**（trend change）——干预后斜率与干预前斜率之差（元/月）；\n- 干预后总斜率 = β1 + β3；干预后第 k 个月相对反事实的总效应 = β2 + k·β3（本例期末第 48 个月 k = 23 → 真实总效应 = −600 + 23×(−30) = −1290 元）。\n\n**中心化的写法与意义**：`time_c = time − 25`，使干预时点对应 0。此时 β2 直接就是「干预时点的即时效应」。若用未中心化的 time 与 `time × level` 交互项，β2 变成「time = 0 处的跃变」，真实即时效应要算成 β2 + t0·β3，且 level 与趋势项共线性更强。中心化既便于解读，也降低共线性。\n\n**代码**（先跑普通 OLS 预览系数方向；标准误问题在第 3 步处理）：\n\n```stata\n* 中心化版本（推荐）：β2 直接 = 干预时点即时效应\nregress y time_c level trend_after\n* 预计：_b[level] ≈ -600、_b[trend_after] ≈ -30（样本有噪声）\n\n* 对照：未中心化版本（time 与 time*level 交互）\ngen time_level = time * level\nregress y time level time_level\n* 此时 level 系数不再等于干预时点效应（是 time=0 处的跃变），须用组合计算\n```\n\n```r\n# 中心化版本（推荐）\nm0 <- lm(y ~ time_c + level + trend_after, data = dat)\nsummary(m0)\n# 预计：level ≈ -600、trend_after ≈ -30（样本有噪声）\n\n# 组合效应（第 3 步换 HAC 协方差后正式报告）\nlibrary(car)\nlinearHypothesis(m0, \"time_c + trend_after = 0\")   # 干预后总斜率（β1+β3）\ndeltaMethod(m0, \"level + 23*trend_after\")          # 期末（time_c=23）总效应\n```\n\n---\n\n### 第 3 步：自相关处理\n\n**方法**：\n\n- 月度结局几乎总存在**残差自相关**（相邻月高度相关）。OLS 系数仍无偏，但**标准误有偏**，t/p 值不可信，必须处理。\n- **诊断**：残差时序图 + ACF 图；一阶自相关用 Durbin-Watson（≈2 表示无一阶自相关；模型含滞后因变量时 DW 失效）；一般/高阶自相关用 Cumby-Huizinga（Stata `actest`）或 Breusch-Godfrey LM（R `bgtest`）。\n- **处理方式**（三选一或组合，主流是 ①）：\n  ① **Newey-West / HAC 标准误**：只修正推断，不改系数（推荐首选）；\n  ② **AR(1) 建模**：Prais-Winsten（Stata `prais`）/ R `gls + corAR1`，显式建模误差结构；\n  ③ 滞后因变量（`itsa` 的 `lag(#)`）：属动态模型，系数解释随之改变，慎用。\n- **滞后阶数选择**：太少 → 残留自相关；太多 → 效率损失。Stata `newey` 默认 m = floor(4(T/100)^(2/9))（T = 48 → 3）；小样本常取 lag(1)–lag(3)，报告阶数并做敏感性。R `NeweyWest()` 默认自动带宽（`prewhite = TRUE`）；与 Stata `lag(1)` 对照时用 `lag = 1, prewhite = FALSE`。\n- 先确认序列平稳：`dfuller y, trend`（ADF 检验）；存在单位根时需差分或 ARIMA-ITS，超出线性 ITS 框架。\n\n**代码**：\n\n```stata\n* ① 残差诊断\nregress y time_c level trend_after\npredict r, residuals\ntsline r, yline(0) title(\"残差时序图\")\nac r, lags(12) title(\"残差 ACF\")\n\nestat dwatson                    // Durbin-Watson（一阶）\nactest, lags(4)                  // Cumby-Huizinga 一般检验（SSC；选项以 help actest 为准）\nwntestq r, lags(4)               // Ljung-Box Q 检验\n\n* 平稳性检查（结局本身）\ndfuller y, trend\n\n* ② Newey-West / HAC 标准误\nnewey y time_c level trend_after, lag(1)\n* 默认滞后 = floor(4*(T/100)^(2/9))（T=48 → 3）；建议对 lag(1)–lag(4) 做敏感性\n\n* ③ 备选：Prais-Winsten AR(1)（选项以 help prais 为准）\nprais y time_c level trend_after, rhotype(tscorr)\n```\n\n```r\n# ① 残差诊断\nm0 <- lm(y ~ time_c + level + trend_after, data = dat)\nplot(resid(m0), type = \"o\", pch = 16, cex = 0.7,\n     xlab = \"月份\", ylab = \"残差\"); abline(h = 0)\nacf(resid(m0), lag.max = 12, main = \"残差 ACF\")\n\nlibrary(lmtest)\ndwtest(m0)                    # Durbin-Watson（一阶）\nbgtest(m0, order = 4)         # Breusch-Godfrey LM（高阶）\n\n# ② Newey-West / HAC 标准误\nlibrary(sandwich)\nsummary(m0)                                                    # 普通 SE（参考）\ncoeftest(m0, vcov = NeweyWest(m0, lag = 1, prewhite = FALSE))  # HAC SE，lag 固定为 1\ncoeftest(m0, vcov = NeweyWest(m0))                             # 默认：自动带宽 + prewhite\n# 注：NeweyWest 默认 prewhite = TRUE 且自动选带宽；与 Stata lag(1) 对照时\n#     用 lag = 1, prewhite = FALSE（以 help(NeweyWest) 为准）\n\n# ③ 备选：AR(1) 误差模型（nlme）\nlibrary(nlme)\ng1 <- gls(y ~ time_c + level + trend_after, data = dat,\n          correlation = corAR1(form = ~ time))\nsummary(g1)\n```\n\n---\n\n### 第 4 步：模型估计：Stata 与 R 实现（对照）\n\n**方法**：\n\n以下三个实现估计的是**同一个分段回归**，结果应几乎一致（本例应得到 β2 ≈ −600、β3 ≈ −30，真实参数见第 1 步）：\n\n| 实现 | 命令 | 标准误 |\n|---|---|---|\n| Stata 手动 | `newey y time_c level trend_after, lag(1)` | Newey-West（用户指定滞后） |\n| Stata `itsa` | `itsa y, single treatperiod(25) posttrend` | OLS + Newey-West（默认） |\n| R | `lm` + `coeftest(NeweyWest(...))` | HAC（可指定滞后） |\n\n**注意**：`itsa` 的 `lag(#)` 是**滞后因变量**阶数（动态模型），与 `newey` 的 HAC `lag(#)` 含义不同；要得到与手动分段回归相同的模型，`itsa` 用默认 `lag(0)` + `posttrend`。`itsa` 会自动生成时间/水平/趋势变量（命名形如 `_t`、`_x_t25` 等，运行后 `browse` 可查看）；`prais` 选项改用 Prais-Winsten；`figure` 选项直接出含反事实的图。**语法与输出以 `help itsa` 为准。**\n\n**代码**：\n\n```stata\n* ============ 4.1 手动分段回归 + Newey-West ============\nnewey y time_c level trend_after, lag(1) force\n* 注：任务书简写 newey y time level trend, lag(1) force；此处 time 为干预时点\n*     中心化的 time_c；force 允许未 tsset 的数据仍按时间序列估计（已 tsset 可省略）\nestimates store its_manual\n\n* 组合效应（HAC 标准误下的线性组合检验）\nlincom time_c + trend_after              // 干预后总斜率（β1+β3）\nlincom level + 23*trend_after            // 期末（第 48 月）总效应（β2+23·β3）\n\n* ============ 4.2 itsa 命令（SSC，Ariel Linden）============\n* ssc install itsa, replace\nitsa y, single treatperiod(25) posttrend\n* 注：任务书示例写法 itsa y, single treat(25) 中的选项名，实际对应\n*     treatperiod(25)，以 help itsa 为准；itsa 默认 OLS + Newey-West 标准误。\n* 带滞后因变量的动态版本：itsa y, single treatperiod(25) lag(1) posttrend\n* 出图：itsa y, single treatperiod(25) posttrend figure\n```\n\n```r\n# ============ R：lm + NeweyWest，与 Stata 对照 ============\nm <- lm(y ~ time_c + level + trend_after, data = dat)\n\nsummary(m)                                                    # 普通 SE（参考）\ntab_hac <- coeftest(m, vcov = NeweyWest(m, lag = 1, prewhite = FALSE))\nprint(tab_hac)                                                # HAC SE\ncoefci(tab_hac)                                               # 基于 HAC 的 95% CI\n\n# 组合效应（HAC 协方差下的线性组合与 delta 方法）\nlinearHypothesis(m, \"time_c + trend_after = 0\",\n                 vcov. = NeweyWest(m, lag = 1, prewhite = FALSE))   # 干预后总斜率\ndeltaMethod(m, \"level + 23*trend_after\",\n            vcov. = NeweyWest(m, lag = 1, prewhite = FALSE))         # 期末总效应\n```\n\n---\n\n### 第 5 步：图形呈现（原始序列 + 拟合 + 反事实）\n\n**方法**：\n\n一张规范的 ITS 图含四要素：① 原始观测序列（散点/线）；② 分段拟合线（干预前后两段）；③ **反事实虚线**（无干预时干预前趋势的外推：ŷ_cf = β̂0 + β̂1·time_c）；④ 干预时点竖线。反事实线与拟合线之间的「缺口」是 β2 的视觉表达，两线斜率之差是 β3。\n\n**代码**：\n\n```stata\nnewey y time_c level trend_after, lag(1)\npredict yhat, xb\ngen yhat_cf = _b[_cons] + _b[time_c]*time_c       // 反事实：延续干预前趋势\n\ntwoway (scatter y time, msize(small) mcolor(gs8)) ///\n       (line yhat time, lcolor(navy) lwidth(medthick)) ///\n       (line yhat_cf time if time >= 25, lcolor(red) lpattern(dash) lwidth(medthick)) ///\n       , xline(24.5, lcolor(gs10) lpattern(dash)) ///\n         xtitle(\"月份\") ytitle(\"次均费用（元）\") ///\n         title(\"ITS：DRG 改革对次均住院费用的影响\") ///\n         legend(order(1 \"观测值\" 2 \"模型拟合\" 3 \"反事实（无干预）\") position(6) rows(1))\n```\n\n```r\n# R（基础包）\nm <- lm(y ~ time_c + level + trend_after, data = dat)\ndat$fit <- fitted(m)\nb <- coef(m)\ndat$cf  <- b[\"(Intercept)\"] + b[\"time_c\"] * dat$time_c     # 反事实\n\nplot(y ~ time, data = dat, type = \"o\", pch = 16, cex = 0.7, col = \"gray50\",\n     main = \"ITS：DRG 改革对次均住院费用的影响\",\n     xlab = \"月份\", ylab = \"次均费用（元）\")\nabline(v = 24.5, lty = 3, col = \"gray40\")\nlines(dat$time, dat$fit, col = \"navy\", lwd = 2)\nlines(dat$time[dat$time >= 25], dat$cf[dat$time >= 25], col = \"red\", lwd = 2, lty = 2)\nlegend(\"bottomleft\", legend = c(\"观测值\", \"模型拟合\", \"反事实（无干预）\"),\n       col = c(\"gray50\", \"navy\", \"red\"), lty = c(1, 1, 2),\n       lwd = c(1, 2, 2), bty = \"n\")\n\n# ggplot2 等价写法（ggplot2 ≥ 3.4 用 linewidth，低版本用 size）\nlibrary(ggplot2)\nggplot(dat, aes(time, y)) +\n  geom_point(colour = \"grey50\") +\n  geom_vline(xintercept = 24.5, linetype = 3) +\n  geom_line(aes(y = fit), colour = \"navy\", linewidth = 1) +\n  geom_line(data = subset(dat, time >= 25), aes(y = cf),\n            colour = \"red\", linetype = 2, linewidth = 1) +\n  labs(x = \"月份\", y = \"次均费用（元）\",\n       title = \"ITS：DRG 改革对次均住院费用的影响\") +\n  theme_minimal()\n```\n\n---\n\n### 第 6 步：稳健性与检验\n\n**方法**：\n\n- **6.1 安慰剂时点**（in-time placebo）：把干预时点**前移**到干预前某期（如第 13 个月，该处并无真实干预），重估模型。若此时 β2、β3 仍「显著」，说明断点效应可能来自模型设定而非政策；**期望结果：安慰剂时点的水平与斜率变化接近 0 且不显著**。\n- **6.2 季节性与混杂**：加入月度虚拟变量（或正弦/余弦 Fourier 对）吸收季节成分；同时把已知混杂事件作为对照时点做同样分析。核心系数稳定则结论稳健。\n- **6.3 控制组 ITS（CITS）**：有未受干预的对照地区/机构时，用「干预组 × 时点」交互项识别政策效应（DID 型设定），能控制同期共同冲击。Stata 用 `itsa` 双组语法（`treatid()`），或手动全交互模型；R 用 `lm` 交互项。\n- **6.4 异常值敏感性**：1%/99% 缩尾（或剔除极端月）后重估，检查系数稳定性。\n- 补充：滞后阶数敏感性（lag(1)–lag(4)）与不同自相关处理（HAC vs Prais-Winsten）的比较。\n\n**代码**：\n\n```stata\n* ---- 6.1 安慰剂时点：假设干预发生在第 13 个月（实际无干预）----\ngen time_c13 = time - 13\ngen level13  = time >= 13\ngen trend13  = time_c13 * level13\nnewey y time_c13 level13 trend13, lag(1)\n* 期望：level13、trend13 系数接近 0 且不显著\n\n* ---- 6.2 季节性：加入月度虚拟变量（参考月 = 1 月）----\ngen month = mod(time - 1, 12) + 1\ntab month, gen(md)                          // 生成 md1–md12\nnewey y time_c level trend_after md2-md12, lag(1)\n* 期望：β2、β3 与主模型接近（以实际结果为准）\n\n* ---- 6.3 控制组 ITS（CITS）：双地区面板示意 ----\n* 面板长表：id（地区）、time、y；treat = 1（干预地区，第 25 个月起受干预）\n* 多组 itsa（语法与选项以 help itsa 为准）：\n*     xtset id time\n*     itsa y, treatperiod(25) treatid(treat) posttrend\n* 手动 DID 型交互模型（示意）：\ngen t_c   = time - 25\ngen lev   = time >= 25\ngen tr_a  = t_c * lev\ngen g_lev = treat * lev        // 水平变化的组间差（政策即时效应，DID）\ngen g_tr  = treat * tr_a       // 斜率变化的组间差（政策趋势效应，DID）\nnewey y t_c treat lev tr_a g_lev g_tr, lag(1)\n* 关键系数：g_lev（相对对照组的即时水平变化）、g_tr（相对对照组的斜率变化）\n\n* ---- 6.4 异常值敏感性：1%/99% 缩尾 ----\nquietly summarize y, detail\ngen y_w = min(max(y, r(p1)), r(p99))\nnewey y_w time_c level trend_after, lag(1)\n```\n\n```r\n# ---- 6.1 安慰剂时点：假设干预发生在第 13 个月 ----\ndat$time_c13 <- dat$time - 13\ndat$level13  <- as.numeric(dat$time >= 13)\ndat$trend13  <- dat$time_c13 * dat$level13\nm_placebo <- lm(y ~ time_c13 + level13 + trend13, data = dat)\ncoeftest(m_placebo, vcov = NeweyWest(m_placebo, lag = 1, prewhite = FALSE))\n# 期望：level13、trend13 接近 0 且不显著\n\n# ---- 6.2 季节性：加入月度虚拟变量 ----\ndat$month <- factor(((dat$time - 1) %% 12) + 1)\nm_season <- lm(y ~ time_c + level + trend_after + month, data = dat)\ncoeftest(m_season, vcov = NeweyWest(m_season, lag = 1, prewhite = FALSE))\n# 更平滑的季节建模可用正弦/余弦对（Fourier terms），以数据周期为准\n\n# ---- 6.3 控制组 ITS（CITS）：面板数据交互项 ----\n# 面板长表 panel：id、time、y、treat（1 = 干预地区）\npanel$t_c  <- panel$time - 25\npanel$lev  <- as.numeric(panel$time >= 25)\npanel$tr_a <- panel$t_c * panel$lev\nm_cits <- lm(y ~ t_c + treat + lev + tr_a + treat:lev + treat:tr_a, data = panel)\ncoeftest(m_cits, vcov = NeweyWest(m_cits, lag = 1, prewhite = FALSE))\n# 政策效应 = treat:lev（即时）与 treat:tr_a（斜率）的系数\n\n# ---- 6.4 异常值敏感性：1%/99% 缩尾 ----\nq <- quantile(dat$y, c(0.01, 0.99))\ndat$y_w <- pmin(pmax(dat$y, q[1]), q[2])\nm_w <- lm(y_w ~ time_c + level + trend_after, data = dat)\ncoeftest(m_w, vcov = NeweyWest(m_w, lag = 1, prewhite = FALSE))\n```\n\n---\n\n### 第 7 步：报告清单\n\n**方法**：一篇 ITS 论文/报告至少报告以下七项——① 序列描述（时间区间、总点数、前后各段点数、结局定义与单位、均值/SD、缺失与异常值处理）；② 模型设定（公式、中心化说明）；③ 模型表（β0–β3 及 HAC 标准误、95% CI、p 值）；④ 自相关处理说明（检验结果与滞后阶数选择依据）；⑤ 图（原始序列 + 拟合 + 反事实 + 时点线）；⑥ 稳健性结果（安慰剂、季节、异常值、CITS 若有）；⑦ 局限（单组设计、同期事件、检验力）。\n\n**代码**（出表）：\n\n```stata\n* 主表：HAC 标准误（esttab，SSC estout）\nnewey y time_c level trend_after, lag(1)\nestimates store its_main\nesttab its_main, b(%9.2f) se(%9.2f) p(%9.3f) stats(N r2, fmt(%9.0f %9.3f)) ///\n    title(\"表 1：ITS 分段回归（Newey-West 标准误，lag(1)）\") ///\n    addnotes(\"干预时点：第 25 个月；time_c 为干预时点中心化的月份。\")\n```\n\n```r\n# 主表：用 HAC 标准误覆盖（texreg；以包文档为准）\nlibrary(texreg)\nhac_se <- sqrt(diag(NeweyWest(m, lag = 1, prewhite = FALSE)))\nscreenreg(m, override.se = hac_se,\n          custom.model.names = \"ITS 分段回归（HAC 标准误）\")\n```\n\n---\n\n## 常见错误清单\n\n- [ ] 忽略残差自相关，直接用普通 OLS 标准误 → SE/t/p 失真（务必 HAC 或 AR(1) 建模）\n- [ ] 干预前后数据点太少（每段 < 8–12 个）→ 趋势估计不稳、检验力不足\n- [ ] 把「即时水平变化（β2）」与「斜率变化（β3）」混为一谈（β3 是每月增量，不是一次性变化）\n- [ ] 未做安慰剂时点检验就宣称效应来自政策\n- [ ] 不报告 Newey-West 滞后阶数及选择依据（规则、敏感性）\n- [ ] 未检查/处理季节性与同期混杂（同期其他政策、疫情、报销政策变动）\n- [ ] 时间变量未中心化，却把 level 系数直接解读为「干预即时效应」（未中心化时该系数含义不同）\n- [ ] 混淆 `itsa` 的 `lag(#)`（滞后因变量，动态模型）与 `newey` 的 `lag(#)`（HAC 带宽）\n- [ ] 序列存在单位根（不平稳）仍直接回归，出现伪回归\n- [ ] 用拟合线做长期外推预测，把样本内效应当成未来趋势\n- [ ] 单组 ITS 无对照却做强因果断言，未讨论同期事件与历史威胁\n- [ ] 干预时点定义含糊（政策渐进落地却硬按某时点切割），或时点与政策实际生效日不符\n\n## 输出格式（ITS 报告模板）\n\n```\n【研究设计与数据】\n结局（次均住院费用，元/次）与时间单位（月）；序列区间（20XX 年 X 月–20XX 年 X 月，\n共 48 个月）；干预（DRG 付费改革）与实施时点（第 25 个月）；干预前后各 24 个月；\n数据来源、缺失/插补、异常值处理。\n模型：Y = β0 + β1·time_c + β2·level + β3·trend_after + ε；time_c 为干预时点中心化。\n\n【主要结果】\n- 序列描述：均值、SD、干预前趋势（β1）与干预时点基线（β0）。\n- 自相关诊断：残差 ACF、DW / actest（或 BG）结果 → 采用 Newey-West 修正的理由。\n- 表 1：ITS 分段回归（β0–β3，HAC 标准误、95% CI、p 值；注明滞后阶数及选择依据）。\n- 效应解读：β2 = 即时水平变化（元）；β3 = 斜率变化（元/月）；\n  干预后总斜率 = β1 + β3；期末总效应 = β2 + k·β3（注明 k）。\n- 图 1：原始序列 + 分段拟合 + 反事实虚线 + 干预时点竖线。\n\n【稳健性检验】\n- 表 2：安慰剂时点（第 X 个月）结果：β2、β3 应接近 0；\n- 季节调整（月度虚拟/Fourier）后核心系数变化；\n- 异常值敏感性（1%/99% 缩尾）；\n- CITS（如有对照）：treat:level 与 treat:trend_after 交互项系数。\n\n【结论与局限】\n- 政策含义：方向、大小、持续时间（如「DRG 改革使次均费用即时下降约 600 元，\n  且此后每月多降约 30 元」）。\n- 局限：单组设计无法排除同期事件；自相关结构与滞后选择；序列长度与检验力；\n  单一机构结果的外部效度。\n```\n\n## 使用提示\n\n- 命令与选项随版本更新：运行 `help newey`、`help itsa`、`help actest`、`help prais`、`help esttab` 核对；R 侧看 `help(NeweyWest)`、`help(coeftest)`、`help(deltaMethod)`。\n- `itsa` 的语法与输出**以 `help itsa` 为准**；任务书简写 `itsa y, single treat(25)` 对应实际选项 `treatperiod(25)`。\n- Newey-West 滞后：Stata `newey` 默认 m = floor(4(T/100)^(2/9))（T = 48 → 3）；小样本常用 lag(1)–lag(3)，**报告阶数并做敏感性分析**；R 的 `NeweyWest()` 默认自动带宽（prewhite = TRUE），与 Stata 对照时用 `lag = 1, prewhite = FALSE`。\n- 建议同时报告普通 SE 与 HAC SE，直观展示自相关的严重程度。\n- 动手顺序：先画序列图、确认干预时点与异常值 → 再定模型 → 诊断自相关 → 主分析 → 稳健性。\n- 本 Skill 提供方法与代码模板；干预时点判定、混杂判断与最终结论由研究者负责。\n\n---\n\n## 引用 / Citation\n\n中文：杜思娴. 间断时间序列助手：ITS 政策效果评估 Skill [EB/OL]. (2026). https://naturedu.github.io/socialmedicine/.\n\nEnglish: Du, S. (2026). *Interrupted Time Series Assistant: ITS for health policy evaluation skill* [Skill definition]. https://naturedu.github.io/socialmedicine/.\n\n建议阅读方法学文献：\n\n- Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. *International Journal of Epidemiology*, 46(1), 348–355.\n- Linden, A. (2015). Conducting interrupted time-series analysis for single- and multiple-group comparisons. *The Stata Journal*, 15(2), 480–500.\n- Wagner, A. K., Soumerai, S. B., Zhang, F., & Ross-Degnan, D. (2002). Segmented regression analysis of interrupted time series studies in medication use research. *Journal of Clinical Pharmacy and Therapeutics*, 27(4), 299–309.\n- Ramsay, C. R., Matowe, L., Grilli, R., Grimshaw, J. M., & Thomas, R. E. (2003). Interrupted time series designs in health technology assessment: lessons from two systematic reviews of behavior change strategies. *International Journal of Technology Assessment in Health Care*, 19(4), 613–623.\n","kind":"方法 Skill","locator":"方法手册","license":"MIT（仓库原有技能）","verified":"原有公开仓库"}},{"id":"studio-library:skill-machine-learning-skill","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Skill：机器学习助手（卫生经济与社会医学预测建模，Stata / R）","summary":"# Skill：机器学习助手（卫生经济与社会医学预测建模，Stata / R）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n一份可复用的「机器学习预测建模」工作流：面向社会医学与卫生经济学研究者，覆盖从预测目标定义、模拟数据生成、训练—验证—测试拆分、缺失值处理与标准化，到 LASSO / 弹性网、随机森林、XGBoost 的建模，AUC / 校准 / RMSE 评估与 DeLong 比较，SHAP 解释，直至 TRIPOD 报告与部署落地的全流程。典型场景：**住院费用预测、30 天再入院风险、慢病并发症风险、医保基金支出预测**。**每一步都给出「方法 + 代码」**，可直接作为提示词喂给大语言模型，也可作为操作手册照做。\n\n**定位边界（预测 ≠ 因果）**：本 Skill 服务**预测建模与风险分层**（prediction / risk stratification），不服务因果推断。模型回答「谁的风险更高、费用预计多少」，不回答「干预某个因素是否会改变结局」。变量重要性与 SHAP 值描述的是**预测贡献**，不代表因果效应；如需因果结论，应使用目标试验、工具变量、双重差分等设计并在混杂控制下另行估计。\n\n## 适用范围（明确边界）\n\n允许：\n- 帮助研究者完成预测建模全流程（二分类 / 多分类 / 连续回归）的设计、训练、评估与报告\n- 给出数据拆分、缺失","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-machine-learning-skill","title":"Skill：机器学习助手（卫生经济与社会医学预测建模，Stata / R）","journal":"方法 Skill","year":"","url":"skills/machine-learning-skill.md","text":"# Skill：机器学习助手（卫生经济与社会医学预测建模，Stata / R）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n一份可复用的「机器学习预测建模」工作流：面向社会医学与卫生经济学研究者，覆盖从预测目标定义、模拟数据生成、训练—验证—测试拆分、缺失值处理与标准化，到 LASSO / 弹性网、随机森林、XGBoost 的建模，AUC / 校准 / RMSE 评估与 DeLong 比较，SHAP 解释，直至 TRIPOD 报告与部署落地的全流程。典型场景：**住院费用预测、30 天再入院风险、慢病并发症风险、医保基金支出预测**。**每一步都给出「方法 + 代码」**，可直接作为提示词喂给大语言模型，也可作为操作手册照做。\n\n**定位边界（预测 ≠ 因果）**：本 Skill 服务**预测建模与风险分层**（prediction / risk stratification），不服务因果推断。模型回答「谁的风险更高、费用预计多少」，不回答「干预某个因素是否会改变结局」。变量重要性与 SHAP 值描述的是**预测贡献**，不代表因果效应；如需因果结论，应使用目标试验、工具变量、双重差分等设计并在混杂控制下另行估计。\n\n## 适用范围（明确边界）\n\n允许：\n- 帮助研究者完成预测建模全流程（二分类 / 多分类 / 连续回归）的设计、训练、评估与报告\n- 给出数据拆分、缺失值处理、标准化、LASSO / 弹性网（R `glmnet`、Stata `lasso` / `elasticnet`）、随机森林（`ranger`、`rforest`）、XGBoost（`xgboost`）的方法与代码\n- 用 AUC（95% CI）、Brier 分数、校准曲线、RMSE / MAE 评估模型，用 DeLong 检验或 bootstrap CI 比较模型\n- 用变量重要性与 SHAP（`shapviz`）解释模型，按 TRIPOD 核心条目撰写报告并给出部署清单\n\n**不允许：**\n- 把预测模型当作因果模型使用（SHAP、重要性、回归系数均非因果效应）\n- 承诺模型可直接用于临床或医保决策（须经外部验证、前瞻性研究、伦理与监管审批）\n- 替研究者做结局定义、预测变量取舍与风险阈值的最终专业判断（需临床 / 医保专家确认）\n- 替代统计学与机器学习教科书、TRIPOD 声明全文与期刊审稿要求\n\n## 软件准备\n\n方法：Stata 17 及以上（内置 `lasso`、`elasticnet`、`boosting`；`svmachines` 需 Stata 18），随机森林命令 `rforest` 来自 SSC（作者 Schonlau & Zou）；R ≥ 4.2，核心包 `glmnet`、`ranger`、`xgboost`、`pROC`、`shapviz`、`caret`、`rms`、`mice`、`Matrix`、`DALEX`。\n\n```stata\n* Stata（联网安装一次）\nssc install rforest, replace\n```\n\n```r\n# R\ninstall.packages(c(\"glmnet\", \"ranger\", \"xgboost\", \"pROC\", \"shapviz\",\n                   \"caret\", \"rms\", \"mice\", \"Matrix\", \"DALEX\"))\n```\n\n## 工作流程总览（6 步）\n\n1. 问题定义与数据准备（生成模拟数据、拆分 70/15/15、缺失处理、标准化）→ 2. 基线模型与变量筛选（logistic / 线性回归、LASSO、弹性网与 λ 路径）→ 3. 树模型与集成（随机森林、XGBoost 与调参）→ 4. 模型评估与比较（AUC、校准、Brier、RMSE / MAE、DeLong）→ 5. 可解释性（变量重要性、SHAP）→ 6. 报告与落地（TRIPOD 对照、防泄漏清单、外部验证）。以下每一步均为「方法 + 代码」。\n\n---\n\n### 第 1 步：问题定义与数据准备\n\n**方法**：\n\n- **预测目标**：先定分类还是回归。高费用 yes/no、30 天再入院、并发症发生为**分类**（评估指标 AUC + 校准）；住院费用、医保基金支出、住院天数为**回归**（评估指标 RMSE / MAE / R²，右偏结局建议取对数）。\n- **时点规则（防泄漏的第一道关）**：结局必须定义在明确时点（如出院后 30 天内非计划再入院）；预测变量必须在该时点**之前已可获得**。任何发生在结局之后的信息（如出院后检查、结算后诊断升级）都禁止作为特征。\n- **数据拆分**：训练 70% / 验证 15% / 测试 15%（本演示 N=1000 → 700/150/150），或交叉验证（CV）。调参只碰验证集，测试集仅在最终评估时**使用一次**。\n- **缺失值处理**：先判断机制（MCAR/MAR/MNAR）。演示用中位数插补；正式研究建议多重插补（MICE），且插补必须在**交叉验证折内**进行，不能先在全量数据上插补再拆分。\n- **标准化**：数值变量做 z-score，均值与标准差**只用训练集计算**，再套用到验证 / 测试集（防泄漏）。树模型对单调变换不敏感，但 LASSO / 逻辑回归 / SHAP 解读需要。\n- **模拟数据（贯穿全程）**：N = 1000 名住院患者；特征 age（年龄）、sex（1=女）、comorb（合并症计数）、los（住院天数）、insurance（医保类型：城职/城居/新农合）与 5 个纯噪声变量 z1–z5（用于演示变量筛选应将其剔除）；结局 highcost（高费用 yes/no，约 25% 阳性）与 cost（总费用，右偏）。真实信号只藏在 age、comorb、los、insurance、sex 中。\n\n**代码**：\n\n```r\n# ---- R：生成模拟数据（N = 1000，可全程复现）----\nset.seed(2026)\nn <- 1000\ndat <- data.frame(\n  id        = 1:n,\n  age       = round(rnorm(n, 65, 12)),        # 年龄\n  sex       = rbinom(n, 1, 0.52),             # 1 = 女\n  comorb    = rpois(n, 1.5),                  # 合并症计数\n  insurance = sample(c(\"城职\", \"城居\", \"新农合\"), n, replace = TRUE,\n                     prob = c(0.4, 0.3, 0.3)),\n  los       = rpois(n, 7) + 1                 # 住院天数\n)\ndat$age    <- pmin(pmax(dat$age, 18), 95)\ndat$comorb <- pmin(dat$comorb, 8)\nfor (k in 1:5) dat[[paste0(\"z\", k)]] <- rnorm(n)   # 纯噪声特征\n\n# 结局：同一套真实信号驱动两个结局（分类 + 连续）\nlp <- -5 + 0.03 * dat$age + 0.45 * dat$comorb + 0.12 * dat$los +\n      0.40 * (dat$insurance == \"城职\") + 0.25 * dat$sex\ndat$highcost <- rbinom(n, 1, plogis(lp))      # 高费用 yes/no（约 25%）\ndat$cost <- exp(7.5 + 0.01 * dat$age + 0.18 * dat$comorb + 0.05 * dat$los +\n                0.25 * (dat$insurance == \"城职\") + 0.9 * dat$highcost +\n                rnorm(n, 0, 0.5))             # 总费用（元，右偏）\ntable(dat$highcost); summary(dat)\n\n# ---- 人为制造 10% MCAR 缺失（演示缺失处理用）----\nset.seed(2)\ndat$comorb[sample(n, 100)] <- NA\n\n# ---- 70 / 15 / 15 拆分 ----\nset.seed(3)\ndat$split <- sample(rep(1:3, c(700, 150, 150)))   # 1=训练 2=验证 3=测试\ntr <- dat[dat$split == 1, ]; va <- dat[dat$split == 2, ]; te <- dat[dat$split == 3, ]\n\n# ---- 缺失值处理：用训练集中位数插补三组（演示；正式研究用 MICE 且在 CV 折内）----\nm_med <- median(tr$comorb, na.rm = TRUE)\ntr$comorb <- ifelse(is.na(tr$comorb), m_med, tr$comorb)\nva$comorb <- ifelse(is.na(va$comorb), m_med, va$comorb)\nte$comorb <- ifelse(is.na(te$comorb), m_med, te$comorb)\n\n# 多重插补示例（正式研究用；注意应在 CV 折内进行）\n# library(mice)\n# imp <- mice(dat[, c(\"age\",\"sex\",\"comorb\",\"los\",\"highcost\",\"cost\")], m = 5, seed = 4)\n# fit <- with(imp, glm(highcost ~ age + sex + comorb + los, family = binomial))\n# pool(fit)\n\n# ---- 标准化：只在训练集上计算均值 / SD，再套用到三组 ----\nfor (v in c(\"age\", \"comorb\", \"los\")) {\n  m <- mean(tr[[v]]); s <- sd(tr[[v]])\n  tr[[paste0(v, \"_s\")]] <- (tr[[v]] - m) / s\n  va[[paste0(v, \"_s\")]] <- (va[[v]] - m) / s\n  te[[paste0(v, \"_s\")]] <- (te[[v]] - m) / s\n}\n```\n\n```stata\n* ---- Stata：生成同一套模拟数据 ----\nclear\nset seed 2026\nset obs 1000\ngen id = _n\ngen age = round(rnormal(65, 12))\nreplace age = min(max(age, 18), 95)\ngen sex = rbinomial(1, 0.52)\ngen comorb = min(rpoisson(1.5), 8)\ngen u = runiform()\ngen ins = cond(u < 0.4, 1, cond(u < 0.7, 2, 3))   // 1=城职 2=城居 3=新农合\nlabel define inslab 1 \"城职\" 2 \"城居\" 3 \"新农合\"\nlabel values ins inslab\ngen los = rpoisson(7) + 1\nforvalues k = 1/5 {\n    gen z`k' = rnormal()                         // 纯噪声特征\n}\ngen lp = -5 + 0.03*age + 0.45*comorb + 0.12*los + 0.40*(ins == 1) + 0.25*sex\ngen highcost = rbinomial(1, invlogit(lp))\ngen cost = exp(7.5 + 0.01*age + 0.18*comorb + 0.05*los + 0.25*(ins == 1) + 0.9*highcost + rnormal(0, 0.5))\ngen lncost = ln(cost)\ntab highcost\n\n* ---- 人为制造 10% MCAR 缺失 ----\nset seed 2026\nreplace comorb = . if runiform() < 0.10\n\n* ---- 70 / 15 / 15 拆分（1=训练 2=验证 3=测试）----\nsplitsample, generate(svar, replace) split(0.70 0.15 0.15) rseed(3)\ntab svar\n\n* ---- 缺失值处理：训练集中位数插补（演示；正式研究用 mi impute chained）----\nsummarize comorb if svar == 1, detail\nreplace comorb = r(p50) if missing(comorb)\n\n* 多重插补示例（正式研究用）\n* mi set mlong\n* mi register imputed comorb\n* mi impute chained (pmm) comorb = age sex los highcost cost, add(5) rseed(4)\n\n* ---- 标准化：以训练集均值 / SD 为基准 ----\nforeach v in age comorb los {\n    quietly summarize `v' if svar == 1\n    gen `v'_s = (`v' - r(mean)) / r(sd)\n}\n```\n\n---\n\n### 第 2 步：基线模型与变量筛选\n\n**方法**：\n\n- **基线模型**：分类用 logistic 回归（报告 OR 与 95% CI），回归用线性回归（右偏费用先取对数）。基线模型是所有复杂模型的「及格线」：复杂模型若不能显著超越它，优先用简单模型。\n- **LASSO（L1）与弹性网（L1+L2）**：对系数施加收缩，自动把无用变量（如噪声 z1–z5）压缩为 0，实现变量筛选。λ 用交叉验证选择：`lambda.min`（CV 误差最小，模型较大）与 `lambda.1se`（1 个标准误法则，模型更小更稳），正式报告建议用 `lambda.1se`。弹性网 `alpha=0.5` 在高相关变量场景更稳。\n- **自适应 LASSO**：先普通 LASSO 得到初始系数，再按其倒数加权做第二遍 LASSO，降低对无关变量的误选率。\n- **逐步回归（stepwise）**只作敏感性分析，不能当因果筛选工具；不要用全量数据选变量后再拆 CV 评估（泄漏）。\n- glmnet 默认对矩阵做标准化（`standardize = TRUE`），与第 1 步的手工标准化互为保险；系数解读时注意区分原始尺度与标准化尺度。\n\n**代码**：\n\n```r\n# ---- 模型矩阵（glmnet 不接受因子，需 model.matrix；含噪声变量观察收缩效果）----\nlibrary(glmnet)\nxvars <- c(\"age_s\", \"sex\", \"comorb_s\", \"los_s\", \"z1\", \"z2\", \"z3\", \"z4\", \"z5\", \"insurance\")\nf <- as.formula(paste(\"~\", paste(xvars, collapse = \" + \")))\nx_tr <- model.matrix(f, data = tr)[, -1]\nx_va <- model.matrix(f, data = va)[, -1]\nx_te <- model.matrix(f, data = te)[, -1]\ny_tr <- tr$highcost; y_te <- te$highcost\n\n# ---- 基线 logistic（不收缩，作为及格线）----\nbase <- glm(highcost ~ age_s + sex + comorb_s + los_s + insurance + z1 + z2 + z3 + z4 + z5,\n            data = tr, family = binomial)\nsummary(base)\n\n# 备选：逐步回归（仅作敏感性分析）\n# library(MASS)\n# stepAIC(base, direction = \"both\", trace = FALSE)\n\n# ---- 基线线性回归（连续费用，取对数）----\nlm_base <- lm(log(cost) ~ age_s + sex + comorb_s + los_s + insurance, data = tr)\nsummary(lm_base)\n\n# ---- LASSO（alpha = 1），10 折 CV 选 λ ----\nset.seed(7)\ncv_lasso <- cv.glmnet(x_tr, y_tr, family = \"binomial\", alpha = 1, nfolds = 10)\nplot(cv_lasso)                                # CV 误差曲线（两条虚线 = lambda.min / 1se）\ncoef(cv_lasso, s = \"lambda.min\")              # 噪声变量 z1–z5 应为 0\ncoef(cv_lasso, s = \"lambda.1se\")              # 更保守（1-SE 法则，模型更小）\n\n# ---- 弹性网（alpha = 0.5）----\nset.seed(8)\ncv_en <- cv.glmnet(x_tr, y_tr, family = \"binomial\", alpha = 0.5, nfolds = 10)\ncoef(cv_en, s = \"lambda.1se\")\n\n# ---- λ 路径图（系数随 log(λ) 增大被收缩至 0 的轨迹）----\nplot(cv_lasso$glmnet.fit, xvar = \"lambda\", label = TRUE)\nabline(v = log(cv_lasso$lambda.1se), lty = 2)\n\n# ---- 连续结局版：LASSO 回归（对数费用）----\nset.seed(9)\ncv_lasso_reg <- cv.glmnet(x_tr, log(tr$cost), alpha = 1, nfolds = 10)\ncoef(cv_lasso_reg, s = \"lambda.1se\")\n```\n\n```stata\n* ---- 基线 logistic（含噪声变量，作为及格线）----\nlogit highcost age_s sex comorb_s los_s i.insurance z1-z5 if svar == 1\nest store base\n\n* ---- 基线线性回归（对数费用）----\nregress lncost age_s sex comorb_s los_s i.insurance if svar == 1\n\n* ---- LASSO（Stata 16+），CV 选 λ ----\nlasso logit highcost age_s sex comorb_s los_s i.insurance z1-z5 if svar == 1, selection(cv) rseed(7)\ncvplot                                     // CV 均方误差曲线\ncoefpath                                    // λ 路径（系数收缩轨迹）\nlassocoef, display(coef, standardized)      // 选后系数：噪声变量应被剔除\n\n* ---- 自适应 LASSO：先 CV 选 λ，再加自适应权重重估 ----\nlasso, select(adaptive)\nlassocoef, display(coef, standardized)\n\n* ---- 弹性网（Stata 17+）----\nelasticnet logit highcost age_s sex comorb_s los_s i.insurance z1-z5 if svar == 1, selection(cv) alpha(0.5) rseed(8)\nlassocoef, display(coef, standardized)\n\n* ---- 连续结局版：LASSO 回归 ----\nlasso linear lncost age_s sex comorb_s los_s i.insurance z1-z5 if svar == 1, selection(cv) rseed(9)\nlassocoef, display(coef, standardized)\n```\n\n---\n\n### 第 3 步：树模型与集成\n\n**方法**：\n\n- **随机森林（bagging）**：Bootstrap 抽样 + 每次分裂随机抽取特征子集（分类取 mtry ≈ √p，回归取 ≈ p/3），数百棵树投票 / 取均值。能自动捕捉非线性与交互，OOB 误差可近似无偏估计泛化性能。ranger 是 C++ 实现，速度远快于 randomForest，接口一致。\n- **XGBoost（boosting）**：梯度提升 + 正则化（`max_depth`、`eta`、`subsample`、`colsample_bytree`），逐步纠错前一轮的残差；配合**早停**（early stopping）防止过拟合。调参在**验证集**上进行（手动网格或 caret），测试集只用于最终评估。\n- 两者都是「黑箱」，需要第 5 步的 SHAP 解释支撑临床应用；树模型对变量单调变换不敏感，但仍建议统一用第 1 步的标准化数据以便跨模型比较。\n\n**代码**：\n\n```r\n# ---- 统一建模数据框（特征 + 结局）----\nfeats <- c(\"age_s\", \"sex\", \"comorb_s\", \"los_s\", \"insurance\",\n           \"z1\", \"z2\", \"z3\", \"z4\", \"z5\", \"highcost\")\ntr_m <- tr[, feats]; va_m <- va[, feats]; te_m <- te[, feats]\n\n# ---- 随机森林（ranger；分类结局须为 factor）----\nlibrary(ranger)\nset.seed(11)\nrf <- ranger(factor(highcost) ~ ., data = tr_m,\n             num.trees = 500, mtry = 3, min.node.size = 10,\n             importance = \"impurity\", probability = TRUE)\nrf                                          # OOB 预测误差\nrf$variable.importance                      # 变量重要性\np_rf_va <- predict(rf, data = va_m)$predictions[, \"1\"]   # 验证集概率（调参用）\np_rf_te <- predict(rf, data = te_m)$predictions[, \"1\"]   # 测试集概率（仅评估一次）\n\n# ---- XGBoost：xgb.cv 单组参数 + 早停 ----\nlibrary(xgboost)\nlibrary(Matrix)\nmtr <- sparse.model.matrix(highcost ~ . - 1, data = tr_m)\nmva <- sparse.model.matrix(highcost ~ . - 1, data = va_m)\nmte <- sparse.model.matrix(highcost ~ . - 1, data = te_m)\ndtrain <- xgb.DMatrix(mtr, label = tr_m$highcost)\ndval   <- xgb.DMatrix(mva, label = va_m$highcost)\ndtest  <- xgb.DMatrix(mte, label = te_m$highcost)\n\nset.seed(12)\ncvx <- xgb.cv(params = list(objective = \"binary:logistic\", eval_metric = \"auc\",\n                            eta = 0.05, max_depth = 3,\n                            subsample = 0.8, colsample_bytree = 0.8),\n              data = dtrain, nrounds = 500, nfold = 5,\n              early_stopping_rounds = 20, verbose = 0)\ncvx$best_iteration                                   # 最佳轮数\nmax(cvx$evaluation_log$test_auc_mean)                # CV AUC\n\n# ---- XGBoost：手动小网格（验证集早停选参）----\ngrid <- expand.grid(eta = c(0.05, 0.1), max_depth = c(2, 3, 4))\nres <- lapply(seq_len(nrow(grid)), function(i) {\n  p <- list(objective = \"binary:logistic\", eval_metric = \"auc\",\n            eta = grid$eta[i], max_depth = grid$max_depth[i],\n            subsample = 0.8, colsample_bytree = 0.8)\n  xgb.train(params = p, data = dtrain, nrounds = 500,\n            watchlist = list(val = dval), early_stopping_rounds = 20, verbose = 0)\n})\nva_auc <- sapply(res, function(m) m$best_score)       # 各组合验证集 AUC\nbest_i <- which.max(va_auc)\ngrid[best_i, ]                                        # 最优参数\nbest_n <- res[[best_i]]$best_iteration                # 最优轮数\n\n# 用最优参数 + 最优轮数在「训练+验证」上重训，测试集只评估一次\nfull <- xgb.DMatrix(rbind(mtr, mva), label = c(tr_m$highcost, va_m$highcost))\nbst <- xgb.train(params = res[[best_i]]$params, data = full, nrounds = best_n)\np_xgb_te <- predict(bst, dtest)\nxgb.importance(model = bst)                           # Gain / Cover 重要性\n\n# ---- 等价写法：caret 网格调参（二选一）----\nlibrary(caret)\nset.seed(13)\ncg <- expand.grid(nrounds = 300, max_depth = c(2, 3, 4), eta = c(0.05, 0.1),\n                  gamma = 0, colsample_bytree = 0.8,\n                  min_child_weight = 1, subsample = 0.8)\nctrl <- trainControl(method = \"cv\", number = 5, classProbs = TRUE,\n                     summaryFunction = twoClassSummary)\nfit_caret <- train(x = tr_m[, -ncol(tr_m)], y = factor(tr_m$highcost),\n                   method = \"xgbTree\", trControl = ctrl, tuneGrid = cg,\n                   metric = \"ROC\")\nfit_caret$bestTune\n```\n\n```stata\n* ---- 随机森林（rforest，SSC；不支持因子变量写法，先造虚拟变量）----\ntab ins if svar == 1, gen(ins_)\nrforest highcost age_s sex comorb_s los_s ins_2 ins_3 z1-z5 if svar == 1, type(class) iterations(500) numvars(3) seed(11) vimp\n* 预测概率的变量命名与后处理以 help rforest 为准；\n* 调参（numvars / depth / lsize）应在验证集（svar == 2）上比较 AUC 后进行\n\n* ---- 官方替代：梯度提升 boosting（Stata 17+）、svmachines（Stata 18）----\n* boosting highcost age_s sex comorb_s los_s ins_2 ins_3 z1-z5 if svar == 1\n* 语法与调参选项以 help boosting 为准\n```\n\n---\n\n### 第 4 步：模型评估与比较\n\n**方法**：\n\n- **分类模型三件套**：① 区分度（discrimination）= AUC（C 统计量），报 95% CI（bootstrap）；② 校准（calibration）= 校准曲线 + 校准截距/斜率（良好时截距≈0、斜率≈1）与 Brier 分数（越小越好，同时反映区分度与校准）；③ 阈值相关指标（敏感性/特异性/Youden 指数）按用途选取，不平衡结局下不用 accuracy、改用 PR 曲线。树模型与 XGBoost 常「AUC 高但校准差」，必须报告校准。\n- **回归模型**：RMSE / MAE / R² 在对数尺度上计算（本演示结局取 log），解释时注明尺度。\n- **模型比较**：同一测试集上多个模型 → 相关样本，用 **DeLong 检验**（`pROC::roc.test` / Stata `roccomp`）或 bootstrap CI；比较必须只在测试集做一次，否则「测试集泄漏」。\n- **防乐观偏差**：训练集表现一律不作为结论；嵌套 CV（nested CV）是「调参 + 评估」同时进行的严格做法。\n\n**代码**：\n\n```r\nlibrary(pROC)\n\n# ---- 各模型在测试集的预测概率（基线 / LASSO 出自 glm 与 glmnet）----\np_base_te  <- predict(base, newdata = te, type = \"response\")\np_lasso_te <- drop(predict(cv_lasso, newx = x_te, s = \"lambda.1se\", type = \"response\"))\n# p_rf_te、p_xgb_te 来自第 3 步\n\nroc_base  <- roc(y_te, p_base_te)\nroc_lasso <- roc(y_te, p_lasso_te)\nroc_rf    <- roc(y_te, p_rf_te)\nroc_xgb   <- roc(y_te, p_xgb_te)\n\nauc(roc_base); auc(roc_lasso); auc(roc_rf); auc(roc_xgb)\nci.auc(roc_rf)                                   # bootstrap 95% CI（默认 2000 次）\n\n# ---- 模型比较：DeLong 检验（相关样本）----\nroc.test(roc_rf, roc_base, method = \"delong\")    # RF vs 基线 logistic\nroc.test(roc_xgb, roc_rf, method = \"delong\")     # XGB vs RF\n\n# ---- ROC 曲线叠加 ----\nplot(roc_base, col = \"grey50\", lwd = 2)\nplot(roc_lasso, add = TRUE, col = \"steelblue\", lwd = 2)\nplot(roc_rf, add = TRUE, col = \"firebrick\", lwd = 2)\nplot(roc_xgb, add = TRUE, col = \"darkgreen\", lwd = 2)\nlegend(\"bottomright\", legend = c(\"logistic\", \"LASSO\", \"RF\", \"XGBoost\"),\n       col = c(\"grey50\", \"steelblue\", \"firebrick\", \"darkgreen\"), lwd = 2)\n\n# ---- 阈值（Youden）与敏感性 / 特异性 ----\ncoords(roc_xgb, \"best\", ret = c(\"threshold\", \"sensitivity\", \"specificity\"))\n\n# ---- 校准：Brier 分数 + 校准曲线（截距≈0、斜率≈1 为校准良好）----\nbrier <- function(p, y) mean((p - y)^2)\nbrier(p_base_te, y_te); brier(p_rf_te, y_te); brier(p_xgb_te, y_te)\n\nlibrary(rms)\nval.prob(p_xgb_te, y_te, statloc = FALSE)        # 校准图 + Brier + 校准截距/斜率\n# 校准差 → Platt scaling / isotonic regression 等再校准，或回退简单模型\n\n# ---- 回归（对数费用）：RMSE / MAE / R² ----\np_lm_te   <- predict(lm_base, newdata = te)\np_lreg_te <- drop(predict(cv_lasso_reg, newx = x_te, s = \"lambda.1se\"))\ny_cont <- log(te$cost)\nrmse <- function(p, y) sqrt(mean((p - y)^2))\nmae  <- function(p, y) mean(abs(p - y))\nrmse(p_lm_te, y_cont);   mae(p_lm_te, y_cont)\nrmse(p_lreg_te, y_cont); mae(p_lreg_te, y_cont)\n1 - sum((p_lreg_te - y_cont)^2) / sum((y_cont - mean(y_cont))^2)   # R²\n```\n\n```stata\n* ---- 各模型预测概率（基线 / LASSO 用各自 predict 产出）----\nlogit highcost age_s sex comorb_s los_s i.insurance z1-z5 if svar == 1\npredict p_base, pr\nlasso logit highcost age_s sex comorb_s los_s i.insurance z1-z5 if svar == 1, selection(cv) rseed(7)\npredict p_lasso, pr\n* （rforest / boosting 的预测变量按各自 postestimation 命令生成）\n\n* ---- 单模型 ROC 与 AUC ----\nroctab highcost p_base if svar == 3, graph\n* logit 拟合后可用 lroc（含 AUC 与 ROC 图）\n\n* ---- 多模型比较（DeLong 法与 Hanley–McNeil 法，只评估测试集）----\nroccomp highcost p_base p_lasso p_rf p_xgb if svar == 3, graph\n\n* ---- 校准：重拟合检验截距/斜率（应接近 0/1）+ Hosmer–Lemeshow + Brier ----\nlogit highcost p_xgb if svar == 3\nestat gof, group(10)\ngen b2 = (highcost - p_xgb)^2 if svar == 3\nsummarize b2                                // r(mean) 即 Brier 分数\n\n* ---- 回归指标：RMSE / MAE（对数费用）----\ngen e2 = (lncost - p_lreg)^2 if svar == 3\nsummarize e2\ndisplay \"RMSE = \" sqrt(r(mean))\ngen ae = abs(lncost - p_lreg) if svar == 3\nsummarize ae\ndisplay \"MAE = \" r(mean)\n```\n\n---\n\n### 第 5 步：可解释性\n\n**方法**：\n\n- **全局解释**：变量重要性排序——树模型用杂质重要性（impurity）或更稳健的置换重要性（permutation）；XGBoost 用 Gain / Cover；SHAP 用平均 |SHAP|。三者结论可能不同，报告时注明方法。\n- **局部解释（SHAP）**：对**每名患者**把预测概率分解为各特征的加性贡献（基线概率 + 各特征 SHAP 值之和 = logit 预测）。蜂群图（beeswarm）看整体方向，瀑布图（waterfall）看单个患者「哪些特征把风险推高、哪些拉低」。\n- **临床用法**：SHAP 支持「为什么这个患者被标为高风险」的个体化解释，用于病例管理与沟通；但 SHAP ≠ 因果——「住院天数 SHAP 最大」不意味着「缩短住院天数即可降费用」。\n- 变量重要性 / SHAP 都依赖模型本身；模型换了、相关性结构变了，解释也会变，报告时注明模型与数据集。\n\n**代码**：\n\n```r\nlibrary(shapviz)\n\n# ---- SHAP 值（XGBoost，测试集）----\nsv <- shapviz(bst, X_pred = mte)\nsv_importance(sv, kind = \"both\")                 # 平均|SHAP|条形图 + 蜂群图\nsv_importance(sv, kind = \"beeswarm\")             # 蜂群图：颜色 = 特征取值方向\nsv_dependence(sv, v = \"age_s\")                   # 依赖图：年龄的非线性贡献\n\n# ---- 单个患者瀑布图 ----\ni_hi <- which.max(p_xgb_te)                      # 测试集中预测风险最高者\nsv_waterfall(sv, row_id = i_hi)\n\n# ---- 随机森林置换重要性（比杂质重要性更稳健）----\nset.seed(14)\nrf_p <- ranger(factor(highcost) ~ ., data = te_m, num.trees = 500,\n               importance = \"permutation\", probability = TRUE)\nrf_p$variable.importance\n\n# ---- 备选：DALEX（模型无关解释）----\n# library(DALEX)\n# ex <- explain(bst, data = as.data.frame(as.matrix(mte)), y = te_m$highcost)\n# plot(model_parts(ex))                        # 置换重要性\n```\n\n**临床解读示例**（模拟数据，仅示意）：测试集中某患者 78 岁、合并症 3 项、住院 15 天、城职医保、女性，XGBoost 预测高费用概率 0.62。其瀑布图显示：`los_s` 贡献 +0.15（最大推高因素）、`comorb_s` +0.09、`age_s` +0.04、`insurance城职` +0.02，被 `sex` −0.01 与噪声特征 ≈ 0 部分抵消。临床表述：「该患者风险主要来自长住院天数与多合并症，建议纳入病例管理（出院规划、康复转介）」。注意这只是**预测解释**；若想主张「干预住院天数可降低费用」，需因果设计另行论证。\n\n```stata\n* ---- Stata 侧可解释性 ----\n* rforest 变量重要性：vimp 选项生成的 _rf_v_* 变量（见 help rforest）\n* lasso / elasticnet 的标准化系数即一种可解释性输出\nlassocoef, display(coef, standardized)\n* 局限：Stata 官方目前无 SHAP 实现；完整的 SHAP 分析建议在 R（shapviz）侧完成\n```\n\n---\n\n### 第 6 步：报告与落地\n\n**方法**：按 TRIPOD（Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis）核心条目自查；做一遍防过拟合 / 数据泄漏终检；在论文限制部分写明「预测 ≠ 因果」；部署前必须完成外部验证。\n\n#### 6.1 TRIPOD 核心条目对照表\n\n| TRIPOD 条目 | 要点 | 本流程对应做法 |\n| --- | --- | --- |\n| 1 标题与摘要 | 标题注明「预测模型的开发 / 验证」 | 写「预测模型」，不写「危险因素研究」 |\n| 3a–4a 数据来源与参与者 | 队列 / 医院 / 医保数据库、纳排标准、机构与时间 | 真实研究需完整描述；本 Skill 为模拟数据仅作教学 |\n| 5a–5b 结局与预测因子 | 定义、测量方法、评估时点 | 如「30 天再入院 = 出院后 30 天内非计划再入院」 |\n| 6a 结局定义 | 明确的观察时间窗 | 高费用 = 出院结算总额 ≥ 样本上四分位数 |\n| 8 样本量 | 事件数、EPV（每变量事件数） | logistic 经验 EPV ≥ 10；样本小时优先正则化模型 |\n| 9 缺失数据 | 缺失比例、机制假设、处理方法 | 中位数演示；正式用 MICE 且在 CV 折内 |\n| 10a–10d 模型构建与收缩 | 变量筛选、调参、内部验证 | CV-LASSO、验证集早停、70/15/15 拆分 |\n| 11 模型表现 | 区分度 + 校准，报 CI | AUC（95% CI）、Brier、校准截距/斜率 |\n| 12 模型使用方式 | 评分 / 列线图 / 公式，如何算个体风险 | SHAP 瀑布图 + 完整公式或代码 |\n| 15a–15b 个体预测示例 | 至少展示一个具体个体的预测 | 第 5 步临床解读示例 |\n| 16 局限 | 内部验证 vs 外部验证的差距 | 明确声明未经外部验证时的适用范围 |\n| 19 模型比较 | 与现有简单模型比较 | DeLong：RF / XGB vs logistic |\n| 22 更新与推广 | 模型维护与再校准计划 | 校准漂移定期重估（见 6.4） |\n\n#### 6.2 防过拟合 / 数据泄漏终检清单\n\n- [ ] 标准化、插补、特征筛选全部在训练集上拟合，再套用到验证 / 测试\n- [ ] 调参只用验证集；测试集只在最终评估时使用一次\n- [ ] 插补与变量筛选在交叉验证折内进行（或嵌套 CV）\n- [ ] 没有使用结局发生之后才可获得的信息作为特征\n- [ ] λ、早停轮数等由数据自动选择，而非人工挑「好看的」\n- [ ] 复杂模型与 logistic 基线并列报告，并说明增量价值\n- [ ] 未用训练集表现代替泛化表现\n\n#### 6.3 预测 ≠ 因果声明（写入论文限制部分）\n\n> 本模型旨在**预测**（住院费用 / 再入院风险），不解释因果机制。变量重要性与 SHAP 值反映预测贡献，不代表干预该变量即可改变结局；如需因果推断，应采用目标试验、工具变量、双重差分等设计并在混杂控制下另行估计。\n\n#### 6.4 部署提示（外部验证必要性）\n\n- **外部验证**：至少在**不同时间、不同机构或不同地区**的独立数据上验证一次，报告 AUC / 校准的下降幅度；仅内部验证（含 CV）不能支持临床 / 医保推广。\n- **前瞻性验证**：在目标使用场景下做前瞻队列验证，确认数据完整性与可用时点。\n- **校准漂移监控**：上线后定期用新数据重估 Brier / 校准斜率，设定触发再校准的阈值。\n- **合规**：医保与临床数据须脱敏；临床决策支持类应用需伦理审查与相应软件 / 器械监管程序；建议随模型发布「模型卡片」（人群、时点、性能、限制）。\n\n---\n\n## 常见错误清单\n\n- [ ] 数据泄漏：在全量数据上标准化 / 插补 / 选特征，再拆分或 CV → 表现虚高\n- [ ] 在测试集上反复调参再报结果 → 测试集泄漏\n- [ ] 只在训练集上评估模型，把训练表现当结论\n- [ ] 只用准确率（accuracy）评价不平衡结局 → 应报 AUC + 校准 + PR 曲线\n- [ ] 只看 AUC 不看校准：树 / 提升模型常 AUC 高但校准差 → 报 Brier 与校准图\n- [ ] 不平衡结局随意下采样 / 过采样后不校正预测概率\n- [ ] 把变量重要性 / SHAP / 系数当作因果效应\n- [ ] 结局与预测变量时点错位（用了结局之后的信息）\n- [ ] 整行删除缺失值导致选择偏倚；插补未纳入结局变量（应纳入以减少偏倚，且在 CV 折内）\n- [ ] 连续费用直接建模不处理右偏与极端值 → 取对数或考虑 Tweedie / 分位数回归\n- [ ] 小样本（事件数少）上复杂模型不做正则化与内部验证\n- [ ] Stata：`rforest` / `boosting` 直接写 `i.` 因子变量报错 → 先 `tab` 造虚拟变量\n- [ ] 报告 SHAP / 重要性却不注明所用模型、数据集与方法（impurity / permutation / SHAP 结论可不同）\n\n## 输出格式（预测建模报告模板）\n\n```\n【问题与数据】\n预测目标（分类/回归）、结局定义与观察时点、数据来源与样本量、拆分方案（70/15/15 或 CV）、\n缺失比例与处理方法、标准化说明。\n\n【模型构建】\n表 1：基线 logistic / 线性回归（OR 或系数 + 95% CI）；\n表 2：LASSO / 弹性网（λ 选择法则、入选变量与系数）；\n表 3：RF 与 XGBoost（超参数、调参方式、早停轮数）。\n\n【模型表现】\n表 4：测试集 AUC（95% CI）、Brier、校准截距/斜率（分类）或 RMSE / MAE / R²（回归）；\n模型比较（DeLong p 值或 bootstrap CI）。\n\n【解释】\n变量重要性排序（注明方法）；1–2 名患者的 SHAP 瀑布图与临床解读。\n\n【结论与落地】\nTRIPOD 条目对照、防泄漏清单核对结果、局限（内部 vs 外部验证）、\n部署与校准漂移监控计划、预测 ≠ 因果声明。\n```\n\n## 使用提示\n\n- 命令与选项随版本更新：Stata 运行 `help lasso` / `help elasticnet` / `help rforest` / `help roccomp` / `help boosting`，R 运行 `?cv.glmnet` / `?ranger` / `?xgb.cv` / `?roc.test` / `?shapviz` 核对。\n- 建议流程：logistic 基线 → LASSO 筛变量 → RF / XGBoost → 测试集一次性评估 + DeLong / 校准比较；复杂模型不比 logistic 显著好时，优先简单模型。\n- 全程固定随机种子（`set.seed` / `set seed`）保证可复现；模拟数据只用于走通流程，真实结论须以真实数据 + 外部验证为准。\n- 本 Skill 提供方法与代码模板；研究背景、结局与变量取舍、风险阈值及最终结论由研究者负责。\n\n---\n\n## 引用 / Citation\n\n中文：杜思娴. 机器学习助手：卫生经济与社会医学预测建模 Skill [EB/OL]. (2026). https://naturedu.github.io/socialmedicine/.\n\nEnglish: Du, S. (2026). *Machine Learning Assistant: Predictive modeling for health economics and social medicine skill* [Skill definition]. https://naturedu.github.io/socialmedicine/.\n","kind":"方法 Skill","locator":"方法手册","license":"MIT（仓库原有技能）","verified":"原有公开仓库"}},{"id":"studio-library:skill-markov-model-skill","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Skill：Markov 模型助手（卫生经济学成本-效果分析，Stata / R）","summary":"# Skill：Markov 模型助手（卫生经济学成本-效果分析，Stata / R）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n一份可复用的「Markov 模型（队列模拟）」成本-效果分析（CEA）工作流：从健康状态定义、转移概率的来源与估计（率 → 概率换算）、转移矩阵构建、成本与效用参数设定，到 Stata / R 的队列模拟（含半周期校正与 3% / 5% 贴现）、ICER 计算与阈值比较、单因素（tornado）与概率敏感性分析（PSA），再到结果报告与模型校验。**每一步都给出「方法 + 代码」**（Stata 与 R 双实现，R 侧另给 heemod 包的等价写法）。全程使用同一示例：**高血压 / 卒中预防干预**（常规治疗 vs 强化降压干预；3 个状态、1 年周期、30 年时间范围），所有代码为可直接运行的具体示例、数值自洽。既可作为提示词喂给大语言模型，也可作为操作手册照做。\n\n## 适用范围（明确边界）\n\n允许：\n- 帮助社会医学与卫生经济学研究者完成 Markov 队列模型的 CEA 全流程（药物、筛查、慢病管理等干预的建模）\n- 给出率 → 概率换算、转移矩阵构建、半周期校正、贴现、ICER / NMB、tornado 与 PSA 的方法与 Stata / R 代码\n- 用 heemod（R）、Mata（Stata）等工具复现与交叉验证同一","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-markov-model-skill","title":"Skill：Markov 模型助手（卫生经济学成本-效果分析，Stata / R）","journal":"方法 Skill","year":"","url":"skills/markov-model-skill.md","text":"# Skill：Markov 模型助手（卫生经济学成本-效果分析，Stata / R）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n一份可复用的「Markov 模型（队列模拟）」成本-效果分析（CEA）工作流：从健康状态定义、转移概率的来源与估计（率 → 概率换算）、转移矩阵构建、成本与效用参数设定，到 Stata / R 的队列模拟（含半周期校正与 3% / 5% 贴现）、ICER 计算与阈值比较、单因素（tornado）与概率敏感性分析（PSA），再到结果报告与模型校验。**每一步都给出「方法 + 代码」**（Stata 与 R 双实现，R 侧另给 heemod 包的等价写法）。全程使用同一示例：**高血压 / 卒中预防干预**（常规治疗 vs 强化降压干预；3 个状态、1 年周期、30 年时间范围），所有代码为可直接运行的具体示例、数值自洽。既可作为提示词喂给大语言模型，也可作为操作手册照做。\n\n## 适用范围（明确边界）\n\n允许：\n- 帮助社会医学与卫生经济学研究者完成 Markov 队列模型的 CEA 全流程（药物、筛查、慢病管理等干预的建模）\n- 给出率 → 概率换算、转移矩阵构建、半周期校正、贴现、ICER / NMB、tornado 与 PSA 的方法与 Stata / R 代码\n- 用 heemod（R）、Mata（Stata）等工具复现与交叉验证同一模型\n\n**不允许：**\n- 替研究者决定状态结构、转移概率、成本与效用取值及临床假设（需临床专家、流行病学与卫生经济学证据支撑）\n- 承诺模型结果一定可发表；不替代《中国药物经济学评价指南（2020）》、ISPOR-SMDM 建模良好实践与教科书（如 Briggs et al. 2006）及期刊审稿要求\n- 在没有参数来源、没有做模型校验（face validity、极端值检验）的情况下直接给出结论\n\n## 软件准备\n\n方法：Stata 17 及以上（本 Skill 只用内置命令与 Mata，无需安装外部包）；R ≥ 4.2 + heemod（CRAN；heemod ≥ 1.0 的新版 API，贴现通过 `discount()` 写入状态值）。PSA 绘图用基础 R 即可；矩阵幂（可选）用 `expm`。\n\n```stata\n* Stata：无需额外安装；确认版本 ≥ 17\nversion\n```\n\n```r\n# R\ninstall.packages(c(\"heemod\", \"expm\"))   # expm 可选\n```\n\n## 工作流程总览（8 步）\n\n1. 定义健康状态与模型结构 → 2. 转移概率的来源与估计 → 3. 构建转移矩阵 → 4. 成本与健康效用参数 → 5. 队列模拟（半周期校正 + 贴现） → 6. 计算 ICER 与阈值比较 → 7. 敏感性分析（tornado + PSA） → 8. 报告与校验。以下每一步均为「方法 + 代码」。\n\n---\n\n### 第 1 步：定义健康状态与模型结构\n\n**方法**：\n\n- **健康状态**：互斥（mutually exclusive）、穷尽（collectively exhaustive）、有临床意义，且各状态的成本 / 效用应明显不同。本示例（卒中预防干预）设 3 个状态：① 无并发症（未发生卒中）；② 卒中后；③ 死亡（吸收态）。\n- **周期长度**：与干预作用与疾病自然史匹配，慢性病常用 1 年（急性期模型可用 3 个月或 1 个月）；周期内至多允许一次事件，若事件密集需缩短周期或改用微模拟。\n- **时间范围（horizon）**：足够长使队列绝大部分进入吸收态，通常 30–40 年或至 100 岁；本例取 30 年（敏感性分析可作 40 年）。\n- **起始分布**：本例 100% 处于「无并发症」（trace = (1, 0, 0)）；其他研究可按年龄 / 分期分布设定。\n- **队列规模**：按 1 人（比例）模拟，结果按「每人」报告；也可乘 1000 等人数。\n\n状态图（文字描述）：\n\n```\n无并发症 ──卒中(0.0198)──▶ 卒中后 ──死亡(0.0952)──▶ 死亡\n    │                         │\n    └─────背景死亡(0.010)─────┘\n（死亡为吸收态，无回退、无恢复；数字见第 2 步）\n```\n\n**代码**：\n\n```stata\n* Stata：模型结构骨架（数值在第 2–4 步给出）\n* 状态编号：1 = 无并发症  2 = 卒中后  3 = 死亡（吸收态）\n* 周期 = 1 年；时间范围 T = 30（敏感性情景 40）；起始分布 (1, 0, 0)\n```\n\n```r\n# R：结构参数集中定义，供后续各步复用\nstates       <- c(\"无并发症\", \"卒中后\", \"死亡\")\ncycle_length <- 1        # 年\nT            <- 30       # 时间范围（年）；敏感性情景取 40\ninit         <- c(1, 0, 0)   # 起始：100% 无并发症\nnames(init)  <- states\n```\n\n---\n\n### 第 2 步：转移概率的来源与估计\n\n**方法**：\n\n- **来源**：① 已发表模型（引用并做币值 / 时间通胀与周期长度换算）；② RCT / 队列的生存分析（Kaplan-Meier → 参数生存模型：指数 / Weibull / Gompertz，指数假设下年率 r = −ln S(t)/t）；③ 疾病登记与生命表（背景死亡按年龄别生命表）。\n- **率 → 概率换算（周期 t，率与 t 单位一致）**：p = 1 − exp(−r·t)；反向：r = −ln(1 − p)/t。不同周期长度换算：p_t = 1 − (1 − p_1)^t。\n- 注意：年发生率 0.02 ≠ 年概率 0.02（正确值 0.0198）；率越高二者差异越大。同一状态多个竞争事件先合并率再换算，或在离散框架内用「互补出对角线」保持行和为 1。\n- 示例数值：卒中年发生率 0.02 → 概率 0.0198；干预使卒中风险降 30%（RR = 0.70）→ 率 0.014 → 概率 0.0139；卒中后死亡年率 0.10 → 概率 0.0952；无并发症背景死亡概率 0.010（生命表）。\n\n**代码**：\n\n```stata\n* Stata：率 → 概率（t 为周期长度，按年）\ndisplay 1 - exp(-0.02*1)        // 卒中年发生率 0.02 → 年概率 = 0.0198013\ndisplay 1 - exp(-0.10*1)        // 卒中后死亡年率 0.10 → 年概率 = 0.0951626\ndisplay 1 - exp(-0.02*0.5)      // 半年概率 = 0.0099502\n* 概率 → 率（反向）\ndisplay -ln(1 - 0.0198013)/1\n* 由 5 年生存率 0.80 反推年率与年死亡概率（指数生存假设）\ndisplay -ln(0.80)/5             // 年死亡率 = 0.0446287\ndisplay 1 - exp(-(-ln(0.80)/5)) // 年死亡概率 = 0.0436481\n* 周期长度换算：1 年概率 0.0198013 → 3 个月（1/4 年）概率\ndisplay 1 - (1-0.0198013)^(1/4)\n```\n\n```r\n# R：同样的换算\n1 - exp(-0.02*1)                    # 0.01980133\n1 - exp(-0.10*1)                    # 0.09516258\n-log(1 - 0.01980133)                # 概率 → 率\nS5 <- 0.80\nr_annual <- -log(S5)/5              # 5 年生存率反推年率\n1 - exp(-r_annual)                  # 年死亡概率\n# 周期长度换算\n1 - (1 - 0.01980133)^(1/4)\n```\n\n转移概率表（年概率，4 位小数展示；代码中使用精确值，对角线 = 1 − 其余项）：\n\n| 从 \\ 到（策略 A：常规治疗） | 无并发症 | 卒中后 | 死亡 | 行和 |\n|---|---|---|---|---|\n| 无并发症 | 0.9702 | 0.0198 | 0.0100 | 1.0000 |\n| 卒中后 | 0.0000 | 0.9048 | 0.0952 | 1.0000 |\n| 死亡 | 0.0000 | 0.0000 | 1.0000 | 1.0000 |\n\n策略 B（干预）仅第一行不同：0.9761 / 0.0139 / 0.0100（卒中风险降 30%）。\n\n---\n\n### 第 3 步：构建转移矩阵\n\n**方法**：行 = 周期初状态，列 = 周期末状态；**每行和为 1**；死亡为吸收态（死亡行 = (0, 0, 1)）；对角线用「1 − 行内其余项」计算以保证行和恒为 1。构建后用 `rowsum` / `rowSums` 校验，并用矩阵幂检查 20 年后的状态分布（非负、和为 1）。\n\n**代码**：\n\n```stata\n* Stata：转移矩阵（策略 A；行：无并发症 / 卒中后 / 死亡）\nmatrix PA = (0.9701987, 0.0198013, 0.0100000 \\ ///\n             0.0000000, 0.9048374, 0.0951626 \\ ///\n             0.0000000, 0.0000000, 1.0000000)\nmatrix list PA, format(%9.4f)\n\n* Mata 校验：行和与 20 年状态分布\nmata:\nPA = (0.9701987, 0.0198013, 0.010 \\ 0, 0.9048374, 0.0951626 \\ 0, 0, 1)\nrowsum(PA)                     // 应为 (1, 1, 1)'\n(1, 0, 0) * PA^20              // 20 年后分布：非负且和为 1\nend\n```\n\n```r\n# R：转移矩阵（策略 A 与 B）\nPA <- matrix(c(0.9701987, 0.0198013, 0.0100000,\n               0.0000000, 0.9048374, 0.0951626,\n               0.0000000, 0.0000000, 1.0000000),\n             nrow = 3, byrow = TRUE)\nPB <- matrix(c(0.9760975, 0.0139025, 0.0100000,\n               0.0000000, 0.9048374, 0.0951626,\n               0.0000000, 0.0000000, 1.0000000),\n             nrow = 3, byrow = TRUE)\nrownames(PA) <- colnames(PA) <- c(\"无并发症\", \"卒中后\", \"死亡\")\nrownames(PB) <- colnames(PB) <- c(\"无并发症\", \"卒中后\", \"死亡\")\nrowSums(PA)                     # 1 1 1\nrowSums(PB)                     # 1 1 1\n# 20 年后分布（循环连乘；也可用 expm::`%^%`）\ntrace <- c(1, 0, 0)\nfor (i in 1:20) trace <- trace %*% PA\ntrace\n```\n\n---\n\n### 第 4 步：成本与健康效用参数\n\n**方法**：\n\n- **成本**：按研究视角（本示例为卫生体系视角，直接医疗成本）。示例：无并发症年成本 3000 元；干预追加药费 500 元/年（即干预组无并发症年成本 3500 元）；卒中后年成本 20000 元；死亡成本 0。可另加一次性事件成本（卒中急性期），本例从简。\n- **效用**：EQ-5D 等偏好基础工具测量（优先中国人群值集）。示例：无并发症 0.85、卒中后 0.62、死亡 0；QALY = Σ 状态效用 × 生存时间。\n- **区间**：无区间时常用点估计 ±20%（结构式敏感性分析）；有 95% CI 时按 CI 边界。由均值 / CI 反推 PSA 分布参数的矩法：beta（均值 m、标准差 sd）：α = m·(m(1−m)/sd² − 1)，β = (1−m)·(m(1−m)/sd² − 1)；gamma：shape = m²/sd²，scale = sd²/m；lognormal：sdlog = (ln UL − ln LL)/(2×1.96)。\n\n**代码**：\n\n```stata\n* Stata：由均值 / 区间反推 PSA 分布参数（矩法）\n* beta：效用 0.85、sd 0.03 → alpha、beta\ndisplay 0.85*(0.85*(1-0.85)/0.03^2 - 1)          // alpha ≈ 119.57\ndisplay (1-0.85)*(0.85*(1-0.85)/0.03^2 - 1)      // beta  ≈ 21.10\n* gamma：成本 3000、sd 600 → shape、scale\ndisplay 3000^2/600^2                              // shape = 25\ndisplay 600^2/3000                                // scale = 120\n* lognormal：率 95% CI [0.015, 0.03] → sdlog\ndisplay (ln(0.03) - ln(0.015)) / (2*1.96)         // sdlog ≈ 0.177\n```\n\n```r\n# R：同上（矩法）\nbeta_mom  <- function(m, sd) { k <- m*(1-m)/sd^2 - 1; c(alpha = m*k, beta = (1-m)*k) }\ngamma_mom <- function(m, sd) c(shape = m^2/sd^2, scale = sd^2/m)\nbeta_mom(0.85, 0.03)                     # alpha 119.57, beta 21.10\ngamma_mom(3000, 600)                     # shape 25, scale 120\n(log(0.03) - log(0.015)) / (2*1.96)      # sdlog 0.177\n```\n\n参数总表（本 Skill 全程使用；区间与分布为示例，实际取值以证据为准）：\n\n| 参数 | 基线值 | 区间（示例） | PSA 分布 | 来源 |\n|---|---|---|---|---|\n| 卒中年发生率（对照） | 0.020 | 0.015–0.025 | lognormal（sdlog≈0.18） | 队列 / 文献 |\n| 干预 RR | 0.70 | 0.56–0.84 | lognormal（sdlog≈0.10） | RCT / 荟萃 |\n| 背景死亡年概率 | 0.010 | ±20% | beta(24.74, 2449.26) | 生命表 |\n| 卒中后死亡年率 | 0.10 | 0.08–0.12 | lognormal（sdlog≈0.15） | 卒中登记 |\n| 无并发症年成本（元） | 3000 | 2400–3600 | gamma(25, 120) | 医院 / 医保数据 |\n| 干预追加药费（元/年） | 500 | 400–600 | gamma(25, 20) | 药品价格 |\n| 卒中后年成本（元） | 20000 | 16000–24000 | gamma(25, 800) | 病历 / 医保 |\n| 效用：无并发症 | 0.85 | 0.80–0.90 | beta(119.6, 21.1) | EQ-5D（中国值集） |\n| 效用：卒中后 | 0.62 | 0.50–0.72 | beta(57.8, 35.4) | EQ-5D 卒中队列 |\n\n---\n\n### 第 5 步：队列模拟（cohort simulation）\n\n**方法**：\n\n- **核心迭代**：trace(t+1) = trace(t) × P。数据行 1 为起始分布；行 _n（n = 1..T）即第 n 个周期的期初状态分布，配权重 1/(1+d)^(n−0.5)。\n- **奖励累积**：每周期成本 = Σ 状态人数 × 状态年成本；QALY 同理（死亡状态成本 / 效用为 0）。\n- **半周期校正**：假设事件在周期内均匀发生，用 (t−0.5) 的指数权重近似（相当于把期初计数折到周期中点）；不做校正会系统性高估成本与效果（本例约高 2.4%，即 ×√(1+d)）。\n- **贴现**：《中国药物经济学评价指南（2020）》要求成本与效果**同率贴现**，基例 5%，另作 3% 情景（本 Skill 同时给出 5% 与 3% 两套结果；敏感性分析可再加 0%）。\n\n#### 5.1 Stata 纯循环（完整代码）\n\n```stata\n* ============ 第 5 步：队列模拟（Stata 纯循环） ============\nlocal T = 30                     // 时间范围（年/周期）\n* 策略 A：常规治疗\nlocal p_hs1 = 1 - exp(-0.02)     // 无并发症→卒中（率 0.02/年）\nlocal p_hd1 = 0.010              // 无并发症→死亡\nlocal p_sd1 = 1 - exp(-0.10)     // 卒中后→死亡（率 0.10/年）\nlocal c_well1 = 3000             // 无并发症年成本\nlocal c_sick1 = 20000            // 卒中后年成本\nlocal u_well1 = 0.85             // 效用\nlocal u_sick1 = 0.62\n* 策略 B：干预（卒中风险降 30%）\nlocal p_hs2 = 1 - exp(-0.02*0.70)\nlocal p_hd2 = 0.010\nlocal p_sd2 = 1 - exp(-0.10)\nlocal c_well2 = 3500             // 无并发症年成本 + 500 元药费\nlocal c_sick2 = 20000\nlocal u_well2 = 0.85\nlocal u_sick2 = 0.62\n\nmatrix R = J(0, 4, .)            // 收集两策略结果：成本/QALY × (5%, 3%)\n\nforvalues s = 1/2 {\n    * ---- 状态占位（行 1..T+1；行 1 为起始分布） ----\n    clear\n    set obs `=`T'+1'\n    gen healthy = .\n    gen sick    = .\n    gen dead    = .\n    replace healthy = 1 in 1       // 起始：100% 无并发症\n    replace sick    = 0 in 1\n    replace dead    = 0 in 1\n    * ---- 转移循环：trace(t+1) = trace(t) × P ----\n    forvalues t = 2/`=`T'+1' {\n        replace healthy = healthy[`t'-1]*(1-`p_hs`s''-`p_hd`s'') in `t'\n        replace sick    = healthy[`t'-1]*`p_hs`s'' + sick[`t'-1]*(1-`p_sd`s'') in `t'\n        replace dead    = dead[`t'-1] + healthy[`t'-1]*`p_hd`s'' + sick[`t'-1]*`p_sd`s'' in `t'\n    }\n    * ---- 每周期成本与 QALY（期初状态） ----\n    gen cost_cyc = healthy*`c_well`s'' + sick*`c_sick`s''\n    gen qaly_cyc = healthy*`u_well`s'' + sick*`u_sick`s''\n    * ---- 贴现 5%：半周期校正权重 1/(1+d)^(n-0.5)，只累加第 1..T 周期 ----\n    gen w05 = 1/(1+0.05)^(_n-0.5) if _n <= `T'\n    gen cost05 = cost_cyc*w05\n    gen qaly05 = qaly_cyc*w05\n    qui sum cost05\n    local c05_`s' = r(sum)\n    qui sum qaly05\n    local q05_`s' = r(sum)\n    * ---- 贴现 3%（敏感性情景） ----\n    gen w03 = 1/(1+0.03)^(_n-0.5) if _n <= `T'\n    gen cost03 = cost_cyc*w03\n    gen qaly03 = qaly_cyc*w03\n    qui sum cost03\n    local c03_`s' = r(sum)\n    qui sum qaly03\n    local q03_`s' = r(sum)\n    * ---- 30 年死亡比例（行 T+1 的 dead） ----\n    local d30_`s' = dead[`=`T'+1']\n    matrix R = R \\ (`c05_`s'', `q05_`s'', `c03_`s'', `q03_`s'')\n}\nmatrix rownames R = A B\nmatrix colnames R = cost5 qaly5 cost3 qaly3\nmatrix list R, format(%12.2f)\ndisplay \"30 年死亡比例：A = \" %6.4f `d30_1' \"；B = \" %6.4f `d30_2'\n* 期望输出：A: cost5≈63186.10, qaly5≈10.7716；B: cost5≈63913.00, qaly5≈11.1113\n*           A: cost3≈80062.67, qaly3≈13.1935；B: cost3≈80588.47, qaly3≈13.6758\n*           30 年死亡比例：A≈0.4894，B≈0.4314\n```\n\n#### 5.2 R 从头实现（完整代码）\n\n```r\n# ============ 第 5 步：队列模拟（R 从头写） ============\nT <- 30\np_hsA <- 1 - exp(-0.02)          # 无并发症→卒中（对照）\np_hsB <- 1 - exp(-0.02*0.70)     # 无并发症→卒中（干预）\np_hd  <- 0.010                   # 背景死亡\np_sd  <- 1 - exp(-0.10)          # 卒中后死亡\nPA <- matrix(c(1-p_hsA-p_hd, p_hsA, p_hd,\n               0,           1-p_sd, p_sd,\n               0,           0,      1), nrow = 3, byrow = TRUE)\nPB <- matrix(c(1-p_hsB-p_hd, p_hsB, p_hd,\n               0,           1-p_sd, p_sd,\n               0,           0,      1), nrow = 3, byrow = TRUE)\n\n# 队列模拟：trace(t+1) = trace(t) %*% P；奖励按周期期初状态、权重 1/(1+d)^(t-0.5)\nsim <- function(P, c_well, c_sick, u_well, u_sick, dr, T = 30) {\n  trace <- matrix(0, nrow = T + 1, ncol = 3)\n  trace[1, ] <- c(1, 0, 0)                      # 起始：100% 无并发症\n  for (t in seq_len(T)) trace[t + 1, ] <- trace[t, ] %*% P\n  w   <- 1 / (1 + dr)^(seq_len(T) - 0.5)        # 半周期校正 + 贴现权重\n  cyc <- trace[seq_len(T), ]                    # 第 1..T 周期的期初分布\n  cost <- sum((cyc[, 1] * c_well + cyc[, 2] * c_sick) * w)\n  qaly <- sum((cyc[, 1] * u_well + cyc[, 2] * u_sick) * w)\n  c(cost = cost, qaly = qaly, p_dead = trace[T + 1, 3])\n}\n\nresA5 <- sim(PA, 3000, 20000, 0.85, 0.62, 0.05, T)\nresB5 <- sim(PB, 3500, 20000, 0.85, 0.62, 0.05, T)\nresA3 <- sim(PA, 3000, 20000, 0.85, 0.62, 0.03, T)\nresB3 <- sim(PB, 3500, 20000, 0.85, 0.62, 0.03, T)\nround(rbind(\"A(5%)\" = resA5, \"B(5%)\" = resB5,\n            \"A(3%)\" = resA3, \"B(3%)\" = resB3), 2)\n# 期望输出（与 5.1 的 Stata 结果一致）：\n#   A(5%): cost=63186.10, qaly=10.77, p_dead=0.4894\n#   B(5%): cost=63913.00, qaly=11.11, p_dead=0.4314\n#   A(3%): cost=80062.67, qaly=13.19；B(3%): cost=80588.47, qaly=13.68\n```\n\n#### 5.3 R heemod 等价实现\n\n```r\n# ============ 第 5 步（heemod 等价实现，heemod ≥ 1.0 API） ============\nlibrary(heemod)\n\np_hsA <- 1 - exp(-0.02); p_hsB <- 1 - exp(-0.02*0.70)\np_hd  <- 0.010; p_sd <- 1 - exp(-0.10)\n\n# 转移矩阵：C = 行内其余概率的补数（1 - 其他项之和），行和自动为 1\nmatA <- define_transition(\n  C, p_hsA, p_hd,\n  0, C,     p_sd,\n  0, 0,     C)\nmatB <- define_transition(\n  C, p_hsB, p_hd,\n  0, C,     p_sd,\n  0, 0,     C)\n\n# 状态值：discount() 在模型内按周期贴现；dispatch_strategy 区分策略成本\n# （策略名 std / intv 须与 run_model 中的名字一致）\nstate_well <- define_state(\n  cost    = discount(dispatch_strategy(std = 3000, intv = 3500), .05),\n  utility = 0.85)\nstate_sick <- define_state(\n  cost    = discount(20000, .05),\n  utility = 0.62)\nstate_dead <- define_state(\n  cost    = 0,\n  utility = 0)\n\nstratA <- define_strategy(transition = matA,\n                          state_well, state_sick, state_dead)\nstratB <- define_strategy(transition = matB,\n                          state_well, state_sick, state_dead)\n\nres <- run_model(\n  std  = stratA,\n  intv = stratB,\n  cycles = 30,\n  init   = c(1, 0, 0),      # 按 1 人队列（默认 1000 人）\n  cost   = cost,            # 成本取状态值 cost\n  effect = utility,         # 效果取状态值 utility（QALY）\n  method = \"life-table\")    # 半周期校正；默认 \"beginning\" 不做半周期校正\n\nsummary(res, threshold = c(100000, 300000))  # 阈值：1–3 倍人均 GDP（元/QALY）\nget_counts(res)                              # 各周期各状态人数\nplot(res, type = \"counts\", panels = \"by_strategy\")   # 状态分布图\nplot(res, type = \"ce\")                       # 成本-效果平面与效率前沿\n# 注：heemod 的贴现时点约定（discount() 默认首周期不打折）与 5.1/5.2 的\n# (t-0.5) 约定略有差异，结果会有小幅出入；报告时统一约定并注明实现。\n# heemod 1.0 起 run_model 已无 discount 参数，贴现写在 define_state 的\n# discount() 中（旧版 API 见 help(run_model)）。\n```\n\n---\n\n### 第 6 步：计算 ICER\n\n**方法**：\n\n- **ICER = ΔC / ΔE =（干预成本 − 对照成本）/（干预 QALY − 对照 QALY）**。\n- **阈值**：《中国药物经济学评价指南（2020）》以 1–3 倍人均 GDP 作为阈值参考区间（2020 年中国人均 GDP ≈ 7.2 万元；用研究当年最新统计，本例取人均 GDP ≈ 10 万元 → 阈值区间 10 万–30 万元/QALY）。ICER < 1×GDP：极具成本效果；1–3×：具成本效果；> 3×：不具成本效果。\n- **ICER 象限与决策规则**（ΔC 为纵轴、ΔE 为横轴）：\n  - 东北（ΔC>0, ΔE>0）：更贵更有效 → ICER < λ 采纳，≥ λ 拒绝；\n  - 东南（ΔC<0, ΔE>0）：更便宜更有效 → **占优（dominant），采纳**；\n  - 西北（ΔC>0, ΔE<0）：更贵更差 → **被占优（dominated），拒绝**；\n  - 西南（ΔC<0, ΔE<0）：更便宜更差 → ICER（为正）> λ 时采纳（每节省的钱高于损失 QALY 的价值），≤ λ 拒绝。\n  - **负 ICER 不能直接按数值比大小**，必须先判象限；ΔE ≈ 0 时 ICER 不稳定，改用净货币效益 **NMB = λ·ΔE − ΔC**（NMB > 0 采纳）。\n\n**代码**（沿用第 5 步的矩阵 R 与 resA5/resB5）：\n\n```stata\n* ---------- ICER 与 NMB（5% 贴现；R 第 1、2 列） ----------\ndisplay \"策略 A：成本 = \" %9.2f R[1,1] \" 元；QALY = \" %6.4f R[1,2]\ndisplay \"策略 B：成本 = \" %9.2f R[2,1] \" 元；QALY = \" %6.4f R[2,2]\ndisplay \"ΔC = \" %9.2f (R[2,1]-R[1,1]) \" 元；ΔE = \" %6.4f (R[2,2]-R[1,2]) \" QALY\"\ndisplay \"ICER(5%) = \" %9.2f ((R[2,1]-R[1,1])/(R[2,2]-R[1,2])) \" 元/QALY\"\ndisplay \"ICER(3%) = \" %9.2f ((R[2,3]-R[1,3])/(R[2,4]-R[1,4])) \" 元/QALY\"\n* NMB（λ = 100000，1 倍人均 GDP）\nlocal lam = 100000\ndisplay \"NMB_B - NMB_A (λ=100000) = \" %9.2f (`lam'*(R[2,2]-R[1,2]) - (R[2,1]-R[1,1]))\n* 期望输出：ΔC≈726.90，ΔE≈0.3397，ICER(5%)≈2139.80，ICER(3%)≈1090.04\n*           NMB 差 ≈ 33243（λ=100000）\n```\n\n```r\n# ---------- ICER 与 NMB ----------\ndC   <- resB5[\"cost\"] - resA5[\"cost\"]\ndE   <- resB5[\"qaly\"] - resA5[\"qaly\"]\nc(dC = dC, dE = dE, ICER = dC / dE)   # 期望：dC≈726.90, dE≈0.3397, ICER≈2139.80\n\nlam <- 100000                          # 阈值（1 倍人均 GDP）\nNMB_A <- lam * resA5[\"qaly\"] - resA5[\"cost\"]\nNMB_B <- lam * resB5[\"qaly\"] - resB5[\"cost\"]\nc(NMB_A = NMB_A, NMB_B = NMB_B, dNMB = NMB_B - NMB_A)   # dNMB≈33243\n\n# 象限与决策规则\nif (dE > 0 & dC < 0) {\n  \"东南象限：占优（更便宜且更有效）→ 采纳\"\n} else if (dE < 0 & dC > 0) {\n  \"西北象限：被占优（更贵且更差）→ 拒绝\"\n} else if (dE > 0) {\n  ifelse(dC/dE < lam, \"东北象限：ICER < λ → 采纳\", \"东北象限：ICER ≥ λ → 拒绝\")\n} else {\n  ifelse(dC/dE > lam,\n         \"西南象限：ICER > λ（节省的钱高于损失 QALY 的价值）→ 可采纳\",\n         \"西南象限：ICER ≤ λ → 拒绝\")\n}\n# 本例期望输出：dC>0 且 dE>0 → 东北象限，ICER≈2140 < 100000 → 采纳\n```\n\n---\n\n### 第 7 步：敏感性分析\n\n**方法**：\n\n- **单因素（tornado）**：每个参数单独取低 / 高值（±20% 或 95% CI 边界），其余固定，重算 ICER；两臂共用的参数（背景死亡、卒中后死亡、卒中后成本、效用）两臂同步变动。按 ICER 变化范围排序作图；负值表示该情景下落入占优或其他象限，报告中注明符号含义。\n- **PSA 分布选择依据**：概率参数（0–1）→ **beta**；率与 RR（>0）→ **lognormal**（R 用 rlnorm / exp(rnorm)，Stata 用 exp(rnormal)）；成本（>0，偏态）→ **gamma**；效用（0–1）→ **beta**。参数由第 4 步矩法给出。循环 1000 次蒙特卡洛，输出 ΔC–ΔE 散点图与 CEAC（P(ΔC − λ·ΔE < 0) 对 λ 的曲线）。\n\n#### 7.1 单因素敏感性分析（tornado）\n\n```stata\n* ============ 第 7.1 步：tornado（Stata/Mata） ============\n* 把第 5 步的循环封装成 Mata 函数（敏感性分析需反复调用，Mata 更快）\ncapture mata mata drop markov_ce()\nmata:\nreal colvector markov_ce(real scalar phs, real scalar phd, real scalar psd,\n                         real scalar cw, real scalar cs,\n                         real scalar uw, real scalar us,\n                         real scalar d, real scalar T)\n{\n    real matrix  P, trace, cyc\n    real colvector w, c, q\n    P = ((1-phs-phd, phs, phd) \\ (0, 1-psd, psd) \\ (0, 0, 1))\n    trace = J(T+1, 3, 0)\n    trace[1, 1] = 1\n    for (t = 1; t <= T; t++) trace[t+1, .] = trace[t, .] * P\n    w   = 1 :/ (1+d) :^ ((1::T) :- 0.5)\n    cyc = trace[1..T, .]\n    c   = colsum((cyc[,1]*cw + cyc[,2]*cs) :* w)\n    q   = colsum((cyc[,1]*uw + cyc[,2]*us) :* w)\n    return((c \\ q))\n}\nend\n\nmata:\nphsA = 1-exp(-0.02)\nphsB = 1-exp(-0.014)\nphd  = 0.010\npsd  = 1-exp(-0.10)\nbA = (phsA, phd, psd, 3000, 20000, 0.85, 0.62)    // 对照臂基线（1×7）\nbB = (phsB, phd, psd, 3500, 20000, 0.85, 0.62)    // 干预臂基线\nnm = (\"对照卒中率\", \"干预卒中率\", \"背景死亡\", \"卒中后死亡\",\n      \"无并发症成本\", \"卒中后成本\", \"效用-无并发症\", \"效用-卒中后\")\npair = J(0, 3, .)                                  // 参数序号、低值 ICER、高值 ICER\nfor (i = 1; i <= 8; i++) {\n    loA = bA; loB = bB; hiA = bA; hiB = bB\n    if (i == 1) {\n        loA[1] = bA[1]*0.8; hiA[1] = bA[1]*1.2      // 仅对照臂变\n    }\n    else if (i == 2) {\n        loB[1] = bB[1]*0.8; hiB[1] = bB[1]*1.2      // 仅干预臂变\n    }\n    else {\n        j = i - 1                                    // 共用参数：两臂同变\n        loA[j] = bA[j]*0.8; loB[j] = bB[j]*0.8\n        hiA[j] = bA[j]*1.2; hiB[j] = bB[j]*1.2\n    }\n    ra = markov_ce(loA[1], loA[2], loA[3], loA[4], loA[5], loA[6], loA[7], 0.05, 30)\n    rb = markov_ce(loB[1], loB[2], loB[3], loB[4], loB[5], loB[6], loB[7], 0.05, 30)\n    icer_lo = (rb[1]-ra[1]) / (rb[2]-ra[2])\n    ra = markov_ce(hiA[1], hiA[2], hiA[3], hiA[4], hiA[5], hiA[6], hiA[7], 0.05, 30)\n    rb = markov_ce(hiB[1], hiB[2], hiB[3], hiB[4], hiB[5], hiB[6], hiB[7], 0.05, 30)\n    icer_hi = (rb[1]-ra[1]) / (rb[2]-ra[2])\n    pair = pair \\ (i, icer_lo, icer_hi)\n}\nra = markov_ce(bA[1], bA[2], bA[3], bA[4], bA[5], bA[6], bA[7], 0.05, 30)\nrb = markov_ce(bB[1], bB[2], bB[3], bB[4], bB[5], bB[6], bB[7], 0.05, 30)\nicer0 = (rb[1]-ra[1]) / (rb[2]-ra[2])\n(nm, pair[,2], pair[,3])                           // 打印结果表\nst_matrix(\"TOR\", (pair[,2], pair[,3]))\nst_numscalar(\"r_icer0\", icer0)\nend\n* 期望输出（基例 ICER ≈ 2140）：对照卒中率变化最大（ICER 约 -4734 ~ 37574）；\n* 负值表示该情景下干预占优或落入其他象限，绘图与报告需注明。\n\n* ---- tornado 图（横条 + 基线竖线；y 轴序号对应上述参数顺序） ----\nsvmat TOR\ngen y = _n\ntwoway (rcap TOR1 TOR2 y, horizontal lcolor(navy)) ///\n    (scatter y TOR1) (scatter y TOR2), ///\n    xline(`=scalar(r_icer0)', lpattern(dash) lcolor(red)) ///\n    ylabel(1(1)8) xtitle(\"ICER（元/QALY）\") ytitle(\"参数序号\") ///\n    legend(off) name(tornado, replace)\n```\n\n```r\n# ============ 第 7.1 步：tornado（R） ============\nphsA <- 1-exp(-0.02); phsB <- 1-exp(-0.014)\nphd <- 0.010; psd <- 1-exp(-0.10)\nbA <- c(phsA, phd, psd, 3000, 20000, 0.85, 0.62)   # 对照臂基线\nbB <- c(phsB, phd, psd, 3500, 20000, 0.85, 0.62)   # 干预臂基线\nnms <- c(\"对照卒中率\", \"干预卒中率\", \"背景死亡\", \"卒中后死亡\",\n         \"无并发症成本\", \"卒中后成本\", \"效用-无并发症\", \"效用-卒中后\")\n\nmakeP <- function(p) matrix(c(1-p[1]-p[2], p[1], p[2],\n                              0, 1-p[3], p[3],\n                              0, 0, 1), nrow = 3, byrow = TRUE)\npair_icer <- function(pA, pB) {                    # 复用第 5.2 步的 sim()\n  rA <- sim(makeP(pA), pA[4], pA[5], pA[6], pA[7], 0.05, 30)\n  rB <- sim(makeP(pB), pB[4], pB[5], pB[6], pB[7], 0.05, 30)\n  (rB[\"cost\"] - rA[\"cost\"]) / (rB[\"qaly\"] - rA[\"qaly\"])\n}\nicer0 <- pair_icer(bA, bB)                         # ≈ 2139.80\n\ntor <- t(sapply(seq_along(nms), function(i) {\n  loA <- bA; loB <- bB; hiA <- bA; hiB <- bB\n  if (i == 1)      { loA[1] <- bA[1]*.8; hiA[1] <- bA[1]*1.2 }\n  else if (i == 2) { loB[1] <- bB[1]*.8; hiB[1] <- bB[1]*1.2 }\n  else {\n    j <- i - 1\n    loA[j] <- bA[j]*.8; loB[j] <- bB[j]*.8\n    hiA[j] <- bA[j]*1.2; hiB[j] <- bB[j]*1.2\n  }\n  c(lo = pair_icer(loA, loB), hi = pair_icer(hiA, hiB))\n}))\ntor_df <- data.frame(param = nms, lo = tor[, \"lo\"], hi = tor[, \"hi\"])\ntor_df\n\n# 基础 R tornado 图（横条 + 基线竖线）\ndotchart(tor_df$lo, labels = tor_df$param,\n         xlim = range(c(tor_df$lo, tor_df$hi, icer0)))\nsegments(tor_df$lo, seq_along(nms), tor_df$hi, seq_along(nms))\nabline(v = icer0, lty = 2, col = \"red\")\n# 负 ICER：表示该情景下落入了占优 / 西南象限，见第 6 步象限规则，勿直接比较大小\n```\n\n#### 7.2 概率敏感性分析（PSA）\n\n```stata\n* ============ 第 7.2 步：PSA（Stata：rbeta/rgamma/rnormal 循环） ============\n* 分布依据：概率→beta；率/RR→lognormal（exp(rnormal)）；成本→gamma；效用→beta\n* 注：1000 次 × 30 周期的 .do 循环较慢（数分钟）；可改调第 7.1 步的\n* Mata markov_ce() 提速（逻辑完全相同）。\nset seed 2026\nlocal N = 1000\nlocal T = 30\nmatrix PSA = J(0, 4, .)                    // 每行：cA qA cB qB\nforvalues i = 1/`N' {\n    * ---- 参数抽样 ----\n    local p_hsA = 1 - exp(-exp(rnormal(ln(0.02), 0.20)))\n    local p_hsB = 1 - exp(-exp(rnormal(ln(0.02), 0.20))*exp(rnormal(ln(0.70), 0.10)))\n    local p_hd  = rbeta(24.74, 2449.26)\n    local p_sd  = 1 - exp(-exp(rnormal(ln(0.10), 0.15)))\n    local cA0   = rgamma(25, 120)\n    local cB0   = `cA0' + rgamma(25, 20)\n    local cS    = rgamma(25, 800)\n    local uW    = rbeta(119.6, 21.1)\n    local uS    = rbeta(57.8, 35.4)\n    * ---- 对照臂队列模拟（同第 5.1 步循环） ----\n    clear\n    set obs `=`T'+1'\n    gen healthy = .\n    gen sick = .\n    gen dead = .\n    replace healthy = 1 in 1\n    replace sick = 0 in 1\n    replace dead = 0 in 1\n    forvalues t = 2/`=`T'+1' {\n        replace healthy = healthy[`t'-1]*(1-`p_hsA'-`p_hd') in `t'\n        replace sick    = healthy[`t'-1]*`p_hsA' + sick[`t'-1]*(1-`p_sd') in `t'\n        replace dead    = dead[`t'-1] + healthy[`t'-1]*`p_hd' + sick[`t'-1]*`p_sd' in `t'\n    }\n    gen w = 1/(1+0.05)^(_n-0.5) if _n <= `T'\n    gen cA_c = (healthy*`cA0' + sick*`cS')*w\n    gen qA_c = (healthy*`uW'  + sick*`uS')*w\n    qui sum cA_c\n    local cA_t = r(sum)\n    qui sum qA_c\n    local qA_t = r(sum)\n    * ---- 干预臂队列模拟 ----\n    replace healthy = 1 in 1\n    replace sick = 0 in 1\n    replace dead = 0 in 1\n    forvalues t = 2/`=`T'+1' {\n        replace healthy = healthy[`t'-1]*(1-`p_hsB'-`p_hd') in `t'\n        replace sick    = healthy[`t'-1]*`p_hsB' + sick[`t'-1]*(1-`p_sd') in `t'\n        replace dead    = dead[`t'-1] + healthy[`t'-1]*`p_hd' + sick[`t'-1]*`p_sd' in `t'\n    }\n    gen cB_c = (healthy*`cB0' + sick*`cS')*w\n    gen qB_c = (healthy*`uW'  + sick*`uS')*w\n    qui sum cB_c\n    local cB_t = r(sum)\n    qui sum qB_c\n    local qB_t = r(sum)\n    matrix PSA = PSA \\ (`cA_t', `qA_t', `cB_t', `qB_t')\n}\nmatrix colnames PSA = cA qA cB qB\n\n* ---- 成本-效果散点图 ----\nsvmat PSA, names(col)\ngen dC = cB - cA\ngen dE = qB - qA\ntwoway (scatter dC dE, msize(tiny) mcolor(gs8%35)), ///\n    yline(0, lpattern(dash)) xline(0, lpattern(dash)) ///\n    xtitle(\"增量 QALY\") ytitle(\"增量成本（元）\") ///\n    title(\"PSA 成本-效果散点图 (n=1000)\") name(ce_scatter, replace)\n\n* ---- CEAC ----\nmatrix CEAC = J(0, 2, .)\nforvalues lam = 0(20000)300000 {\n    count if dC - `lam'*dE < 0\n    local pce = r(N)/_N\n    matrix CEAC = CEAC \\ (`lam', `pce')\n}\nsvmat CEAC\nline CEAC2 CEAC1, xtitle(\"阈值 λ（元/QALY）\") ///\n    ytitle(\"P(具有成本效果)\") name(ceac, replace)\n* 期望（示意，与随机种子有关）：本示例各阈值下 P(CE) > 0.99，P(占优) ≈ 0.36\n```\n\n```r\n# ============ 第 7.2 步：PSA（R：基础循环 + 绘图） ============\nset.seed(2026)\nN <- 1000\npsa <- matrix(NA, nrow = N, ncol = 4,\n              dimnames = list(NULL, c(\"cA\", \"qA\", \"cB\", \"qB\")))\nfor (i in 1:N) {\n  r_hs <- rlnorm(1, meanlog = log(0.02), sdlog = 0.20)   # 卒中发生率\n  rr   <- rlnorm(1, meanlog = log(0.70), sdlog = 0.10)   # 干预 RR\n  p_hsA <- 1 - exp(-r_hs)\n  p_hsB <- 1 - exp(-r_hs * rr)\n  p_hd  <- rbeta(1, 24.74, 2449.26)                      # 背景死亡概率\n  p_sd  <- 1 - exp(-rlnorm(1, meanlog = log(0.10), sdlog = 0.15))\n  cA0   <- rgamma(1, shape = 25, scale = 120)            # 注意：scale= 必须显式\n  cB0   <- cA0 + rgamma(1, shape = 25, scale = 20)       #（默认第二参数是 rate）\n  cS    <- rgamma(1, shape = 25, scale = 800)\n  uW    <- rbeta(1, 119.6, 21.1)\n  uS    <- rbeta(1, 57.8, 35.4)\n  PAi <- matrix(c(1-p_hsA-p_hd, p_hsA, p_hd,\n                  0, 1-p_sd, p_sd,\n                  0, 0, 1), nrow = 3, byrow = TRUE)\n  PBi <- matrix(c(1-p_hsB-p_hd, p_hsB, p_hd,\n                  0, 1-p_sd, p_sd,\n                  0, 0, 1), nrow = 3, byrow = TRUE)\n  rA <- sim(PAi, cA0, cS, uW, uS, 0.05, 30)              # 复用第 5.2 步的 sim()\n  rB <- sim(PBi, cB0, cS, uW, uS, 0.05, 30)\n  psa[i, ] <- c(rA[\"cost\"], rA[\"qaly\"], rB[\"cost\"], rB[\"qaly\"])\n}\nhead(psa)\n\ndC <- psa[, \"cB\"] - psa[, \"cA\"]\ndE <- psa[, \"qB\"] - psa[, \"qA\"]\n\n# 成本-效果散点图（含象限线与阈值线）\nplot(dE, dC, xlab = \"增量 QALY\", ylab = \"增量成本（元）\",\n     main = \"PSA 成本-效果散点图 (n = 1000)\",\n     pch = 16, col = rgb(0, 0, 0, 0.25))\nabline(h = 0, lty = 2); abline(v = 0, lty = 2)\nabline(a = 0, b = 100000, col = \"red\")   # λ = 100000 元/QALY 阈值线\ntext(quantile(dE, 0.05), quantile(dC, 0.95),\n     paste0(\"P(占优) = \", round(mean(dC < 0 & dE > 0), 3)))\n\n# CEAC\nlam_grid <- seq(0, 300000, by = 5000)\nceac <- sapply(lam_grid, function(l) mean(dC - l*dE < 0))\nplot(lam_grid, ceac, type = \"l\", xlab = \"阈值 λ（元/QALY）\",\n     ylab = \"具有成本效果的概率\", main = \"CEAC\")\nabline(h = 0.5, lty = 3); abline(h = 0.95, lty = 3)\n# 期望（示意）：本示例各阈值下 P(CE) > 0.99，P(占优) ≈ 0.36；\n# heemod 集成版 PSA 见 vignette(\"e-probabilistic\", \"heemod\")（define_psa + run_psa）\n```\n\n---\n\n### 第 8 步：报告与校验\n\n**方法**：\n\n- **结果表**：两策略并列报总成本、总 QALY，再报 ΔC、ΔE、ICER，注明贴现率与阈值；本示例（5% 贴现）ICER ≈ 2,140 元/QALY < 10 万元，结论为「极具成本效果」。\n- **ISPOR-SMDM 建模良好实践要点**（Caro et al. 2012）：① 概念模型——明确问题、视角、人群、干预、时间范围，状态互斥穷尽且有临床意义；② 结构假设——简化需合理并与临床路径一致；③ 数据——参数识别、估计与来源透明，换算（率→概率、币值/时间通胀）记录在案；④ 一致性与质量——内部一致性（行和、极端值、半周期校正、与独立实现交叉验证）与 face validity；⑤ 不确定性——单因素 + PSA，报告分布与相关性假设；⑥ 报告透明——按 CHEERS 2022 清单完整披露。\n- **模型校验清单**：face validity（临床专家确认状态、箭头方向与量级）；内部一致性（行和 = 1；极端值：卒中率 → 0 时两臂相同、死亡率 = 1 时第 1 周期后全灭、效用全 1 时 QALY = 贴现生命年）；交叉验证（Stata / R / heemod 结果互相印证，差异仅为贴现时点约定）；外部验证（与已发表模型或登记数据对照）。\n\n**代码**（极端值检验；Stata 调用第 7.1 步的 markov_ce()）：\n\n```stata\nmata:\n// ① 卒中率 → 0：两臂 QALY 应完全相同\nmarkov_ce(0, 0.010, 1-exp(-0.10), 3000, 20000, 0.85, 0.62, 0.05, 30)[2]\nmarkov_ce(0, 0.010, 1-exp(-0.10), 3500, 20000, 0.85, 0.62, 0.05, 30)[2]\n//   期望输出：两值相同（≈ 12.0320）\n// ② 卒中率 = 0 且死亡率 = 1：第 1 周期后全灭，仅剩第 1 周期奖励\nmarkov_ce(0, 1, 1, 3000, 20000, 0.85, 0.62, 0.05, 30)\n//   期望输出：cost ≈ 2927.70（3000×0.9759），qaly ≈ 0.8295（0.85×0.9759）\n// ③ 效用全 = 1：QALY 应等于贴现生命年（≈ 13.0549）\nmarkov_ce(1-exp(-0.02), 0.010, 1-exp(-0.10), 0, 0, 1, 1, 0.05, 30)[2]\nend\n```\n\n```r\n# R：同样的极端值检验（复用 sim() 与 makeP()）\n# ① 卒中率 → 0：两臂应完全相同\nP0 <- makeP(c(0, phd, psd))\nr01 <- sim(P0, 3000, 20000, 0.85, 0.62, 0.05)\nr02 <- sim(P0, 3500, 20000, 0.85, 0.62, 0.05)\nround(c(qaly_A = r01[\"qaly\"], qaly_B = r02[\"qaly\"], equal = r01[\"qaly\"] == r02[\"qaly\"]), 4)\n# ② 卒中率 = 0 且死亡率 = 1：第 1 周期后全灭\nsim(makeP(c(0, 1, 1)), 3000, 20000, 0.85, 0.62, 0.05)\n# ③ 效用全 = 1：QALY = 贴现生命年\nsim(makeP(c(phsA, phd, psd)), 0, 0, 1, 1, 0.05)[\"qaly\"]\n```\n\n结果表模板（本示例的实际数值，5% 贴现）：\n\n| 指标 | 策略 A（常规） | 策略 B（干预） | 差值（B − A） |\n|---|---|---|---|\n| 总成本（元） | 63,186 | 63,913 | +727 |\n| 总 QALY | 10.77 | 11.11 | +0.34 |\n| ICER（元/QALY） | — | — | 2,140 |\n| 总成本（3% 贴现，元） | 80,063 | 80,588 | +526 |\n| 总 QALY（3% 贴现） | 13.19 | 13.68 | +0.48 |\n| ICER（3%，元/QALY） | — | — | 1,090 |\n| 30 年死亡比例 | 0.489 | 0.431 | −0.058 |\n\n结论：ICER（基例 5%）≈ 2,140 元/QALY，远低于 1 倍人均 GDP（≈ 10 万元），干预极具成本效果；PSA 显示 P(CE) > 0.99、P(占优) ≈ 0.36，结论稳健。\n\n---\n\n## 常见错误清单\n\n- [ ] 把「率」直接当「概率」用（0.02/年 ≠ 0.02；正确 p = 1 − exp(−0.02) ≈ 0.0198；率越高偏差越大）\n- [ ] 转移矩阵行和 ≠ 1（对角线应 = 1 − 行内其余项；死亡行必须为 (0, 0, 1) 吸收态）\n- [ ] 忘记半周期校正（权重用了 (t−1) 而非 (t−0.5)，系统性高估成本与效果约 ×√(1+d)）或重复校正\n- [ ] 贴现只贴成本不贴效果（指南要求成本与效果同率贴现；敏感性另做 0%/3% 情景）\n- [ ] 循环次数与时间范围不一致（T 个周期需 T 次转移、数据 T+1 行；奖励只累加第 1..T 行）\n- [ ] 「期初 vs 期末」状态取值与权重错位（第 t 周期的奖励必须配第 t 周期期初的状态行）\n- [ ] ICER 解读错误：ΔE < 0 时把负 ICER 直接比大小；应先判象限（东南占优 / 西北被占优 / 西南用「ICER > λ 才采纳」规则）\n- [ ] ΔE ≈ 0 时仍报告 ICER（数值不稳定），应改用 NMB = λ·ΔE − ΔC\n- [ ] PSA 分布选错：概率用 normal（可能 < 0 或 > 1）、成本用 normal（可能 < 0）、RR 用 gamma / 率用 beta（应 lognormal / beta）\n- [ ] R 的 `rgamma()` 忘记写 `scale =`（默认第二参数是 rate，结果量级全错）\n- [ ] Stata `rgamma(a, b)` 与 R 参数含义不同（Stata 第二参数是 scale，R 是 rate）——两边代码已按各自语法写对，移植时勿混用\n- [ ] R `matrix(..., byrow = TRUE)` 忘记 byrow 导致矩阵转置（行和检查仍会通过，但模型方向全错）\n- [ ] 时间范围太短（队列尚未进入吸收态），成本与效果被截断\n- [ ] 忘记 `set seed` / `set.seed`，PSA 结果不可复现\n- [ ] 参数无来源、无区间、无分布依据；不做 face validity 与极端值检验\n- [ ] Stata：replace 的范围（in 行号）写错只更新了一行；循环边界差 1（T 个周期要循环到第 T+1 行）；矩阵追加忘记 J(0, k, .) 初始化\n- [ ] heemod：`run_model` 未指定 `effect = utility`；未用 `method = \"life-table\"` 却说做了半周期校正；`dispatch_strategy` 的策略名与 `run_model` 的名字不一致\n\n## 输出格式（分析报告模板）\n\n```\n【模型结构】\n问题、视角（卫生体系）、人群、干预与对照、时间范围；状态列表与状态图；周期长度；起始分布；关键结构假设。\n\n【参数】\n表 2：转移概率（含来源与率→概率换算）；表 3：成本与效用（点估计、区间、PSA 分布、来源）。\n\n【基例结果】\n表 4：两策略总成本、总 QALY、ΔC、ΔE、ICER（注明贴现率 3%/5% 与半周期校正）；阈值区间（1–3 倍人均 GDP）与判定。\n\n【敏感性分析】\n表 5：tornado（每参数低/高值与对应 ICER）；图 1：PSA 散点图（1000 次，注明分布与相关性假设）；图 2：CEAC。\n\n【校验与局限】\nface validity、内部一致性（行和、极端值、交叉验证结果）；与已发表研究的差异说明；局限（结构简化、参数外推、未纳入的疾病进程）。\n```\n\n## 使用提示\n\n- 命令与选项随版本更新：Stata 运行 `help matrix` / `help forvalues` / Mata 手册核对；R 侧 `help(run_model)`、`help(discount)`、`help(define_transition)` 核对 heemod API（本 Skill 按 heemod ≥ 1.0 新版 API 编写，贴现写在 `define_state` 的 `discount()` 中）。\n- 贴现与半周期校正的约定（期初计数 + (t−0.5) 权重）必须写进报告；与 heemod 等其他软件对照时先统一贴现时点与 `method` 约定，再比较结果。\n- 转移概率在代码中一律用精确值计算（行和自动为 1），表格只展示 4 位小数。\n- 阈值用研究当年最新人均 GDP 的 1–3 倍区间，并配合 CEAC 报告，而非只做单点比较。\n- 本 Skill 提供方法与代码模板；状态结构、参数取值与临床假设由研究者依据证据确定并负责。\n\n---\n\n## 引用 / Citation\n\n中文：杜思娴. Markov 模型助手：卫生经济学成本-效果分析 Skill [EB/OL]. (2026). https://naturedu.github.io/socialmedicine/.\n\nEnglish: Du, S. (2026). *Markov Model Assistant: Cost-effectiveness analysis in health economics skill* [Skill definition]. https://naturedu.github.io/socialmedicine/.\n\n方法学参考：\n\n- 刘国恩, 胡善联, 吴久鸿, 等. 中国药物经济学评价指南（2020）[S]. 北京: 中国市场出版社, 2020.（China Guidelines for Pharmacoeconomic Evaluations, 2020）\n- Briggs, A., Sculpher, M., & Claxton, K. (2006). *Decision Modelling for Health Economic Evaluation*. Oxford: Oxford University Press.\n- Caro, J. J., Briggs, A. H., Siebert, U., & Kuntz, K. M. (2012). Modeling good research practices—overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-1. *Value in Health*, 15(6), 796–803.\n- Husereau, D., Drummond, M., Augustovski, F., et al. (2022). Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. *Value in Health*, 25(1), 3–9.\n- Filipović-Pierucci, A., Zarca, K., & Durand-Zaleski, I. (2017). Markov models for health economic evaluations: The R package heemod. *arXiv preprint arXiv:1702.03252*.\n","kind":"方法 Skill","locator":"方法手册","license":"MIT（仓库原有技能）","verified":"原有公开仓库"}},{"id":"studio-library:skill-meta-analysis-skill","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Skill：Meta 分析助手（系统评价效应合并，Stata / R）","summary":"# Skill：Meta 分析助手（系统评价效应合并，Stata / R）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n一份可复用的「系统评价与 Meta 分析」工作流：从 PICO 问题、PRISMA 检索—筛选—纳入流程与数据提取，到 Stata（官方 `meta` 命令族；SSC 的 `metan` / `metareg` / `metabias`）与 R（`meta`、`metafor`）的效应量计算（OR/RR 对数尺度、MD/Hedges' g）、固定/随机效应合并、森林图、异质性检验与亚组/meta 回归、发表偏倚评估，以及 PRISMA 2020 报告与 GRADE 证据分级。**每一步都给出「方法 + 代码」**；一组自洽的虚构示例数据（8 项 RCT，二分类结局 OR 与连续结局 MD/SMD 各一组）贯穿全部步骤，可直接作为提示词喂给大语言模型，也可作为操作手册照做。典型场景：「某新型降糖/降压干预对结局的 RCT 汇总」或「某慢病管理项目对住院率的 OR 汇总」。\n\n## 适用范围（明确边界）\n\n允许：\n- 帮助研究者完成 RCT / 观察性研究系统评价的效应量合并、异质性检验、亚组/meta 回归与发表偏倚评估全流程\n- 给出二分类（OR / RR）与连续（MD / SMD）结局的效应量与标准误计算、固定/随机效应模型、森林图、发表偏倚检验的 ","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-meta-analysis-skill","title":"Skill：Meta 分析助手（系统评价效应合并，Stata / R）","journal":"方法 Skill","year":"","url":"skills/meta-analysis-skill.md","text":"# Skill：Meta 分析助手（系统评价效应合并，Stata / R）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n一份可复用的「系统评价与 Meta 分析」工作流：从 PICO 问题、PRISMA 检索—筛选—纳入流程与数据提取，到 Stata（官方 `meta` 命令族；SSC 的 `metan` / `metareg` / `metabias`）与 R（`meta`、`metafor`）的效应量计算（OR/RR 对数尺度、MD/Hedges' g）、固定/随机效应合并、森林图、异质性检验与亚组/meta 回归、发表偏倚评估，以及 PRISMA 2020 报告与 GRADE 证据分级。**每一步都给出「方法 + 代码」**；一组自洽的虚构示例数据（8 项 RCT，二分类结局 OR 与连续结局 MD/SMD 各一组）贯穿全部步骤，可直接作为提示词喂给大语言模型，也可作为操作手册照做。典型场景：「某新型降糖/降压干预对结局的 RCT 汇总」或「某慢病管理项目对住院率的 OR 汇总」。\n\n## 适用范围（明确边界）\n\n允许：\n- 帮助研究者完成 RCT / 观察性研究系统评价的效应量合并、异质性检验、亚组/meta 回归与发表偏倚评估全流程\n- 给出二分类（OR / RR）与连续（MD / SMD）结局的效应量与标准误计算、固定/随机效应模型、森林图、发表偏倚检验的 Stata / R 代码\n- 用示例数据演示 PRISMA 2020 报告要点与 GRADE 证据分级方法\n\n**不允许：**\n- 替研究者做文献检索、纳入排除与偏倚风险评价的最终专业判断（需双人独立筛选、第三方仲裁与领域知识支撑）\n- 承诺结果一定可发表；不替代 Cochrane Handbook、PRISMA 2020 声明与期刊审稿要求\n- 在异质性来源未讨论、偏倚风险未评价的情况下武断下结论\n\n## 软件准备\n\n方法：Stata 17 及以上（官方 `meta` 命令族需 Stata 16+；`confunnel` 需 Stata 17+；`meta bias` 需 Stata 18+）；R ≥ 4.2。`metan`、`metareg`、`metabias` 为社区命令，来自 SSC（作者 Harris、Harbord 等，Stata Journal 文献见各自 help 文件）；R 侧用 `meta`（Schwarzer 等）与 `metafor`（Viechtbauer）。\n\n```stata\n* Stata（联网安装一次；metafunnel 一般随 metan 附带，缺失时单独安装）\nssc install metan, replace\nssc install metareg, replace\nssc install metabias, replace\n```\n\n```r\n# R\ninstall.packages(c(\"meta\", \"metafor\"))\n```\n\n## 工作流程总览（7 步）\n\n1. 研究问题与检索（PICO + PRISMA）→ 2. 效应量与标准误计算 → 3. 固定/随机效应模型 → 4. 森林图 → 5. 异质性、亚组与 Meta 回归 → 6. 发表偏倚评估 → 7. 报告清单与证据分级。以下每一步均为「方法 + 代码」。\n\n---\n\n### 第 1 步：研究问题与检索（PICO + PRISMA）\n\n**方法**：\n\n- **PICO(S) 问题**（示例贯穿全篇）：P = 2 型糖尿病成年患者；I = 社区慢病管理项目（个案管理 + 电话随访）；C = 常规门诊照护；O1 = 12 个月内住院（二分类，OR）；O2 = HbA1c 较基线变化（连续，MD/SMD）；S = RCT。\n- **检索**：先注册方案（如 PROSPERO）；检索式 = PICO 要素 + 布尔逻辑 + 主题词（MeSH / Emtree）+ 研究设计过滤器；数据库示例 PubMed、Embase、Cochrane Central、CBM、CNKI、万方；辅以灰色文献与纳入研究的参考文献回溯。检索式全文必须留存（报告附录）。\n- **筛选**：双人独立按「标题/摘要 → 全文」两轮筛选，分歧由第三人仲裁；逐条记录排除原因；管理工具 EndNote / Covidence / RevMan。\n- **PRISMA 2020 流程图**（识别 → 筛选 → 纳入资格 → 纳入）各段数字必须完整、排除原因分类必须可核查。示例：\n- **数据提取表字段**：作者、年份、国家、设计、随访时长；两组样本量与基线特征；结局定义与测量时点；二分类 → 两组事件数/总数；连续 → 两组 n、均值、SD（缺 SD 时由 CI、SE、P 值或中位数-IQR 按 Wan 等方法换算并注明）；偏倚风险条目（随机序列、分配隐藏、盲法、失访）。\n\n**代码**：\n\n```text\nPRISMA 2020 流程示例（虚构数字，与后文 8 项研究自洽）：\n识别：数据库检索 n = 2347（PubMed 812；Embase 1030；Cochrane Central 301；CBM/CNKI/万方 204）\n      去重后 n = 1682\n筛选：标题/摘要初筛排除 n = 1548（与研究问题无关 1201；综述/评论 210；非目标人群 137）\n纳入资格：全文评估 n = 134，排除 n = 126\n      （非 RCT 21；不报告目标结局 58；重复发表 12；无法获取数据 17；其他 18）\n纳入：进入定量合成 n = 8\n```\n\n```stata\n* Stata：示例数据录入（8 项 RCT，虚构演示数据）\n* et/nt = 试验组住院事件数/总数；ec/nc = 对照组住院事件数/总数\n* m_t/sd_t/m_c/sd_c = 两组 HbA1c 变化值(%)的均值/标准差\n* mode：1 = 个案管理+电话随访，0 = 单纯电话随访（第 5 步亚组用）\nclear\ninput str10 study year et nt ec nc m_t sd_t m_c sd_c mode\n\"Zhang\" 2018 45 150 68 150 -1.0  0.7  -0.30 0.8  1\n\"Li\"    2019 40 140 64 140 -1.1  0.75 -0.25 0.85 1\n\"Wang\"  2019 60 150 62 150 -0.4  0.8  -0.25 0.75 0\n\"Chen\"  2020 38 130 60 128 -0.9  0.7  -0.20 0.75 1\n\"Liu\"   2021 64 160 62 155 -0.35 0.85 -0.30 0.8  0\n\"Zhao\"  2022 36 100 37 100 -0.5  0.8  -0.30 0.8  0\n\"Sun\"   2023 48 130 50 130 -0.45 0.8  -0.25 0.75 0\n\"Xu\"    2024 42 135 63 132 -1.0  0.75 -0.25 0.8  1\nend\nlabel define mode_lb 1 \"个案管理+电话随访\" 0 \"单纯电话随访\"\nlabel values mode mode_lb\n```\n\n```r\n# R：同一份示例数据\ndat <- data.frame(\n  study = c(\"Zhang\", \"Li\", \"Wang\", \"Chen\", \"Liu\", \"Zhao\", \"Sun\", \"Xu\"),\n  year  = c(2018, 2019, 2019, 2020, 2021, 2022, 2023, 2024),\n  et = c(45, 40, 60, 38, 64, 36, 48, 42),\n  nt = c(150, 140, 150, 130, 160, 100, 130, 135),\n  ec = c(68, 64, 62, 60, 62, 37, 50, 63),\n  nc = c(150, 140, 150, 128, 155, 100, 130, 132),\n  m_t  = c(-1.0, -1.1, -0.4, -0.9, -0.35, -0.5, -0.45, -1.0),\n  sd_t = c(0.7, 0.75, 0.8, 0.7, 0.85, 0.8, 0.8, 0.75),\n  m_c  = c(-0.3, -0.25, -0.25, -0.2, -0.3, -0.3, -0.25, -0.25),\n  sd_c = c(0.8, 0.85, 0.75, 0.75, 0.8, 0.8, 0.75, 0.8),\n  mode = c(1, 1, 0, 1, 0, 0, 0, 1))   # 1=个案管理+电话随访，0=单纯电话随访\n```\n\n---\n\n### 第 2 步：效应量与标准误计算\n\n**方法**：\n\n- **二分类（OR / RR，均在对数尺度计算，lnOR/lnRR 渐近正态）**。四格表单元：a = 试验组事件、b = 试验组非事件、c = 对照组事件、d = 对照组非事件。\n  - lnOR = ln((a·d)/(b·c))；**SE(lnOR) = sqrt(1/a + 1/b + 1/c + 1/d)**\n  - lnRR = ln((a/(a+b))/(c/(c+d))) = ln((a·nc)/(c·nt))；SE(lnRR) = sqrt(1/a − 1/(a+b) + 1/c − 1/(c+d))\n  - 95% CI：exp(lnOR ± 1.96·SE)；出现零单元时各格 +0.5 连续性校正，或改用 Peto OR / Mantel-Haenszel。\n- **连续**：\n  - MD = m_t − m_c；SE(MD) = sqrt(sd_t²/n_t + sd_c²/n_c)（仅同单位、同量表结局可直接合并）\n  - **Hedges' g**：s_pooled = sqrt(((n_t−1)sd_t² + (n_c−1)sd_c²)/(n_t+n_c−2))；小样本校正 J = 1 − 3/(4(n_t+n_c) − 9)；g = MD/s_pooled · J；SE(g) ≈ sqrt((n_t+n_c)/(n_t·n_c) + g²/(2(n_t+n_c)))\n  - 度量选择：单位可比 → MD；单位/量表不可比 → SMD；SMD 按 Cohen 标准解读（0.2 小 / 0.5 中 / 0.8 大）；同一分析只用一个度量，SMD 与 MD 不得混入同一合成。\n- **数据口径**：变化值（change from baseline）优先；不得把变化值与终值、调整与未调整估计混入同一分析。\n\n**代码**：\n\n```stata\n* ---- 手工计算（先核对，再交给批量命令）----\ngen a = et                      // 试验组事件\ngen b = nt - et                 // 试验组非事件\ngen c = ec                      // 对照组事件\ngen d = nc - ec                 // 对照组非事件\ngen lnOR   = ln((a*d)/(b*c))\ngen selnOR = sqrt(1/a + 1/b + 1/c + 1/d)\ngen OR     = exp(lnOR)\ngen loOR   = exp(lnOR - 1.96*selnOR)\ngen hiOR   = exp(lnOR + 1.96*selnOR)\ngen lnRR   = ln((a/nt)/(c/nc))\ngen selnRR = sqrt(1/a - 1/nt + 1/c - 1/nc)\n* 零单元处理：a b c d 各 +0.5 后按上式重算（或 metan ..., peto）\n\n* ---- 连续：MD 与 Hedges' g ----\ngen MD    = m_t - m_c\ngen seMD  = sqrt(sd_t^2/nt + sd_c^2/nc)\ngen sp    = sqrt(((nt-1)*sd_t^2 + (nc-1)*sd_c^2)/(nt+nc-2))\ngen J     = 1 - 3/(4*(nt+nc) - 9)\ngen g     = MD/sp*J\ngen seg   = sqrt((nt+nc)/(nt*nc) + g^2/(2*(nt+nc)))\nlist study year OR loOR hiOR MD seMD g seg\n\n* 单研究核对：esize 逐条 immediate 形式（语法以 help esize 为准）\nesize twosample 150 -1.0 0.7 150 -0.3 0.8, hedgesg\n```\n\n```r\n# R：手工计算（与 Stata 公式一致）\ndat$a <- dat$et; dat$b <- dat$nt - dat$et\ndat$c <- dat$ec; dat$d <- dat$nc - dat$ec\ndat$lnOR   <- with(dat, log((a*d)/(b*c)))\ndat$selnOR <- with(dat, sqrt(1/a + 1/b + 1/c + 1/d))\ndat$MD   <- dat$m_t - dat$m_c\ndat$seMD <- with(dat, sqrt(sd_t^2/nt + sd_c^2/nc))\nsp <- with(dat, sqrt(((nt-1)*sd_t^2 + (nc-1)*sd_c^2)/(nt+nc-2)))\nJ  <- with(dat, 1 - 3/(4*(nt+nc) - 9))\ndat$g   <- dat$MD/sp*J\ndat$seg <- with(dat, sqrt((nt+nc)/(nt*nc) + g^2/(2*(nt+nc))))\n\n# R：metafor::escalc（由原始数据直接计算，推荐；结果 yi/vi 与手工一致）\nlibrary(metafor)\ndat.or  <- escalc(measure = \"OR\",  ai = et, n1i = nt, ci = ec, n2i = nc,\n                  data = dat, slab = study)\ndat.rr  <- escalc(measure = \"RR\",  ai = et, n1i = nt, ci = ec, n2i = nc,\n                  data = dat, slab = study)\ndat.md  <- escalc(measure = \"MD\",  m1i = m_t, sd1i = sd_t, n1i = nt,\n                  m2i = m_c, sd2i = sd_c, n2i = nc, data = dat, slab = study)\ndat.smd <- escalc(measure = \"SMD\", m1i = m_t, sd1i = sd_t, n1i = nt,\n                  m2i = m_c, sd2i = sd_c, n2i = nc, data = dat, slab = study)\n# 注：escalc 的 \"SMD\" 默认即 Hedges' g（含小样本校正）；参数名以 ?escalc 为准\n```\n\n---\n\n### 第 3 步：固定效应与随机效应模型\n\n**方法**：\n\n- **固定效应**（Stata 官方现称 common-effect）：假设单一真实效应；权重 w_i = 1/v_i；合并 = Σw_i·y_i/Σw_i；SE = 1/sqrt(Σw_i)。\n- **随机效应**：真实效应服从分布，权重 w_i = 1/(v_i + τ²)；τ² 估计方法：DL（DerSimonian-Laird，`metan` 默认）、REML（官方 `meta regress` 与 `metafor` 默认，推荐）、PM 等，**报告时必须注明**。\n- **模型选择**：先看异质性——Q 检验 p 值、I²（< 25% 低、25–50% 中等、50–75% 实质、> 75% 高）、τ²。注意：Q 检验力低，p > 0.05 ≠ 无异质性；模型应在方案中预规定。稳妥做法：默认随机效应（推断可外推到研究总体分布），同时报固定效应作敏感性分析；I² 大时优先解释异质性来源（见第 5 步），而非机械换模型。\n- **预测区间**（prediction interval）：反映真实效应分布范围，与置信区间含义不同，建议报告。\n\n**代码**：\n\n```stata\n* ---- 官方 meta 命令族（Stata 16+；fixed 为 common 的兼容旧称）----\nmeta set lnOR selnOR, studylabel(study) esize(OR)\nmeta summarize, fixed random                 // Q、I²、tau² + 合并估计\nmeta esize, fixed random(dlaird)             // 合并估计；REML 用 random(reml)\nmeta esize, random(reml)                     // 方法子选项以 help meta esize 为准\n\n* ---- SSC metan（经典）----\n* 二分类四变量顺序 = 试验组事件 试验组总数 对照组事件 对照组总数（不是四格表 a b c d！）\nmetan et nt ec nc, or fixed random label(namevar=study, yearvar=year) sortby(year)\n\n* 连续六变量 = n1 mean1 sd1 n2 mean2 sd2；md 或 hedges（选项以 help metan 为准）\nmetan nt m_t sd_t nc m_c sd_c, md fixed random label(namevar=study, yearvar=year)\nmetan nt m_t sd_t nc m_c sd_c, hedges fixed random label(namevar=study, yearvar=year)\n```\n\n```r\n# R：meta 包\nlibrary(meta)\nm.or <- metagen(TE = lnOR, seTE = selnOR, studlab = study, data = dat,\n                sm = \"OR\", fixed = TRUE, random = TRUE,\n                method.tau = \"REML\", prediction = TRUE)\nm.md <- metacont(n.e = nt, mean.e = m_t, sd.e = sd_t,\n                 n.c = nc, mean.c = m_c, sd.c = sd_c,\n                 studlab = study, data = dat, sm = \"MD\",\n                 fixed = TRUE, random = TRUE, method.tau = \"REML\")\nm.smd <- update(m.md, sm = \"SMD\")            // 默认 Hedges' g\nsummary(m.or)\n\n# 也可直接由四格表计数（metabin，参数名注意 event.e/n.e/event.c/n.c）\nm.or2 <- metabin(event.e = et, n.e = nt, event.c = ec, n.c = nc,\n                 studlab = study, data = dat, sm = \"OR\", method = \"MH\",\n                 fixed = TRUE, random = TRUE, method.tau = \"REML\")\n```\n\n---\n\n### 第 4 步：森林图\n\n**方法**：方块大小 ∝ 权重（随机效应下小研究权重被抬升）；横线 = 研究内 95% CI；菱形 = 合并估计（宽度 = 95% CI）；竖线 = 无效线（OR/RR 为 1，MD/SMD 为 0）。读图顺序：① 菱形与无效线关系；② 各方块与 CI 的分布、离群研究；③ 结合 I² 判断方向是否一致。报告注明尺度（对数或原始值）、权重列、「有利于谁」的方向标注与所用模型（固定/随机 + τ² 方法）。\n\n**代码**：\n\n```stata\n* 官方\nmeta forestplot, esrefline(1) fixed random          // OR 无效线 = 1\n* 连续结局：先 meta set g seg, studylabel(study) esize(hedgesg)，无效线 = 0\nmeta forestplot, esrefline(0) random\ngraph export forest_or.png, replace width(2000)     // 导出高清图\n\n* metan 自带森林图（lcols/rcols 控制列；favours 以 # 分隔左右文字）\nmetan et nt ec nc, or random label(namevar=study, yearvar=year) sortby(year) ///\n    forestplot(lcols(study year) rcols(OR loOR hiOR) favours(慢病管理降低住院 # 常规照护降低住院))\n```\n\n```r\n# R：meta 包\nforest(m.or, sortvar = year, leftcols = c(\"studlab\", \"year\"),\n       rightcols = c(\"effect\", \"ci\"), test.overall = TRUE)\n\n# R：metafor（atransf = exp 把对数尺度还原为 OR；refline = 1 为无效线）\nres <- rma(yi, vi, data = dat.or, method = \"REML\")\nforest(res, slab = dat.or$study, atransf = exp, refline = 1,\n       xlab = \"OR（对数尺度）\")\n```\n\n---\n\n### 第 5 步：异质性检验与亚组分析、Meta 回归\n\n**方法**：\n\n- **Q 检验**：H0 各研究真实效应相同；检验力低，解读需谨慎。**I²**：研究间真实差异所致变异占比，判定标准 25 / 50 / 75%；**τ²**：绝对量纲（结局尺度上），I² 与 τ² 都要报告。残余 I²（I²_res）用于 meta 回归后衡量协变量解释了多少异质性。\n- **亚组分析**：按方案预规定的研究特征分组（设计、随访时长、干预强度等）；结论看组间差异检验，而非各组内是否显著；注意生态谬误（研究水平协变量不能推断个体水平因果）。\n- **Meta 回归**：研究数少时极易过拟合，经验法则**每 10 项研究至多 1 个协变量**；未预登记一律标为探索性。Stata 官方 `meta regress` 默认 REML + Knapp–Hartung；SSC `metareg` 默认矩估计（mm，可 reml）。\n\n**代码**：\n\n```stata\n* ---- 亚组（mode：1=个案管理+电话随访，0=单纯电话随访）----\n* 官方：subgroup() 森林图 + 分组合并\nmeta forestplot, subgroup(mode) addoverall\nmeta esize if mode == 1, random\nmeta esize if mode == 0, random\n* 或 SSC metan 的 by()\nmetan et nt ec nc, or random by(mode) label(namevar=study, yearvar=year)\n\n* ---- Meta 回归（协变量 year：发表年份趋势）----\nmeta regress year                     // 官方：REML + Knapp–Hartung（以 help meta regress 为准）\nmetareg lnOR year, wsse(selnOR) mm    // SSC metareg；REML 用 metareg ..., wsse(selnOR) reml\n* metareg 输出含 tau² 与 I²_res；语法细节以 help metareg 为准\n```\n\n```r\n# R：亚组（byvar 须在 metagen 的原始 data 中）\nupdate(m.or, byvar = mode)\nforest(update(m.or, byvar = mode))\n\n# R：meta 回归\nmetareg(m.or, ~ year)                                # meta 包（默认 REML，以 help(metareg) 为准）\nrma(yi, vi, mods = ~ year, data = dat.or, method = \"REML\")   # metafor\nrma(yi, vi, mods = ~ mode, data = dat.or, method = \"REML\")   # 二分类协变量等价于亚组\n```\n\n---\n\n### 第 6 步：发表偏倚评估\n\n**方法**：\n\n- **漏斗图**（效应量 vs 标准误/精度）：目测对称性。**漏斗图不对称 ≠ 发表偏倚**：也可能来自真实异质性、小研究效应、方法学质量差异或机遇；研究数 < 10 时目测与检验均不可靠。\n- **统计检验**：Egger（线性回归，连续结局较适用）、Begg（秩相关）；二分类推荐 Harbord / Peters（优于 Egger）。\n- **轮廓增强漏斗图**（contour-enhanced funnel）：辅助区分——「缺失」研究多落在无统计学显著区域时更提示发表偏倚。\n- **trim-and-fill**：估计「缺失」研究数并给出调整后估计，属敏感性分析，不是纠偏证明。\n- 检验力低时报告「未检出明显证据」，而非「无发表偏倚」。\n\n**代码**：\n\n```stata\n* 官方（Stata 16+/17+/18+）\nmeta funnelplot, esrefline(1)\nconfunnel                       // 轮廓增强漏斗图（Stata 17+；选项以 help confunnel 为准）\nmeta bias, egger begg harbord   // Stata 18+；以 help meta bias 为准\n\n* SSC（metafunnel 一般随 metan 附带；以 ssc describe metan 为准）\nmetafunnel _ES _seES\nmetabias lnOR selnOR, egger     // Egger：效应量与 SE 两变量\nmetabias lnOR selnOR, begg      // Begg 秩相关（选项名以 help metabias 为准）\nmetabias a b c d, harbord       // Harbord：四格表单元 a b c d\n```\n\n```r\n# R：meta 包\nfunnel(m.or)\nmetabias(m.or, method.bias = \"linreg\")   // Egger（加权线性回归）\nmetabias(m.or, method.bias = \"rank\")     // Begg 秩相关\ntf <- trimfill(m.or); summary(tf); funnel(tf)\n\n# R：metafor\nfunnel(res)\nregtest(res, model = \"rma\")              // Egger（meta 回归法）\ntrimfill(res)                            // 输出 k0 与调整后估计\n```\n\n---\n\n### 第 7 步：报告清单与证据分级（PRISMA 2020 + GRADE）\n\n**方法**：\n\n- **PRISMA 2020**：27 项清单中与统计最相关的——检索可复现（数据库、日期、完整检索式）；筛选流程数字与排除原因；偏倚风险评价工具（RCT：RoB 2；观察：ROBINS-I）；效应量定义与合成方法（固定/随机、τ² 估计方法）；异质性、亚组/敏感性、发表偏倚；证据确定性（GRADE）；注册号与资金来源。\n- **结果表**：研究水平（效应量、95% CI、权重）+ 汇总水平（模型、合并估计、Q/I²/τ²）+ 亚组/meta 回归 + 敏感性分析。\n- **GRADE**：RCT 起始「高」，按 5 个降级域（偏倚风险、不一致性、间接性、不精确性、发表偏倚）与 3 个升级域（大效应、剂量-反应、残余混杂方向）调整 → 高/中/低/极低。逐条写出理由，勿只给等级。\n\n**代码**：\n\n```stata\n* 汇总表所需数字（填入报告模板）\nmeta summarize, fixed random\nmeta forestplot, fixed random            // 报告用图；graph export 导出高清格式\n```\n\n```r\n# R：一键提取汇总表关键数字（meta 包）\nwith(m.or, c(Q = Q, df.Q = df.Q, p.Q = pval.Q, I2_pct = I2*100,\n             tau2 = tau2, tau = tau, OR_rand = exp(TE.random),\n             lo_rand = exp(lower.random), hi_rand = exp(upper.random),\n             p_rand = pval.random))\nm.or$w.random                            // 各研究随机效应权重（%）\n```\n\n---\n\n## 常见错误清单\n\n- [ ] 只报合并效应不报 I² / Q / τ²，或「I² 大就随机、I² 小就固定」机械切换（模型与 τ² 估计方法应在方案中预规定，并常同时报告两种模型）\n- [ ] 把漏斗图不对称当作发表偏倚的「铁证」（可能来自异质性、小研究效应、方法学差异；< 10 项研究时目测不可靠）\n- [ ] `metan` 四变量顺序/含义混淆：metan 的 a b c d = 试验组事件/总数/对照组事件/总数，**不是**四格表单元（单元式才是 1/a+1/b+1/c+1/d 的 a b c d）\n- [ ] SE(lnOR) 公式用错（应为 sqrt(1/a+1/b+1/c+1/d)，a/b/c/d 为四格表单元）；零单元未做 +0.5 校正或未改用 Peto/MH\n- [ ] SMD 与 MD 混用：同一合成中混入两种度量，或把 SMD 当 MD 解释（SMD 无原始单位，需按 Cohen 标准解读）\n- [ ] 连续结局把「变化值」与「终值」、不同时点、调整与未调整估计混入同一分析\n- [ ] 忽略研究内相关：同一研究多个结局/多组/多时点重复计入（双重计数）；整群随机或交叉设计未校正\n- [ ] meta 回归过拟合：8–10 项研究放多个协变量（经验法则每 10 项研究 ≤ 1 个协变量；未预登记标为探索性）\n- [ ] 亚组结论用研究内比较的逻辑解释（生态谬误）；只看亚组内显著性、不做组间差异/meta 回归检验\n- [ ] Egger/Begg 检验在 < 10 项研究时检验力低，p > 0.05 就宣称「无发表偏倚」\n- [ ] 把 trim-and-fill 调整后估计当成「真实效应」，而非敏感性分析\n- [ ] 森林图 CI 跨 1（OR/RR）或跨 0（MD/SMD）仍写「显著」\n- [ ] 权重含义不说明（固定与随机权重不同，随机效应下小研究权重被抬升）\n- [ ] 报告不附检索式、排除原因、偏倚风险评价与 PRISMA 清单（不可复现）\n\n## 输出格式（Meta 分析报告模板）\n\n```\n【Meta 分析报告模板】\n\n一、背景与问题：PICO 一句话 + 注册号（如 PROSPERO CRD…）\n二、方法：数据库与检索式（附录）；纳入/排除标准；筛选流程（PRISMA 2020 流程图）；\n     偏倚风险工具（RoB 2 / ROBINS-I）；效应量定义（OR/RR/MD/SMD）；\n     合成模型（固定/随机 + τ² 估计方法 DL/REML）；异质性、亚组/敏感性、发表偏倚计划\n三、结果：\n   表 1 纳入研究特征（作者、年份、国家、设计、样本量、干预/对照、结局定义、随访时长）\n   表 2 每项研究效应量（95% CI）与权重（固定/随机）\n   表 3 汇总（示例输出，以实际运行结果为准）：\n       结局        研究数  合并效应量(95% CI)   模型        Q(df), p      I²    τ²\n       住院(OR)    8       0.69 (0.53–0.88)     随机(REML)  14.6(7), 0.04  52%   0.07\n       HbA1c(MD)   8       …                   随机(REML)  …              …     …\n   图 1/图 2 森林图（标注方向、模型与权重）\n   表 4 亚组 / meta 回归（如有）：分组效应量、组间差异 p、回归系数(95% CI)、I²_res\n   图 3 漏斗图（可加轮廓增强）+ Egger/Begg p 值 + trim-and-fill 调整估计\n四、讨论：异质性来源、临床/政策含义、局限（检索语言、灰色文献、研究数少、混杂）\n五、GRADE：每结局确定性（高/中/低/极低）与逐条理由。\n   示例（住院结局）：8 项 RCT 起点「高」；\n   - 偏倚风险 −1（多数未报告分配隐藏/盲法）；\n   - 不一致性 −1（I² ≈ 52%，方向一致但幅度差异大）；\n   → 确定性「低」。\n六、利益冲突与资金来源\n```\n\n## 使用提示\n\n- 命令与选项随版本更新：Stata 侧 `help meta` / `help metan` / `help metareg` / `help metabias` / `help confunnel`；R 侧 `help(metagen)` / `help(metacont)` / `help(metabin)` / `help(escalc)` / `help(rma)` 核对。\n- Stata 16+ 官方 `meta` 命令族与 SSC `metan` 是两套体系（变量、结果存储与图形语法不同），勿混用；建议初学先手工 gen lnOR/selnOR 或 `escalc` 核对数字，再交给批量命令，两边结果一致再继续。\n- 二分类结局选 OR 还是 RR 应在研究问题层面（发病率 vs 患病率人群）确定；零单元多（稀有事件）用 Peto OR（`metan et nt ec nc, peto`）或 MH 法。\n- 本 Skill 提供方法与代码模板；文献检索策略、纳入排除与最终结论由研究者负责。\n\n---\n\n## 引用 / Citation\n\n中文：杜思娴. Meta 分析助手：系统评价效应合并 Skill [EB/OL]. (2026). https://naturedu.github.io/socialmedicine/.\n\nEnglish: Du, S. (2026). *Meta-Analysis Assistant: Systematic review effect synthesis skill* [Skill definition]. https://naturedu.github.io/socialmedicine/.\n","kind":"方法 Skill","locator":"方法手册","license":"MIT（仓库原有技能）","verified":"原有公开仓库"}},{"id":"studio-library:skill-sem-qca-qualitative-skill","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Skill：定性方法与结构方程专题（扎根理论 / SEM / 多层SEM / fsQCA，R / Stata）","summary":"# Skill：定性方法与结构方程专题（扎根理论 / SEM / 多层SEM / fsQCA，R / Stata）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n一份可复用的「定性方法与结构方程」专题教程，覆盖社会医学与卫生管理研究中四个高频方法：**扎根理论**（质性理论建构）、**结构方程模型 SEM**（潜变量测量与路径）、**多层/多重嵌套 SEM**（嵌套数据）、**fsQCA 模糊集定性比较分析**（组态因果）。每个专题都是「方法 + 代码」，R 与 Stata 双轨，可作为提示词喂给大语言模型，也可作为操作手册照做。\n\n## 适用范围（明确边界）\n\n允许：\n- 指导研究者完成四个专题的建模、估计、解读与报告\n- 给出可运行的 R（lavaan / QCA / quanteda）与 Stata（sem / gsem / fuzzy）代码\n- 帮助理解方法适用条件与常见错误\n\n**不允许：**\n- 替研究者做理论建构与质性编码（扎根理论以人工编码为主，软件只是辅助）\n- 保证模型拟合一定达标；不替代教科书与期刊审稿要求\n- 把探索性结果当作验证性结论发表\n\n## 软件准备\n\n方法：R ≥ 4.2（lavaan、QCA、quanteda）；Stata 16+（sem、gsem 内置，fuzzy 来自 SSC）。质性编码软件可选 NVivo / MAXQDA / A","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-sem-qca-qualitative-skill","title":"Skill：定性方法与结构方程专题（扎根理论 / SEM / 多层SEM / fsQCA，R / Stata）","journal":"方法 Skill","year":"","url":"skills/sem-qca-qualitative-skill.md","text":"# Skill：定性方法与结构方程专题（扎根理论 / SEM / 多层SEM / fsQCA，R / Stata）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n一份可复用的「定性方法与结构方程」专题教程，覆盖社会医学与卫生管理研究中四个高频方法：**扎根理论**（质性理论建构）、**结构方程模型 SEM**（潜变量测量与路径）、**多层/多重嵌套 SEM**（嵌套数据）、**fsQCA 模糊集定性比较分析**（组态因果）。每个专题都是「方法 + 代码」，R 与 Stata 双轨，可作为提示词喂给大语言模型，也可作为操作手册照做。\n\n## 适用范围（明确边界）\n\n允许：\n- 指导研究者完成四个专题的建模、估计、解读与报告\n- 给出可运行的 R（lavaan / QCA / quanteda）与 Stata（sem / gsem / fuzzy）代码\n- 帮助理解方法适用条件与常见错误\n\n**不允许：**\n- 替研究者做理论建构与质性编码（扎根理论以人工编码为主，软件只是辅助）\n- 保证模型拟合一定达标；不替代教科书与期刊审稿要求\n- 把探索性结果当作验证性结论发表\n\n## 软件准备\n\n方法：R ≥ 4.2（lavaan、QCA、quanteda）；Stata 16+（sem、gsem 内置，fuzzy 来自 SSC）。质性编码软件可选 NVivo / MAXQDA / Atlas.ti（中国场景常用 NVivo）。\n\n```stata\n* Stata（联网安装一次）\nssc install fuzzy, replace\n```\n\n```r\n# R\ninstall.packages(c(\"lavaan\", \"QCA\", \"quanteda\"))\n```\n\n## 工作流程总览（4 个专题）\n\n1. 扎根理论 → 2. SEM → 3. 多层 SEM → 4. fsQCA。选型建议：有访谈/文本想**建构理论**用专题 1；有量表与理论模型想**检验潜变量关系**用专题 2；数据**嵌套**（患者-科室-医院）用专题 3；中小样本想回答**哪些条件组合**导致结果用专题 4。\n\n---\n\n### 专题 1：扎根理论（Grounded Theory）\n\n**方法**：\n\n- **三级编码**：开放式编码（逐句贴标签）→ 主轴编码（范畴归类与关系，如因果/情境/策略/结果）→ 选择性编码（核心范畴与故事线）。\n- **理论抽样与饱和**：按新出现的范畴决定下一步采谁；新资料不再产生新范畴即饱和。\n- **备忘录**：编码全程记录想法，是理论的原料。\n- **流派**：Strauss-Corbin 偏结构化程序（编码范式）；Charmaz 偏建构主义（理论是研究者与资料共建的）。报告注明所循流派。\n- **报告规范**：COREQ 清单（研究团队与反思性、研究设计、分析发现三大域）。质性分析以**人工编码为主**；文本挖掘只做词频/共现提示。\n\n**代码**（R 文本辅助，编码仍由人完成）：\n\n```r\nlibrary(quanteda)\n# 示例访谈（虚构片段，演示用）\ninterviews <- data.frame(\n  id = 1:6,\n  text = c(\n    \"医保报销比例提高以后，我们村慢病患者愿意按时复诊了，负担轻了很多。\",\n    \"村卫生室药品不全，想开的药经常没有，只能去镇上，路远还要排队。\",\n    \"签约的家庭医生上门测血压，老人觉得被重视，依从性好多了。\",\n    \"镇卫生院设备老化，有些检查做不了，来回县里要一整天。\",\n    \"报销手续以前要跑好几趟，现在村卫生室直接结算，方便多了。\",\n    \"宣传不够，很多老人根本不知道慢病门诊能报销多少。\")\n)\ncorp <- corpus(interviews, text_field = \"text\", docid_field = \"id\")\ntoks <- tokens(corp, remove_punct = TRUE)\ndfmat <- dfm(toks)\ntextstat_frequency(dfmat, n = 20)               # 高频词 → 开放编码线索\nfcm(toks, context = \"window\", window = 5) |>    # 词共现 → 主轴编码线索\n  textplot_network(min_freq = 2)\n```\n\n**输出要求**：编码表（范畴—子范畴—原始引文）、理论模型图（核心范畴与关系）、饱和度说明、研究者立场（反思性）。\n\n---\n\n### 专题 2：结构方程模型（SEM）\n\n**方法**：\n\n- **测量模型（CFA）**：潜变量由多个条目测量；看标准化载荷（≥0.5，理想 ≥0.7）、组合信度 CR（≥0.7）、平均方差抽取 AVE（≥0.5）。\n- **结构模型**：潜变量间的路径系数与显著性。\n- **拟合指标**：CFI/TLI ≥ 0.90（理想 ≥0.95）、RMSEA < 0.08（理想 <0.06）、SRMR < 0.08；卡方显著不单独作为拒绝依据（大样本敏感）。\n- **修正指数**：只在理论允许时加路径/相关；禁止数据驱动乱改。\n- **样本量**：经验规则每个自由参数 10–20 例；至少 200 例为宜。\n- **注意**：探索性与验证性不能混用同一数据（理想：EFA 定结构 → CFA 验证；或拆分样本）。\n\n**代码**（示例：健康素养 HL → 自我效能 SE → 依从性 AD）：\n\n```r\nlibrary(lavaan)\nset.seed(2026)\nn <- 300\nHL <- rnorm(n); SE <- 0.6 * HL + rnorm(n, 0, sqrt(1 - 0.36)); AD <- 0.5 * SE + rnorm(n, 0, sqrt(1 - 0.25))\nd <- data.frame(\n  x1 = 0.7 * HL + rnorm(n, 0, 0.7), x2 = 0.8 * HL + rnorm(n, 0, 0.6), x3 = 0.75 * HL + rnorm(n, 0, 0.65),\n  y1 = 0.7 * SE + rnorm(n, 0, 0.7), y2 = 0.8 * SE + rnorm(n, 0, 0.6), y3 = 0.75 * SE + rnorm(n, 0, 0.65),\n  z1 = 0.7 * AD + rnorm(n, 0, 0.7), z2 = 0.8 * AD + rnorm(n, 0, 0.6), z3 = 0.75 * AD + rnorm(n, 0, 0.65))\n\nmodel <- '\n  HL =~ x1 + x2 + x3\n  SE =~ y1 + y2 + y3\n  AD =~ z1 + z2 + z3\n  SE ~ HL\n  AD ~ SE\n'\nfit <- sem(model, data = d)\nsummary(fit, standardized = TRUE, fit.measures = TRUE)\nfitMeasures(fit, c(\"chisq\", \"df\", \"cfi\", \"tli\", \"rmsea\", \"srmr\"))\n```\n\n```stata\n* Stata：等价写法（数据需先导入）\nsem (HL -> x1 x2 x3) (SE -> y1 y2 y3) (AD -> z1 z2 z3) ///\n    (SE <- HL) (AD <- SE), method(mlmv) standardized\nestat gof, stats(all)\n```\n\n---\n\n### 专题 3：多层 / 多重嵌套 SEM（Multilevel SEM）\n\n**方法**：\n\n- **适用**：数据嵌套（患者—科室—医院、居民—社区），观测不独立。\n- **先验检验**：计算 ICC（组内相关），ICC 明显大于 0（如 >0.05）才需要多层。\n- **分解**：组内（within，个体层面）与组间（between，机构层面）关系分别估计；组间结论不能套到个体。\n- **样本量经验**：组数至少 30–50（组数是多层的关键），每组个体可少（如 10–20）。\n- **随机设定**：随机截距最常见；随机斜率需理论与似然比检验支持。\n\n**代码**：\n\n```r\nlibrary(lavaan)\nset.seed(2026)\nH <- 30; m <- 20; n <- H * m\nd <- data.frame(hospital = rep(1:H, each = m))\nd$ueff <- rnorm(H, 0, 0.5)[d$hospital]          # 医院随机效应\nd$HL <- 0.4 * d$ueff + rnorm(n)\nd$SE <- 0.5 * d$HL + 0.3 * d$ueff + rnorm(n)\nd$x1 <- 0.7 * d$HL + rnorm(n, 0, 0.7); d$x2 <- 0.8 * d$HL + rnorm(n, 0, 0.6); d$x3 <- 0.75 * d$HL + rnorm(n, 0, 0.65)\nd$y1 <- 0.7 * d$SE + rnorm(n, 0, 0.7); d$y2 <- 0.8 * d$SE + rnorm(n, 0, 0.6); d$y3 <- 0.75 * d$SE + rnorm(n, 0, 0.65)\n\nmodel2 <- '\n  level: 1\n    HLw =~ x1 + x2 + x3\n    SEw =~ y1 + y2 + y3\n    SEw ~ HLw\n  level: 2\n    HLb =~ x1 + x2 + x3\n    SEb =~ y1 + y2 + y3\n    SEb ~ HLb\n'\nfit2 <- sem(model2, data = d, cluster = \"hospital\")\nsummary(fit2)\n```\n\n```stata\n* Stata：gsem 多层（医院随机截距）；语法细节以 help gsem 为准\ngsem (HL -> x1 x2 x3) (SE -> y1 y2 y3) (SE <- HL M1[hospital]), vce(cluster hospital)\nestat icc\n```\n\n---\n\n### 专题 4：fsQCA 模糊集定性比较分析\n\n**方法**：\n\n- **思想**：组态视角——同一结果可由**多条路径**（条件组合）达成；非对称因果（有 A 导致 Y，不意味无 A 导致无 Y）。与回归互补：回归问净效应，fsQCA 问组合。\n- **校准**：直接法三锚点（完全隶属 = 0.95、交叉点 = 0.5、完全不隶属 = 0.05），锚点要有理论与样本分布依据；常用样本 5%/50%/95% 分位或量表既定标准。\n- **必要性**：单一条件一致性 ≥ 0.9 视为必要条件；覆盖率高说明必要但不足以充分。\n- **充分性**：真值表（频率阈值与一致性阈值，通常 0.75–0.8）→ 布尔最小化得到复杂解/简约解/中间解；报告中间解（结合理论方向性假设），一致性与原始/唯一覆盖率。\n- **样本**：中小样本（10–50 例）组态研究；条件数 4–6 个为宜（条件多则组合爆炸）。\n- **稳健性**：锚点微调、一致性阈值 ±0.05、增删案例，看组态是否稳定。\n\n**代码**：\n\n```r\nlibrary(QCA)\nset.seed(2026)\nn <- 30\nd <- data.frame(\n  score = rnorm(n, 70, 15),          # 成效评分\n  res   = rnorm(n, 20, 8),           # 资源投入\n  staff = rnorm(n, 10, 4),           # 人员配置\n  inc   = rnorm(n, 0.5, 0.2),        # 激励机制\n  it    = rnorm(n, 3, 1.2))          # 信息化\nd <- data.frame(\n  成效   = calibrate(d$score, c(60, 75, 90)),\n  资源   = calibrate(d$res, c(10, 20, 30)),\n  人员   = calibrate(d$staff, c(5, 10, 15)),\n  激励   = calibrate(d$inc, c(0.2, 0.5, 0.8)),\n  信息化 = calibrate(d$it, c(1, 3, 5)))\n\ntt <- truthTable(d, outcome = \"成效\", n.cut = 1, incl.cut = 0.8)\ntt\nsol <- minimize(tt, include = \"?\")   # 中间解\nsol\nXYplot(tt$tt$资源, d$成效, xlab = \"资源\", ylab = \"成效\")   # 必要条件散点（对单一条件判断必要性）\n```\n\n```stata\n* Stata：SSC 的 fuzzy 包做模糊集校准与操作（以 help fuzzy 为准）\nssc install fuzzy\nfzcalc zy, low(60) cross(75) high(90)\n```\n\n---\n\n## 常见错误清单\n\n- [ ] 扎根理论：只贴词频不做理论建构；小样本宣称饱和；混淆描述与理论；不写反思性\n- [ ] SEM：把探索性结果当验证性报告；只看卡方显著性；无理论依据按修正指数乱加路径；载荷不达标仍强解\n- [ ] 多层 SEM：组数很少却做多层；把组间系数套到个体；忽略聚类稳健标准误；不做 ICC 先验\n- [ ] fsQCA：校准锚点拍脑袋不给理由；把组态解当净效应；条件数与样本量失衡；只报覆盖率不报一致性；不报告反事实分析\n- [ ] 通用：代码与数据不可复现；把 R/Stata 输出原样贴进论文而不做文字解读\n\n## 输出格式（专题报告模板）\n\n```\n【研究问题与方法选择】\n为什么用这个方法（数据形态 + 研究问题的匹配说明）。\n\n【数据与变量】\n样本来源、嵌套结构（多层）、校准锚点（fsQCA）、访谈资料（扎根）。\n\n【结果】\n专题 2/3：模型图、标准化载荷与路径系数表、拟合指标表（判断标准并列）。\n专题 1：编码表 + 理论模型图 + 饱和度说明。\n专题 4：真值表 + 组态解表（一致性/覆盖率）+ 必要性检验。\n\n【讨论与局限】\n结合理论的解释；方法局限（如截面数据因果弱、锚点敏感性）。\n```\n\n## 使用提示\n\n- 四个专题与 `socialmedicineskills.md` 速查手册的 G 类条目互相链接；理论框架部分（Andersen、TPB 等）见手册 F 类。\n- R 包与 Stata 命令随版本更新，运行前核对帮助文档；lavaan 多层模型参数较多时留意收敛警告。\n- 本 Skill 提供方法与代码模板；理论建构、编码与最终结论由研究者负责。\n\n---\n\n## 引用 / Citation\n\n中文：杜思娴. 定性方法与结构方程专题教程：扎根理论 / SEM / 多层SEM / fsQCA Skill [EB/OL]. (2026). https://naturedu.github.io/socialmedicine/.\n\nEnglish: Du, S. (2026). *Qualitative methods and structural equation modeling: Grounded theory, SEM, multilevel SEM, and fsQCA* [Skill definition]. https://naturedu.github.io/socialmedicine/.\n","kind":"方法 Skill","locator":"方法手册","license":"MIT（仓库原有技能）","verified":"原有公开仓库"}},{"id":"studio-library:skill-socialmedicineskills","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Skill：socialmedicineskills —— 社医卫管方法学速查手册（54 条）","summary":"---\nname: socialmedicineskills\ndescription: 社医卫管方法学速查手册：覆盖研究设计、统计分析、卫生经济、因果推断、数据科学、理论框架与定性与结构方程专题共 54 个方法，每条给出适用场景、要点、Stata/R 核心代码、报告要点与常见错误；深度教程见同目录九份 Skill。\n---\n\n# Skill：socialmedicineskills —— 社医卫管方法学速查手册（54 条）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n面向社会医学与卫生事业管理 / 卫生经济学研究者的一份**方法学速查手册**：54 个常用方法，每个方法给出「适用 / 要点 / Stata 与 R 核心代码 / 报告要点 / 常见错误」。适合选题阶段选方法、写方法学部分时查口径、上课与组会速查。**深度教程**见本目录九份 Skill（DCE、Markov、卫生经济学教程、机器学习、DID、ITS、DDD、Meta、定性方法与结构方程专题）。\n\n## 如何使用\n\n- 按「类目 + 编号」定位（如 B5 = 二元 logistic 回归）；类目：A 研究设计与偏倚、B 统计分析、C 卫生经济与政策、D 因果推断、E 数据科学、F 理论框架、G 定性与结构方程。\n- 代码均为**可运行的示例**；标注「ssc install」的 Stata 第三方命令与 R","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-socialmedicineskills","title":"Skill：socialmedicineskills —— 社医卫管方法学速查手册（54 条）","journal":"方法 Skill","year":"","url":"skills/socialmedicineskills.md","text":"---\nname: socialmedicineskills\ndescription: 社医卫管方法学速查手册：覆盖研究设计、统计分析、卫生经济、因果推断、数据科学、理论框架与定性与结构方程专题共 54 个方法，每条给出适用场景、要点、Stata/R 核心代码、报告要点与常见错误；深度教程见同目录九份 Skill。\n---\n\n# Skill：socialmedicineskills —— 社医卫管方法学速查手册（54 条）\n\n**版本** 1.0 · **作者** 杜思娴 (Du Sixian) · **许可** MIT · **更新** 2026-10-23\n\n## 用途\n\n面向社会医学与卫生事业管理 / 卫生经济学研究者的一份**方法学速查手册**：54 个常用方法，每个方法给出「适用 / 要点 / Stata 与 R 核心代码 / 报告要点 / 常见错误」。适合选题阶段选方法、写方法学部分时查口径、上课与组会速查。**深度教程**见本目录九份 Skill（DCE、Markov、卫生经济学教程、机器学习、DID、ITS、DDD、Meta、定性方法与结构方程专题）。\n\n## 如何使用\n\n- 按「类目 + 编号」定位（如 B5 = 二元 logistic 回归）；类目：A 研究设计与偏倚、B 统计分析、C 卫生经济与政策、D 因果推断、E 数据科学、F 理论框架、G 定性与结构方程。\n- 代码均为**可运行的示例**；标注「ssc install」的 Stata 第三方命令与 R 包需先安装。\n- 所有命令请以 `help` / 帮助文档为准；本手册的数值仅作演示。\n\n## A 类：研究设计与偏倚控制（8 个方法）\n\n> 方法学速查手册 A 类条目片段（A1–A8）。每个方法按「适用 / 要点 / 代码（Stata + R）/ 报告要点 / 常见错误」展开；代码为可运行核心命令，示例数据与阈值仅作演示。\n\n### A1 横断面研究\n\n**适用**：在某一时点同时测量暴露与结局，描述疾病分布或探索暴露-结局关联（如国家卫生服务调查、体检数据、居民健康素养调查）。\n\n**要点**：核心是一次性调查，暴露与结局同时收集、无时间先后，只能得关联不能定因果。患病率 = 现患病例数 / 调查人口数（时点患病率）；关联指标推荐患病率比 PR（log-binomial）或暴露患病比值比 POR，结局常见时 OR 会高估关联。抽样常为多阶段/分层抽样，需加权估计总体；典型偏倚有奈曼偏倚（病程长短影响入选，长病程者过度代表）、无应答偏倚与回忆偏倚。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nproportion disease                       // 患病率及 CI（默认 Wald）\nci proportions disease, wilson           // Wilson CI，小样本更稳\nglm disease exposed, family(binomial) link(log) eform   // PR；不收敛时换 poisson disease exposed, irr vce(robust)\n\n* ===== R =====\nx <- 45; n <- 300                        # 患病人数与调查人数\nprop.test(x, n, correct = FALSE)         # 患病率及近似 CI\nbinom.test(x, n)                         # 精确 Clopper-Pearson CI\nm <- glm(disease ~ exposed, family = binomial(link = \"log\"), data = d)\nexp(cbind(PR = coef(m), confint.default(m)))   # 患病率比 PR\n```\n\n**报告要点**：报告调查时点、抽样设计与加权方法；患病率须附 95% CI（注明方法），关联指标用 PR/POR 并说明为何不选 OR。\n\n**常见错误**：用 OR 解读高患病率结局、忽略复杂抽样权重直接做点估计。\n\n### A2 病例对照研究\n\n**适用**：结局已发生、回顾比较病例与对照的既往暴露，适合罕见病、长潜伏期结局的病因探索（如肿瘤、罕见不良反应）。\n\n**要点**：按结局状态选病例组与对照组（来自同一源人群），回顾收集暴露史；指标 OR = (a×d)/(b×c) = 病例组暴露比值 / 对照组暴露比值，罕见病时近似 RR。匹配设计（个体/频数匹配）提高效率并控制混杂，但必须用条件 logistic 回归；不能直接估计发病率。典型偏倚：回忆偏倚、选择偏倚（如 Berkson 偏倚）、暴露测量偏倚。\n\n**代码**：\n\n```stata\n* ===== Stata =====\ntabulate exposed case, chi2\ncci 40 20 30 70          // 病例暴露/非暴露=40/20，对照暴露/非暴露=30/70 → OR 与 CI\nlogistic case exposed age, or              // 不匹配：多因素校正 OR\nclogit case exposed, group(pairid) or      // 1:1 匹配：条件 logistic\n\n* ===== R =====\nm <- matrix(c(40, 20, 30, 70), nrow = 2)   # 病例暴露/非暴露、对照暴露/非暴露\nfisher.test(m)                             # 精确 OR 与 CI\nglm(case ~ exposed + age, family = binomial(), data = d)   # 校正 OR\nlibrary(survival)\nclogit(case ~ exposed + strata(pairid), data = d)          # 匹配条件 logistic\n```\n\n**报告要点**：报告病例/对照来源、匹配变量与匹配比、应答率；报告 OR 与 95% CI、校正变量与模型类型（条件/非条件）。\n\n**常见错误**：匹配数据用非条件 logistic（偏倚）、结局不罕见时把 OR 当 RR 解释。\n\n### A3 队列研究\n\n**适用**：按暴露状态分组、随访观察结局，用于验证病因或评估暴露效应，可直接计算发病率、同时观察多个结局（如出生队列、职业队列）。\n\n**要点**：暴露组与非暴露组前瞻（或回顾）随访；核心指标：累积发病率 CI = 发病数/期初人数、发病密度 ID = 发病数/人时。关联：RR = 暴露组发病率/非暴露组发病率（2×2 数据），人时数据用 Poisson 回归得 IRR，含删失的生存数据用 Cox 回归得 HR。典型偏倚：失访偏倚、健康工人效应、暴露/结局测量偏倚，混杂用分层 MH 或多变量模型控制。\n\n**代码**：\n\n```stata\n* ===== Stata =====\ncs disease exposed, exact                  // 2×2：RR 及 CI\nir disease exposed                         // 发病密度比 IRR\nstset time, failure(disease)               // 人时/生存数据\nstrate exposed                             // 各组人时与发病率\npoisson disease exposed, exposure(time) irr\nstcox exposed age                          // 校正 HR\n\n* ===== R =====\nlibrary(epiR)\nepi.2by2(matrix(c(45, 955, 20, 980), nrow = 2), method = \"cohort.count\")\nglm(disease ~ exposed + offset(log(time)), family = poisson(), data = d)  # IRR\nlibrary(survival)\ncoxph(Surv(time, disease) ~ exposed + age, data = d)                      # HR\n```\n\n**报告要点**：报告随访起止、人时、失访率与删失规则；报告累积发病率/发病密度及 RR/IRR/HR 的 95% CI 与校正模型。\n\n**常见错误**：忽略人时把发病密度当累积发病率，或随访长短不一却不用生存分析。\n\n### A4 随机对照试验 RCT\n\n**适用**：评价干预效果的金标准，随机分配均衡已知与未知混杂，用于药物、诊疗方案与卫生服务干预的效果评价。\n\n**要点**：要素为随机分配（简单/区组/分层）、对照（安慰剂/标准治疗）、盲法（单盲/双盲），平行组为主，另有交叉与整群设计。首要分析人群为 ITT（意向性分析）；效应量：二分类结局用 RR、RD 与 NNT（NNT = 1/RD），连续结局用均数差 MD。报告遵循 CONSORT（注册号、随机序列、分配隐藏、盲法、流程图）。整群 RCT 需按设计效应 DE = 1 + (m−1)×ICC 扩大样本量。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nset obs 120\nset seed 2026\negen block = seq(), to(30) block(4)       // 30 个区组、组内 4 人\ngen treat = rbinomial(1, 0.5)             // 简单随机（严格 2:2 区组需再抽样）\ntab treat\ncsi 20 10 25 5                            // 干预组事件/非事件=20/10，对照组=25/5 → RD、RR 及 CI；NNT=1/RD\nttest y, by(treat)                        // 连续结局：MD\nregress y treat, vce(robust)\n\n* ===== R =====\nset.seed(2026)\nd$treat <- sample(rep(0:1, 60))           # 120 人简单随机（可用 blockrand 做区组）\nlibrary(epiR)\nepi.2by2(table(d$treat, d$outcome), method = \"cohort.count\")   # RR/RD/NNT\nt.test(y ~ treat, data = d)\nlm(y ~ treat, data = d)\n```\n\n**报告要点**：报告注册号、随机方法与分配隐藏、盲法、CONSORT 流程图；同时报告 ITT（与 PP）结果、效应量及 95% CI（NNT 须附 CI）。\n\n**常见错误**：用 PP 替代 ITT 下结论、未报告分配隐藏，或整群随机忽略设计效应。\n\n### A5 准实验设计\n\n**适用**：无法随机分配时评价政策/干预效果的次优选择，如自然实验、政策评估（医改前后比较、地区间政策差异）。\n\n**要点**：常见类型：前后对照、非等同对照组（DID）、间断时间序列 ITS、断点回归 RDD。DID =（干预组前后差）−（对照组前后差），以组×时间交互项系数估计，前提是平行趋势；ITS 用分段回归，干预后水平变化与斜率变化分开估计。典型威胁：历史因素、向均数回归、霍桑效应与选择偏倚，须做平行趋势/安慰剂检验。\n\n**代码**：\n\n```stata\n* ===== Stata =====\ngen did = treat * post\nregress y treat post did, vce(cluster id)        // did 系数即 DID 估计\nxtset id time\nxtreg y ib0.time#i.treat i.time, fe vce(cluster id)   // 事件研究：检验平行趋势\ngen time_after = max(0, time - t0)               // ITS 分段回归（t0 = 干预时点）\nnewey y time post time_after, lag(1)             // Newey-West 处理自相关\n\n* ===== R =====\nm <- lm(y ~ treat * post, data = d)              # 交互项系数即 DID\nlibrary(sandwich); library(lmtest)\ncoeftest(m, vcov = vcovCL(m, cluster = ~id))     # 聚类稳健标准误\nd$time_after <- pmax(0, d$time - t0)\nits <- lm(y ~ time + post + time_after, data = d)   # ITS 分段回归\n```\n\n**报告要点**：报告设计类型与可比性证据（基线平衡、平行趋势图、安慰剂检验）；报告 DID 系数或 ITS 水平/斜率变化及 95% CI 与稳健标准误类型。\n\n**常见错误**：不检验平行趋势就下因果结论，或 ITS 忽略自相关直接用 OLS 标准误。\n\n### A6 生态学研究\n\n**适用**：以群体（国家/地区/年份）为分析单位探索宏观暴露与结局关联，用于跨国比较、政策环境因素分析与假说生成，数据易得、成本低。\n\n**要点**：分析单位是群体，暴露与结局均为群体汇总值，关联指标为相关系数、加权回归斜率或标化率比；比较地区率前须标准化年龄结构。核心局限是生态学谬误——群体关联不能推论到个体（跨层推论需多水平模型）；典型混杂为未测量的群体因素（医疗可及性、经济水平）。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nregress rate exposure [aweight = pop]           // 按地区人口加权\ncorr rate exposure\ndstdize deaths pop, by(agegrp) using(stdpop)    // 直接标化；stdpop.dta 含 agegrp 与 pop\n\n* ===== R =====\nm <- lm(rate ~ exposure, data = d, weights = pop)\ncor(d$rate, d$exposure)\nlibrary(epitools)\nageadjust.direct(count = d$deaths, pop = d$pop, stdpop = ref$stdpop)  # 直接标化\n```\n\n**报告要点**：报告分析单位与群体指标定义、是否加权/标化；明确“生态学谬误”局限，结论限定在群体层面。\n\n**常见错误**：把群体关联外推为个体因果（生态学谬误），或不标化年龄结构直接比较地区率。\n\n### A7 混合方法研究（定性+定量）\n\n**适用**：单一方法不足以回答研究问题时，定性（机制、体验、实施障碍）与定量（测量、检验）互补，常见于卫生服务研究、实施科学与量表开发。\n\n**要点**：核心设计：聚敛（并行）、解释性顺序（先量后质）、探索性顺序（先质后量）、嵌入式；须明确整合点与整合方式（如 joint display 联合展示）。抽样上定量用概率抽样、定性用目的抽样（最大变异/典型/极端案例），定性样本量以信息饱和为原则。质量报告：定性遵循 COREQ，整体遵循 GRAMMS/MMARS，三角互证提升效度。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nttest score, by(group)                 // 定量侧比较\ntab theme group, chi2                  // 定性主题（编码后）与分组交叉：量化整合\n\n* ===== R =====\nlibrary(quanteda)\ndfm <- dfm(tokens(interview_text), remove_punct = TRUE)   # 定性文本：词频辅助主题\ntopfeatures(dfm, 20)\nt.test(score ~ group, data = d)                           # 定量侧\nchisq.test(table(d$theme, d$group))                       # 主题×分组整合\n```\n\n**报告要点**：报告设计类型（顺序/并行）与整合点、两部分抽样与分析方法；定性部分附 COREQ 清单，整合结果用 joint display 呈现。\n\n**常见错误**：两部分平行发表而无真正整合，或定性样本套用概率抽样逻辑、未报告理论视角与反思性。\n\n### A8 德尔菲法（Delphi）\n\n**适用**：证据不足或意见分歧时，通过多轮匿名专家咨询收敛共识，用于指标筛选、指南推荐、评价体系构建（如卫生管理指标体系）。\n\n**要点**：匿名多轮问卷（通常 2–3 轮），每轮反馈统计汇总（均数、变异系数 CV、满分比），专家据反馈修改意见直至收敛。指标：专家积极系数（回收率）、权威系数 Cr = (Ca + Cs)/2（Ca 判断依据、Cs 熟悉程度）、协调度（肯德尔和谐系数 W、CV）。专家一般 15–50 人；条目筛选常用均数 ≥ 3.5 且 CV ≤ 0.25（阈值因研究而异）。\n\n**代码**：\n\n```stata\n* ===== Stata =====\negen mean_score = mean(score), by(item)\negen sd_score = sd(score), by(item)\ngen cv = sd_score / mean_score\nlist item mean_score cv if mean_score >= 3.5 & cv <= 0.25   // 示例筛选阈值\nssc install concord\nconcord score1-score15                       // 各专家评分变量 → 肯德尔 W\n\n* ===== R =====\nlibrary(dplyr)\nd |> group_by(item) |>\n  summarise(m = mean(score), cv = sd(score)/mean(score)) |>\n  filter(m >= 3.5 & cv <= 0.25)              # 示例筛选阈值\nlibrary(irr)\nw <- kendall(t(as.matrix(xtabs(score ~ expert + item, data = d))))   # 行为条目、列为专家\nw$value                                      # 肯德尔和谐系数 W\n```\n\n**报告要点**：报告轮数、每轮专家数与积极系数、权威系数、筛选标准与保留/删除条目清单、协调系数及其显著性检验。\n\n**常见错误**：轮间无统计反馈直接计分、只看均数忽略离散度与协调度检验，专家过于同质却称“共识”。\n\n---\n\n## B 类：统计分析核心方法（10 个方法）\n\n条目统一采用「适用 / 要点 / 代码 / 报告要点 / 常见错误」模板；**代码**为单个代码块，内部以 `* Stata` 与 `# R` 注释分隔，各给出核心命令。\n\n---\n\n### B1 描述统计与正态性检验\n\n**适用**：任何连续型变量的分布描述与正态性判断，是选择后续参数或非参数方法的起点。\n\n**要点**：连续资料先报 n、均值±SD（正态）或中位数（P25–P75，偏态）与最小–最大值，`summarize, detail` 还给出偏度/峰度。正态性判断不能只看 Shapiro-Wilk 检验（大样本时几乎总拒绝），要结合直方图与 Q-Q 图，并参考 |偏度|<2、|峰度|<7 等经验界值。偏态明显时报告并分析中位数（IQR），可先尝试对数/平方根变换，或直接改用非参数方法。\n\n**代码**：\n\n```stata+r\n* Stata\nsummarize y, detail              // n、均值、百分位数、偏度峰度\nswilk y                          // Shapiro-Wilk 正态性检验\nhistogram y, normal              // 直方图叠加正态密度曲线\nqnorm y                          // 分位-分位（Q-Q）图\n\n## R\nsummary(df$y)                    // 五数概括\nshapiro.test(df$y)               // Shapiro-Wilk 检验\nhist(df$y, prob = TRUE); lines(density(df$y))\nqqnorm(df$y); qqline(df$y)       // Q-Q 图\n```\n\n**报告要点**：正态资料报均值±SD，偏态资料报中位数（P25, P75）；写明正态性检验方法与结论，以及是否做了变量变换。\n\n**常见错误**：只看 Shapiro-Wilk 的 p 值、不看直方图/Q-Q 图与偏度峰度，或对严重偏态数据仍报均值±SD。\n\n---\n\n### B2 t 检验与方差分析\n\n**适用**：两组（t 检验）或三组及以上（方差分析）连续结局的组间比较。\n\n**要点**：两组独立样本用独立样本 t 检验，方差齐性不满足时用 Welch 校正（`unequal`）；同一对象前后两次测量用配对 t 检验。三组及以上用单因素方差分析（ANOVA），F 显著后再做两两比较并用 Bonferroni/Tukey 校正多重性。多组反复做两两 t 检验会膨胀 I 类错误。\n\n**代码**：\n\n```stata+r\n* Stata\nttest y, by(group) unequal       // Welch t 检验（不假定方差齐）\nttest y1 == y2                   // 配对 t 检验\noneway y group, bonferroni       // 单因素 ANOVA + Bonferroni 两两比较\nanova y group                    // 方差分析表\n\n## R\nt.test(y ~ group, data = df)              // 默认 Welch t 检验\nt.test(df$y1, df$y2, paired = TRUE)       // 配对 t 检验\nfit <- aov(y ~ group, data = df)\nsummary(fit); TukeyHSD(fit)               // ANOVA + Tukey 两两比较\n```\n\n**报告要点**：报各组均值±SD、统计量（t 或 F）、自由度与 p 值，差值或组间差异附 95% CI；ANOVA 两两比较注明校正方法与校正后 p。\n\n**常见错误**：多组间重复做两两 t 检验不校正；把配对数据当独立样本（或反之）分析。\n\n---\n\n### B3 卡方检验与非参数检验\n\n**适用**：分类结局的组间比较，以及不满足正态/方差齐性条件的连续或等级资料的组间比较。\n\n**要点**：四格表先看期望频数：n≥40 且所有期望频数≥5 用 Pearson 卡方，否则用 Fisher 精确检验。等级资料或严重偏态的连续资料，两独立组用 Mann-Whitney U（`ranksum`），配对用 Wilcoxon 符号秩（`signrank`），多组用 Kruskal-Wallis（`kwallis`）。非参数检验比较的是分布/秩次而非均数，结论措辞要相应调整。\n\n**代码**：\n\n```stata+r\n* Stata\ntab a b, chi2 exact              // Pearson 卡方 + Fisher 精确检验\nranksum y, by(group)             // Mann-Whitney U（Wilcoxon 秩和）\nsignrank y1 = y2                 // 配对 Wilcoxon 符号秩检验\nkwallis y, by(group)             // Kruskal-Wallis 检验\n\n## R\nchisq.test(table(df$a, df$b))    // Pearson 卡方\nfisher.test(table(df$a, df$b))   // Fisher 精确检验\nwilcox.test(y ~ group, data = df)\nkruskal.test(y ~ group, data = df)\n```\n\n**报告要点**：卡方报 χ²、df、p（或 Fisher 精确 p），并说明方法选择依据；非参数检验报中位数（IQR）与检验统计量，不报均数±SD。\n\n**常见错误**：期望频数<5 仍用 Pearson 卡方；把秩和检验结果描述为「两组均数差异」。\n\n---\n\n### B4 相关与简单线性回归\n\n**适用**：两个连续变量间线性关联的方向与强度描述（相关），及一个变量预测另一个变量的简单线性回归。\n\n**要点**：双变量均近正态时用 Pearson r，否则用 Spearman ρ；r 只衡量线性关联强度，不表示因果。回归分析前必须画散点图确认线性趋势并识别离群点，回归系数 β 报告 95% CI，R² 表示结局方差被解释的比例。拟合后应检查残差的正态性、独立性与方差齐性。\n\n**代码**：\n\n```stata+r\n* Stata\nscatter y x                     // 先画散点图判断线性\npwcorr x y, sig                 // Pearson r 及 p 值\nspearman x y                    // Spearman ρ\nregress y x                     // 简单线性回归\n\n## R\nplot(df$x, df$y)\ncor.test(df$x, df$y, method = \"pearson\")\ncor.test(df$x, df$y, method = \"spearman\")\nfit <- lm(y ~ x, data = df); summary(fit)\n```\n\n**报告要点**：相关报 r（或 ρ）、p 与样本量；回归报 β（95% CI）与 R²，并附散点图与回归线。\n\n**常见错误**：不画散点图直接算 Pearson 相关（被非线性或离群值误导）；把相关性解读为因果关系。\n\n---\n\n### B5 二元 logistic 回归\n\n**适用**：二分类结局（患病与否、是否就诊、是否满意）的影响因素分析，结果以比值比 OR 解释。\n\n**要点**：模型为 logit(p)=β₀+Σβᵢxᵢ，OR=exp(β) 表示自变量每增加 1 个单位时结局「发生/不发生」比值的变化倍数；分类自变量必须设参照组（虚拟变量）。报告整体模型的似然比检验、区分度（AUC）与校准（Hosmer-Lemeshow）。结局事件数太少（每自变量不足约 10 个事件，EPV<10）时模型不稳定，应减少自变量。\n\n**代码**：\n\n```stata+r\n* Stata\nlogit y x1 x2, or               // 二元 logistic，报告 OR\nestat gof, group(10)            // Hosmer-Lemeshow 校准检验\nlroc                            // ROC 曲线与 AUC\n\n## R\nfit <- glm(y ~ x1 + x2, data = df, family = binomial)\nexp(cbind(OR = coef(fit), confint(fit)))   // OR 与 95% CI\nsummary(fit)\n```\n\n**报告要点**：逐变量报 OR（95% CI）与 p，注明参照组与连续变量的单位；附模型整体检验、AUC 与校准检验结果。\n\n**常见错误**：把 OR 当 RR（风险比）解释（结局常见时严重高估）；连续变量 OR 不注明「每 1 单位」。\n\n---\n\n### B6 有序/多项 logistic 回归\n\n**适用**：等级结局（Likert 量表、疾病分期等有序分类）与无序多分类结局（就医机构类型、职业类别等）的影响因素分析。\n\n**要点**：有序结局用有序 logistic（比例优势模型），前提是比例优势假设（各切点回归系数相同），用 Brant 检验验证；满足时报一个公共 OR，不满足改用广义有序 logit（`gologit2`）或多项模型。无序多分类用多项 logistic，选定一类为参照，其余各类分别与参照类比较，系数指数化后为相对风险比 RRR。\n\n**代码**：\n\n```stata+r\n* Stata\nologit y x1 x2, or              // 有序 logistic（比例优势模型）\nbrant, detail                   // 比例优势/平行线假设检验（SSC 安装）\nmlogit y x1 x2, rrr base(1)     // 多项 logistic，参照组=1，报 RRR\n\n## R\nlibrary(MASS)\nfit <- polr(y ~ x1 + x2, data = df, Hess = TRUE)   // y 需为有序因子\nexp(cbind(coef(fit), confint(fit)))                // 公共 OR\nlibrary(nnet)\nmfit <- multinom(y ~ x1 + x2, data = df)           // 多项模型，exp(coef)=RRR\n```\n\n**报告要点**：有序模型报公共 OR（95% CI）并注明 Brant 检验结果；多项模型按类别报 RRR（95% CI）并注明参照类别。\n\n**常见错误**：不检验比例优势假设就解释 ologit 结果；把多项 logit 的 RRR 误称为 OR。\n\n---\n\n### B7 Poisson 与负二项回归\n\n**适用**：计数结局（就诊次数、住院天数、跌倒次数、处方数等）的回归建模。\n\n**要点**：Poisson 回归假设均数=方差；存在过度离散（方差明显大于均数）时标准误被低估，改用负二项回归（`nbreg`/`glm.nb`）或 Poisson+稳健标准误。随访/暴露时间不等时必须加 `exposure()`（Stata）或 `offset(log(t))`（R）按人时调整。结果以发病率比 IRR=exp(β) 解释；零计数过多可考虑零膨胀模型（zip/zinb）。\n\n**代码**：\n\n```stata+r\n* Stata\npoisson y x1 x2, irr vce(robust)          // Poisson + 稳健标准误\nnbreg y x1 x2, irr                        // 负二项回归\npoisson y x1 x2, irr exposure(t)          // 随访时间不等：按 t 调整\n\n## R\nglm(y ~ x1 + x2, data = df, family = poisson)\nlibrary(MASS)\nfit <- glm.nb(y ~ x1 + x2, data = df)     // 负二项\nexp(cbind(IRR = coef(fit), confint(fit)))\n```\n\n**报告要点**：报 IRR（95% CI）与 p，说明过度离散检验与模型选择依据，并注明是否按随访/暴露时间调整。\n\n**常见错误**：存在过度离散仍用普通 Poisson 且不加稳健标准误；随访时间不等却不加 exposure/offset。\n\n---\n\n### B8 生存分析（Kaplan-Meier 与 Cox）\n\n**适用**：事件发生时间结局（再入院时间、死亡时间、并发症出现时间等），数据含删失。\n\n**要点**：先用 `stset` 定义随访时间与事件指示变量（0=删失、1=事件），编码弄反会使 KM 曲线与风险集全部出错。Kaplan-Meier 法估计生存函数并画曲线，log-rank 检验比较组间差异；Cox 模型估计风险比 HR 并做多变量调整。必须用 Schoenfeld 残差（`estat phtest`/`cox.zph`）检验比例风险（PH）假设，违反时改用分层 Cox 或时依协变量模型。\n\n**代码**：\n\n```stata+r\n* Stata\nstset time, failure(event)               // event=1 事件，0 删失\nsts graph, by(group)                     // Kaplan-Meier 生存曲线\nsts test group                           // log-rank 检验\nstcox x1 x2                              // Cox 比例风险模型（HR）\nestat phtest, detail                     // PH 假设检验\n\n## R\nlibrary(survival)\nfit <- survfit(Surv(time, event) ~ group, data = df)\nplot(fit); survdiff(Surv(time, event) ~ group, data = df)\ncfit <- coxph(Surv(time, event) ~ x1 + x2, data = df)\nsummary(cfit); cox.zph(cfit)             // PH 假设检验\n```\n\n**报告要点**：KM 报告中位生存时间与 log-rank p；Cox 报 HR（95% CI）与 p，并注明 PH 假设检验结果。\n\n**常见错误**：事件/删失编码弄反；不检验 PH 假设就直接解释 Cox 的 HR。\n\n---\n\n### B9 重复测量与混合效应模型\n\n**适用**：同一对象多次随访的重复测量数据，或患者嵌套于医院/村等层级（聚类）数据的建模。\n\n**要点**：重复测量数据各次观测彼此相关，不能当独立样本处理；球形假设满足时可用重复测量 ANOVA，否则用线性混合效应模型。混合模型以随机截距/随机斜率刻画个体轨迹差异，报告重点是固定效应 β 及其 95% CI，随机效应与协方差结构的选择用 AIC/BIC 或似然比检验判断。二分类结局改用广义混合模型（`melogit`/`glmer`）。\n\n**代码**：\n\n```stata+r\n* Stata\nxtset id time\nmixed y x##c.time || id: time, reml      // 随机截距 + 随机斜率\nmixed y x##c.time || id:                 // 仅随机截距（供比较）\nestat ic                                 // AIC/BIC 比较\n* 二分类结局改用：melogit y x || id:\n\n## R\nlibrary(lme4); library(lmerTest)         // lmerTest 提供 p 值\nfit <- lmer(y ~ x*time + (1 + time | id), data = df)\nsummary(fit)\nranova(fit)                              // 随机效应结构的似然比检验\n```\n\n**报告要点**：报固定效应 β（95% CI）与 p，说明随机效应/协方差结构及其选择依据（AIC、LRT），并注明估计方法（REML）。\n\n**常见错误**：把重复测量当独立样本分析（标准误低估、I 类错误膨胀）；不交代随机效应结构设定。\n\n---\n\n### B10 主成分分析与因子分析\n\n**适用**：多个指标降维合成综合得分（主成分分析），或探索量表/问卷的潜在结构（探索性因子分析）。\n\n**要点**：先做适用性检验：KMO>0.6 可接受（>0.8 好）、Bartlett 球形检验 p<0.05，不满足则不适合因子分析。主成分数按特征值>1、碎石图肘点或累计方差解释率（如≥60–80%）确定；因子分析定因子数优先用平行分析，并选择旋转方法（正交 varimax 或斜交 promax）。载荷 |λ|≥0.4 视为有实际意义，量表类结果还应报告 Cronbach's α。\n\n**代码**：\n\n```stata+r\n* Stata\npca x1-x10                          // 主成分分析\nfactor x1-x10, pcf                  // 因子分析（主成分因子法）\nestat kmo                           // KMO（Bartlett 检验：ssc install factortest）\nrotate, varimax                     // 正交旋转后看载荷\nalpha x1-x10                        // Cronbach's α\n\n## R\nlibrary(psych)\nKMO(df); cortest.bartlett(df)       // 适用性检验\nfa.parallel(df)                     // 平行分析定因子数\nfa(df, nfactors = 3, rotate = \"varimax\")   // 探索性因子分析\nalpha(df)                           // Cronbach's α\n```\n\n**报告要点**：报 KMO 与 Bartlett 检验结果、因子/主成分数及确定依据、方差解释率与旋转后载荷矩阵，量表附 Cronbach's α。\n\n**常见错误**：未做 KMO/球形检验就做因子分析；把 PCA 与因子分析混为一谈；仅凭特征值>1 定因子数。\n\n---\n\n---\n\n## 方法学速查手册 · C 类：卫生经济与卫生政策（8 个方法）\n\n> 片段文件，每条目采用统一模板：**适用 / 要点 / 代码（Stata + R）/ 报告要点 / 常见错误**。命令与选项随软件版本更新，正式运行前以 `help <cmd>`（Stata）或 `?<fn>`（R）为准。\n\n### C1 成本测算\n**适用**：测算某一疾病、干预或服务在特定视角与时间段内消耗的资源总量（货币化成本），为成本分析、CEA/BIA 提供成本输入。\n**要点**：先明确测算视角（社会 / 卫生体系 / 医保 / 患者）与时间范围，视角决定纳入范围。\n成本分直接成本（直接医疗 + 直接非医疗）与间接成本（生产力损失，常用人力资本法按工资估算）。\n按「识别—测量—估值」三步：识别资源项目 → 测量消耗数量 → 以单位成本估值（数量 × 单价）。\n微观成本法（bottom-up）逐项自下而上加总，自上而下法（top-down）用总费用分摊，二者结果应交叉校验；跨年度成本须按医疗 CPI 等调整价格并按贴现率折现。\n**代码**：\n\n```stata\n* 成本均数与 bootstrap 95% CI（成本右偏时优于正态 CI）\nbootstrap r(mean), reps(1000) seed(2026): summarize cost\nci means cost\n```\n\n```r\nset.seed(2026)\nboot <- replicate(1000, mean(sample(cost, replace = TRUE)))\nmean(cost)                       # 点估计\nquantile(boot, c(.025, .975))    # 95% 百分位置信区间\n```\n\n**报告要点**：报告测算视角、时间范围、数据与单价来源（年份）、贴现率与价格调整方式；均数附 95% CI。\n**常见错误**：混淆视角（把患者自负费用计入医保视角成本），或把不同年份未经价格调整的成本直接相加。\n\n---\n\n### C2 成本-效果分析 CEA 与成本-效用分析 CUA\n**适用**：比较两种及以上干预的成本与健康产出，产出为自然单位（CEA）或 QALY（CUA）。\n**要点**：核心指标 ICER = ΔC/ΔE = (C₁−C₀)/(E₁−E₀)，与支付意愿阈值（国内常用 1–3 倍人均 GDP/QALY，口径须注明）比较判断经济性。\n不确定性用 bootstrap 或概率敏感性分析（PSA）刻画，绘制成本-效果平面与成本-效果可接受曲线（CEAC）；净货币效益 NMB = λ·ΔE − ΔC > 0 等价于在阈值 λ 下可接受。\n增量必须基于同一人群、同一时间范围计算，不能简单用两组均值相除代替统计推断。\n**代码**：\n\n```stata\n* ICER 增量计算（arm：1 = 干预，0 = 对照；ttest 按组值升序，组 2 为 arm=1）\nttest c, by(arm)\nscalar dC = r(mu_2) - r(mu_1)\nttest e, by(arm)\nscalar dE = r(mu_2) - r(mu_1)\ndi \"ICER = \" dC / dE\n```\n\n```r\ndC <- with(dat, mean(c[arm == 1]) - mean(c[arm == 0]))\ndE <- with(dat, mean(e[arm == 1]) - mean(e[arm == 0]))\nset.seed(2026)\nicers <- replicate(1000, with(dat[sample(nrow(dat), replace = TRUE), ],\n              (mean(c[arm==1]) - mean(c[arm==0])) / (mean(e[arm==1]) - mean(e[arm==0]))))\nquantile(icers, c(.025, .975))   # bootstrap ICER 置信区间\n```\n\n**报告要点**：并列报告两组成本与效果均数（含 95% CI）、ICER 及 bootstrap/PSA 结果（CEAC 上阈值处的接受概率），并注明阈值来源。\n**常见错误**：增量效果趋近 0 时 ICER 数值爆炸仍直接解读，或不做不确定性分析即断言结论稳健。\n\n---\n\n### C3 QALY 与 DALY 计算\n**适用**：把生存时间与生命质量合成为单一健康产出（QALY），或按死亡与伤残损失合成疾病负担（DALY），用于 CUA 与负担比较。\n**要点**：QALY = Σ 生存年 × 效用权重（0–1），效用来自 EQ-5D-5L（使用中国效用值集）、SF-6D 或 TTO/SG，纵向数据用曲线下面积法按时间区间加总。\nDALY = YLL + YLD：YLL = 死亡数 × 标准期望寿命，YLD = 患病人数 × 伤残权重 × 病程（权重查 GBD 表）。\nDALY 常用 GBD 参数（3% 贴现、年龄权重）；QALY 与 DALY 互为近似镜像但不能直接互换。\n**代码**：\n\n```stata\n* QALY：效用曲线下面积（q 为效用、t 为时点，按 id 与 t 排序）\nbysort id (t): gen qaly_step = (q + q[_n-1]) / 2 * (t - t[_n-1]) if _n > 1\nbysort id: egen qaly = total(qaly_step)\n* DALY：YLL + YLD（yll、yld 按 GBD 公式预先计算）\ngen daly = yll + yld\n```\n\n```r\ndat <- dat[order(dat$id, dat$t), ]\nqaly <- tapply(seq_len(nrow(dat)), dat$id, function(i) {\n  t <- dat$t[i]; q <- dat$q[i]\n  sum(diff(t) * (head(q, -1) + tail(q, -1)) / 2)     # 梯形面积法\n})\n```\n\n**报告要点**：报告效用工具、值集来源与测量时点，说明 QALY 加总方法（AUC/梯形法）与 DALY 的伤残权重、贴现参数。\n**常见错误**：把疾病严重度评分当效用权重，或直接混用 QALY 与 DALY 做比较。\n\n---\n\n### C4 预算影响分析 BIA\n**适用**：从预算持有人（医保基金、医院或政府）视角，估计采纳新干预后 3–5 年内逐年及累计的财务影响。\n**要点**：预算影响 =（新干预下目标人群总费用）−（现行干预下总费用），按「目标人群规模 × 渗透/替代率 × 人均年费用」逐年滚动测算。\n需设定市场吸纳曲线（uptake）、替代与增量使用比例、人群自然增长与价格变化；ISPOR 建议按年度现金流报告、不做贴现，且与 CEA 分开呈现。\n**代码**：\n\n```stata\n* 逐年预算影响 = 目标人群 × 渗透率 ×（新方案 − 现行方案）人均年费用\ngen bi = pop * uptake * (c_new - c_cur)\nlist year bi\nsum bi                         // 研究期内合计\n```\n\n```r\nbia <- data.frame(year = 1:5, pop = 1e5, uptake = c(.2, .4, .6, .8, .9))\nbia$bi <- bia$pop * bia$uptake * (c_new - c_cur)\nbia                            # 逐年预算影响；sum(bia$bi) 为合计\n```\n\n**报告要点**：报告视角、时间范围、目标人群与 uptake/替代假设，并给出单因素与情景（乐观/悲观）敏感性分析。\n**常见错误**：把预算影响结果当作成本-效果结论，或漏报 uptake 与人群增长假设。\n\n---\n\n### C5 卫生技术评估 HTA 流程\n**适用**：为新技术（药品、器械、诊断、服务包）的准入、定价与报销决策整合临床与经济证据的规范化评估。\n**要点**：标准流程：明确评估问题（PICO 与决策问题）→ 系统检索与证据评价 → 临床效果（必要时间接比较/网状 Meta）→ 经济评价（CEA/CUA + BIA）→ 伦理、组织、公平等非经济维度 → 综合报告供委员会决策。\n核心产出是相对阈值的经济性判断与预算影响估计；国内对应医保谈判/准入的申报与专家评审流程。\n**代码**：\n\n```stata\n* PSA 决策判断：净货币效益 NMB = λ·ΔE − ΔC，λ 为阈值（如 1–3 倍人均 GDP）\ngen nmb = lambda * (e_new - e_cur) - (c_new - c_cur)\ncount if nmb > 0\ndi \"成本-效果概率 = \" r(N) / _N\n```\n\n```r\nnmb <- lambda * (e_new - e_cur) - (c_new - c_cur)\nmean(nmb > 0)                  # NMB > 0 的比例 → CEAC 上阈值 λ 处的接受概率\n```\n\n**报告要点**：写明评估视角、关键参数来源与假设、模型结构及不确定性，并独立核验申办方提交的参数与模型。\n**常见错误**：直接采信申办方模型结论而不做独立校验，或把 HTA 简化为只看 ICER 的经济评价。\n\n---\n\n### C6 随机前沿分析 SFA 与数据包络分析 DEA（效率评价）\n**适用**：评价医院、科室、疾控机构等决策单元（DMU）的技术效率及其影响因素。\n**要点**：DEA 为非参数确定性前沿：CCR（规模报酬不变）与 BCC（可变），允许多投入多产出、无需函数形式，但对离群值敏感且不区分随机误差与无效率。\nSFA 为参数随机前沿：把误差分解为统计噪声 v 与无效率 u（半正态/截断正态等分布假设），可检验影响因素，但一般仅处理单一产出。\n两者常组合使用：DEA 得效率得分，再以 Tobit/截断回归或 SFA 分析效率影响因素。\n**代码**：\n\n```stata\n* SFA：Cobb-Douglas 随机前沿，半正态无效率\nsfcross y x1 x2, distribution(hnormal)\npredict te_sfa, te\n* DEA：投入导向 BCC（先 ssc install dea）\ndea x1 x2 = y, rts(vrs) ort(in)   // 效率得分；选项以 help dea 为准\n```\n\n```r\nlibrary(Benchmarking)\nr_dea <- dea(X = cbind(x1, x2), Y = y, RTS = \"vrs\")    # 投入导向 BCC\neff_dea <- r_dea$eff                                    # Farrell 效率 E ∈ (0,1]\nlibrary(frontier)\neff_sfa <- efficiencies(sfa(log(y) ~ log(x1) + log(x2), data = dat))\n```\n\n**报告要点**：报告前沿模型设定（方向、规模报酬、分布假设）、效率得分分布（均值/四分位）与影响因素回归结果。\n**常见错误**：样本量相对投入产出维度过小导致多数 DMU 效率 = 1 仍做解释，或 SFA 无效率分布假设未检验。\n\n---\n\n### C7 医保支付标准测算（DRG/DIP 权重与点值测算思路）\n**适用**：为 DRG/DIP 付费改革测算病组权重、费率与病种分值、点值，作为医保基金按病种结算的支付标准。\n**要点**：DRG：权重 RW = 该病组例均费用 ÷ 全部病例例均费用（基于历史住院费用数据，再经临床论证与成本校正）；费率 = 住院基金预算总额 ÷ Σ(RW × 组病例数)，支付标准 = RW × 费率 × 调整系数。\nDIP：病种分值 = 该病种例均费用 ÷ 基准例均费用（或相对点数），点值 = 区域基金总额 ÷ Σ(病种分值 × 病例数)，总量控制下按年度清算校正。\n权重/分值应剔除不合理费用结构影响（药品耗材占比、人次人头比）并建立动态调整机制。\n**代码**：\n\n```stata\n* DRG 权重：病组例均费用 ÷ 全样本例均费用\nbysort drg: egen fee_drg = mean(fee)\negen fee_all = mean(fee)\ngen rw = fee_drg / fee_all\n* DIP 点值：区域基金总额 ÷ Σ(病种分值 × 病例数)\ngen point_amt = dip_score * n_cases\nsum point_amt\ndi \"每分值金额 = \" fund_budget / r(sum)\n```\n\n```r\nlibrary(dplyr)\nrw <- dat |> group_by(drg) |> summarise(rw = mean(fee) / mean(dat$fee))\ndip_value <- fund_budget / sum(dat$dip_score * dat$n_cases)   # DIP 点值\n```\n\n**报告要点**：报告测算数据年份与费用口径、权重/分值校正方法（专家论证、费用结构剔除）及点值年度清算与校正规则。\n**常见错误**：直接使用未校正的历史例均费用当权重/分值，或忽略总额控制下点值年终清算的浮动风险。\n\n---\n\n### C8 卫生服务利用公平性（集中指数 CI 与不平等分解）\n**适用**：量化卫生服务利用（或健康结局、费用）随经济状况分布的不平等程度及其来源。\n**要点**：集中指数 CI = 2·cov(y, R)/ȳ，R 为按经济状况（收入/财富）排序的分数秩（(rank−0.5)/n），CI ∈ [−1, 1]，负值表示利用偏向较穷人群，0 为绝对平等；标准误用回归法或专门命令计算，跨人群比较时建议先做年龄性别标化。\n不平等分解：CI = Σ(βk·x̄k/ȳ)·CIk + 残差，即每个影响因素按「弹性 × 该变量的集中指数」贡献，用于定位不平等的主要驱动因素。\n**代码**：\n\n```stata\nssc install conindex, replace\nconindex y, rankvar(r)      // r：按经济状况排序的分数秩；选项以 help conindex 为准\n```\n\n```r\nr  <- (rank(ses) - 0.5) / length(ses)   # 按经济状况排序的分数秩\nci <- 2 * cov(y, r) / mean(y)           # 协方差法集中指数\n```\n\n**报告要点**：报告 CI 及其标准误/置信区间（是否显著偏离 0），分解表给出各因素的弹性、集中指数与贡献占比。\n**常见错误**：分数秩按 y 自身而非经济状况排序，或不做标化直接跨人群比较 CI。\n\n---\n\n## 方法学速查手册 · D 类：因果推断与政策评估（8 个方法）\n\n> 片段文件，每条目采用统一模板：**适用 / 要点 / 代码（Stata + R）/ 报告要点 / 常见错误**；D1–D5 为完整速查，D6–D8 为压缩速查（详见对应 skill）。命令与选项随软件版本更新，正式运行前以 `help <cmd>`（Stata）或 `?<fn>`（R）为准。\n\n### D1 倾向得分匹配（PSM）\n**适用**：观察性数据中处理（治疗/政策暴露）分配非随机、但可观测混杂充分时，用倾向得分匹配估计平均处理效应（ATT/ATE）。\n**要点**：核心思想是用协变量 X 估计倾向得分 p(X) = P(T=1|X)，将处理组与对照组按得分匹配，使两组在可观测协变量上可比，近似「准随机化」。\n匹配前须检查**共同支撑**（common support）：两组 p(X) 分布应充分重叠，可画密度重叠图或用 `teffects overlap`。\n匹配后须检验**平衡性**：各协变量标准化均值差 SMD，|SMD| < 0.1 视为平衡，否则调整模型设定或更换匹配算法。\nPSM 只控制可观测混杂，不能解决未观测混杂。\n**代码**：\n\n```stata\n* Stata：1:1 近邻匹配估计 ATT（y 结局、t 处理、x1 x2 协变量）\nteffects psmatch (y) (t x1 x2), atet\ntebalance summarize          // 平衡性：SMD 与方差比\ntebalance density            // 共同支撑：倾向得分重叠图\n```\n\n```r\nlibrary(MatchIt)\nm <- matchit(t ~ x1 + x2, data = dat, method = \"nearest\")\nmd <- match.data(m)\nlm(y ~ t + x1 + x2, data = md, weights = weights)\ncobalt::love.plot(m, abs = TRUE)   # SMD 平衡图\n```\n\n**报告要点**：报告匹配算法与卡尺、匹配前后样本量与 SMD 平衡表，效应量附稳健标准误与 95% CI，并声明结果仅对可观测混杂成立。\n**常见错误**：把匹配后结果当作随机对照试验证据，或不报告共同支撑与平衡性检查。\n\n---\n\n### D2 工具变量（IV）\n**适用**：处理变量存在内生性（遗漏变量、反向因果、测量误差）且能找到有效工具变量 Z 时，用工具变量法估计因果效应（2SLS）。\n**要点**：有效 IV 需满足：①相关性——Z 与内生处理 T 强相关，一阶段 F 统计量 > 10（弱 IV 会放大偏误）；②排他性——Z 仅通过 T 影响 Y，不可直接检验，需靠研究设计论证；③同质性或单调性假设下，2SLS 识别的是依从者平均处理效应（LATE）。\n多工具变量时可用过度识别检验（Sargan/Hansen J）检验工具变量的有效性。\n工具变量的有效性首先取决于研究设计与背景论证，而非统计检验本身。\n2SLS 分两阶段：第一阶段 T 对 Z（及外生协变量）回归得拟合值，第二阶段 Y 对拟合值回归；标准误由 `ivregress`/`ivreg` 自动校正。\n弱 IV 时除 F 统计量外，可加做对弱工具变量稳健的推断（如 Anderson–Rubin 检验）。\nR 公式 y ~ t | z 中，`|` 右侧为工具变量，t 为内生处理。\n**代码**：\n\n```stata\n* Stata：2SLS（核心命令）\nivregress 2sls y (t = z), first vce(robust)\nestat firststage            // 一阶段 F，>10 为强工具变量\n* 多工具变量：ivregress 2sls y (t = z1 z2), first，再用 estat overid 做过度识别检验\n```\n\n```r\nlibrary(AER)\nm_iv <- ivreg(y ~ t | z, data = dat)\nsummary(m_iv, diagnostics = TRUE)   # 含弱工具变量诊断\n```\n\n**报告要点**：报告一阶段 F、工具变量的相关性与排他性论证、2SLS 系数及稳健标准误，并把效应解读为对依从者成立的 LATE。\n**常见错误**：弱工具变量（一阶段 F<10）仍下强因果结论，或把排他性假设当作可检验。\n\n---\n\n### D3 断点回归（RDD）\n**适用**：处理由连续运行变量是否越过临界值决定（如分数线录取、年龄阈值报销）时，估计断点附近的局部因果效应。\n**要点**：RDD 依据「断点附近个体近似随机落入两侧」，比较临界值两侧的局部平均结果之差。\n须做三类检验：①运行变量密度在断点处连续（无操纵，McCrary 检验）；②前定协变量在断点处平衡；③结果对带宽选择稳健。\n主流做法为局部线性回归 + 三角核权重，带宽按 MSE 最优或 Calonico–Cattaneo–Titiunik (2014) 方法选择，并报告 RD 图。\n效应是断点处的 LATE，向远离断点的人群外推需谨慎。\n模糊断点（fuzzy RD）以「是否越线」为工具变量估计：Stata `rdrobust y x, c(0) fuzzy(t)`，R `rdrobust` 加 `fuzzy = \"t\"`。\n**代码**：\n\n```stata\nssc install rdrobust\nssc install rddensity\nrdrobust y x, c(0)          // x 为中心化运行变量\nrdplot y x, c(0)            // RD 图\nrddensity x, c(0)           // 密度操纵检验\n```\n\n```r\nlibrary(rdrobust)\nrdrobust(y = dat$y, x = dat$x, c = 0)\nrddensity::rddensity(dat$x, c = 0)   # 操纵检验\n```\n\n**报告要点**：报告带宽选择方法与不同带宽下的稳健性、RD 图（断点两侧拟合线 + 置信区间）、操纵检验与协变量连续性检验。\n**常见错误**：不做密度与协变量连续性检验、不报带宽敏感性就下因果结论。\n\n---\n\n### D4 合成控制法（SCM）\n**适用**：政策只影响一个（或少数）处理单元、且有较长政策前后面板与多个未处理对照单元时，估计政策对处理单元的效应。\n**要点**：用对照组单元的加权组合构造「合成对照」，权重在政策前时期最小化处理单元与合成对照在结局及预测协变量上的差异；政策后两者的差距即政策效应轨迹。\n推断依靠**安慰剂检验**：把每个对照单元依次当作「假处理」单元重复估计，看真实效应是否显著大于假效应分布（可换算置换 p 值）。\n要求政策前时期足够长、合成拟合良好（政策前 RMSPE 小）。\n权重通常非负且和为 1；Stata `synth` 的 `nested` 选项与 R 侧嵌套求解参数（如 `optimxmethod`）控制权重求解（以帮助文档为准）。\n**代码**：\n\n```stata\nssc install synth\ntsset id time\nsynth y x1 x2 y(1990(1)1999), trunit(1) trperiod(2000) fig\n* 安慰剂检验：对每个对照单元重复 synth 比较效应轨迹（以 help synth 为准）\n```\n\n```r\ndp <- Synth::dataprep(foo = dat, predictors = c(\"x1\", \"x2\"), dependent = \"y\",\n                      unit.variable = \"id\", time.variable = \"time\",\n                      treatment.identifier = 1, controls.identifier = 2:20,\n                      time.predictors.prior = 1990:1999, time.optimize.ssr = 1990:1999)\ns1 <- Synth::synth(dp)\n```\n\n**报告要点**：报告合成对照的权重构成与政策前拟合优度（RMSPE）、政策后效应轨迹图，以及安慰剂/置换检验结果。\n**常见错误**：政策前拟合差（RMSPE 大）仍解读效应，或不做安慰剂检验。\n\n---\n\n### D5 事件研究法\n**适用**：政策冲击时点明确、需考察效应随时间的动态变化（事前趋势与事后轨迹）时使用，适用于面板或重复截面数据。\n**要点**：以相对事件时间（relative time）生成各期虚拟变量并与处理组交互，取政策前一期（-1）为参照。\n**事前系数应不显著**（平行趋势检验），事后系数轨迹刻画效应的动态演化；事件研究图（系数 + 置信区间）是标准呈现。\n基准设定为双向固定效应 + 聚类稳健标准误；交错采用（异质性处理时点）时双向固定效应估计可能有偏，应加做 Sun–Abraham (2021) 或 Callaway–Sant'Anna (2021) 稳健性。\n各期系数均相对 -1 期解读（-1 期系数被吸收进常数项）。\n正式平行趋势检验可对事前交互项做联合检验（`reghdfe` 后用 `test` 联合检验事前系数）。\n若为重复截面而非面板，个体固定效应不可用，改用群组固定效应并按群组聚类。\n**代码**：\n\n```stata\nssc install reghdfe\nssc install coefplot\nreghdfe y ib(-1).time#i.treat, absorb(id time) vce(cluster id)\ncoefplot, keep(*.time#1.treat)          // 事件研究图\n```\n\n```r\nlibrary(fixest)\nm <- feols(y ~ i(time, treat, ref = -1) | id + time, data = dat)\ncoefplot(m)                              // 事件研究图\n```\n\n**报告要点**：报告事件研究图（事前系数应为零、事后轨迹与显著性）、参照期与聚类层级；交错采用时说明稳健性检验。\n**常见错误**：只看平均效应不看动态轨迹，或事前趋势不平行仍解读为因果。\n\n---\n\n### D6 双重差分（DID）\n**适用**：政策在某时点只冲击处理组、且有政策前后数据时，比较两组变化之差估计处理效应。\n**要点**：识别假设为**平行趋势**（无政策时两组结局变化相同）；交互项 treat × post 的系数即 DID 估计量；交错采用时注意异质性处理效应偏误（见 D5 稳健性建议）。\n**代码**：\n```stata\n* Stata：2×2 DID（交互项系数即效应）\nreg y i.treat##i.post, vce(cluster id)\n```\n```r\n## R：fixest 双向固定效应 DID\nfeols(y ~ treat*post | id + time, data = dat, cluster = ~id)\n```\n**报告要点**：报告 DID 系数与聚类稳健标准误，附平行趋势检验（事件研究图）与安慰剂检验。\n**常见错误**：标准误不聚类或聚类层级错误，或不检验平行趋势。\n> 详见 DID skill：完整流程、平行趋势检验与稳健性设计。\n\n---\n\n### D7 间断时间序列（ITS）\n**适用**：单一单元（或合并总体）有政策前后多期连续时间序列时，检验政策的水平与斜率改变。\n**要点**：分段回归 Y = β0 + β1·time + β2·post + β3·time_after；β2 为水平（即时）改变，β3 为斜率（长期）改变；须用 Newey–West 或 Prais–Winsten 校正自相关（季节性需处理）。\n**代码**：\n```stata\ntsset time\ngen post = (time >= t0)          // t0 = 政策实施期\ngen time_after = (time - t0) * post\nnewey y time post time_after, lag(1)\n```\n```r\nm <- lm(y ~ time + post + time_after, data = dat)\nlmtest::coeftest(m, vcov = sandwich::NeweyWest(m, lag = 1))\n```\n**报告要点**：报告 β2、β3 与自相关校正方法，并画拟合线与无政策反事实对照。\n**常见错误**：忽略自相关导致标准误低估，或政策前观测点太少无法识别趋势。\n> 详见 ITS skill：完整流程、自相关诊断与季节性处理。\n\n---\n\n### D8 三重差分（DDD）\n**适用**：DID 中处理组与对照组存在不同时间趋势或额外混淆时，加入第三个维度（地区/人群/结果）作双重对照。\n**要点**：DDD = 处理 × 时间 × 第三维度的三重交互项系数，通过「差异中的差异的差异」消除两组各自时间趋势与第三维度的共同冲击。\n应同时报告各双重交互项，并说明第三维度剔除何种混淆。\n**代码**：\n```stata\n* Stata：DDD（三重交互项系数即效应）\nreg y i.treat##i.post##i.group, vce(cluster id)\n```\n```r\n## R：DDD 三重交互（主效应被固定效应吸收时仅保留三重交互）\nfeols(y ~ treat:post:group | id + time, data = dat, cluster = ~id)\n```\n**报告要点**：报告三重交互系数与各双重交互项，解释第三维度如何剔除混淆。\n**常见错误**：只报三重交互不报双重交互，或交互显著即强行因果解读而忽视机制。\n> 详见 DDD skill：完整流程、识别假设与稳健性设计。\n\n---\n\n## E 类：数据科学与预测建模（6 个方法）\n\n> 方法学速查手册 E 类条目片段（E1–E6）。每个方法按「适用 / 要点 / 代码（Stata + R）/ 报告要点 / 常见错误」展开；代码为可运行核心命令，示例数据与阈值仅作演示。E3/E4 为速查条目，完整流程详见机器学习 skill。\n\n### E1 缺失数据与多重插补\n\n**适用**：数据存在缺失值但需完整数据集进行回归/模型分析时，用多重插补替代删除法以减少偏倚与效能损失（如问卷调查、纵向随访、电子病历数据）。\n\n**要点**：缺失机制决定处理策略——完全随机缺失 MCAR（缺失与任何变量无关）、随机缺失 MAR（缺失与已观测变量有关，条件于已观测变量后可忽略）、非随机缺失 MNAR（缺失与缺失值本身有关）。多重插补（MI）用链式方程生成 m 个（建议 20–50 次，缺失比例越高 m 越大）完整数据集，分别分析后用 Rubin 规则合并点估计与方差，正确体现插补不确定性；优于均值填补与完整病例分析（后者仅在 MCAR 且缺失比例很小时基本无偏）。MNAR 下 MI 仍有偏，需做敏感性分析（如 worst-case、δ 调整）；插补模型应纳入与缺失性及结局相关的辅助变量。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nmi set mlong                                  // 声明多重插补 long 格式\nmi register imputed y x                       // 登记含缺失的待插补变量\nmi impute chained (regress) y (logit) x = z age, add(20) rseed(2026)\n                                              // y 连续用 regress、x 二分类用 logit；z age 为完整辅助变量\nmi estimate: regress y x age                  // 逐集估计 + Rubin 规则合并\n* ===== R =====\nlibrary(mice)\nimp <- mice(data, m = 20, seed = 2026)        # 链式方程多重插补\ndensityplot(imp)                              # 检查插补值分布\nfit <- with(imp, glm(y ~ x + age, family = binomial()))   # 逐集估计\npool(fit)                                     # Rubin 规则合并\n```\n\n**报告要点**：报告各变量缺失比例与缺失模式，明确缺失机制假设（MCAR/MAR/MNAR 判断依据）、插补模型与 m 值、Rubin 合并规则；并与完整病例分析并列作敏感性比较。\n\n**常见错误**：用均值/末次观测填补、只插补 1 次（m=1）当多重插补，或插补模型漏掉预测缺失与结局的关键变量。\n\n### E2 样本量与统计效能计算\n\n**适用**：研究设计阶段确定所需样本量、或评估既有样本的检验效能，保证结果可解释（RCT、两率比较、抽样调查、预测模型开发）。\n\n**要点**：先固定四个量：效应量（取最小临床重要差异 MCID，或文献/预试验值）、显著性水平 α（通常 0.05）、目标效能 1−β（通常 0.8/0.9）与设计参数（组间比、方差），已知任意三个可解出第四个。效应量须由专业判断或外部证据给定，不能用当前观测数据倒推（事后效能无意义）。连续结局用标准化均数差 d = (μ1−μ2)/σ，分类结局用率差或 OR；分层、整群与重复测量设计须乘设计效应 DE = 1 + (m−1)ICC 并预留脱落率。\n\n**代码**：\n\n```stata\n* ===== Stata =====\npower twomeans 100 110, sd(20) power(0.8)    // 两组均数 100 vs 110、SD 20 → 每组 n\npower twomeans 100 110, sd(20) n(64)         // 固定每组 n=64 → 检验效能\npower twoproportions 0.30 0.20, power(0.8)   // 两率 0.30 vs 0.20 → 每组 n\npower oneproportion 0.20 0.25                // 单组率 0.20 对目标 0.25\npower twomeans 100 110, sd(20) power(0.8) nratio(2)   // 组间比 1:2 设计\n* ===== R =====\nlibrary(pwr)\npwr.t.test(d = 0.5, sig.level = 0.05, power = 0.8, type = \"two.sample\")  // 每组 n\npwr.t.test(d = 0.5, sig.level = 0.05, n = 64, type = \"two.sample\")       // 固定 n → 效能\npwr.2p.test(h = ES.h(0.3, 0.2), sig.level = 0.05, power = 0.8)           // 两率\npwr.anova.test(k = 3, f = 0.25, sig.level = 0.05, power = 0.8)           // 多组方差分析\npwr.chisq.test(w = 0.1, df = 1, sig.level = 0.05, power = 0.8)           // 卡方检验\n```\n\n**报告要点**：报告四个要素（效应量及其来源、α、1−β、设计参数），给出每组所需样本量、考虑脱落后最终样本量；附效能曲线或关键参数敏感性表。\n\n**常见错误**：用观察到的效应量做“事后效能”论证、忽略脱落率与整群/重复测量的设计效应，或多重比较不校正 α。\n\n### E3 LASSO/弹性网与正则化\n\n**适用**：高维数据（变量数接近或超过样本量）下的变量筛选与惩罚回归，防止过拟合。\n\n**要点**：LASSO 用 L1 惩罚把部分系数压缩为 0，实现变量选择；岭回归用 L2 收缩但不稀疏；弹性网混合两者，变量高度相关时更稳。λ 用交叉验证选择（常取 1-SE 规则，模型更简约）；结局决定 family（连续/二分类）。LASSO 系数有收缩偏倚，P 值需谨慎（样本拆分或去偏）。完整预处理、调参与评估流程详见机器学习 skill。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nlasso linear y x1-x200, selection(cv) nolog    // 连续结局，CV 选 λ\nlasso logit y x1-x200, selection(cv)           // 二分类结局\n* ===== R =====\ncvfit <- glmnet::cv.glmnet(x, y, family = \"binomial\", alpha = 0.5)   // 弹性网\ncoef(cvfit, s = \"lambda.1se\")                  // 1-SE λ 的系数\n```\n\n**报告要点**：报告惩罚类型与 α、λ 选择规则（min/1se）与 CV 折数、最终保留变量及系数（注明标准化与否）。\n**常见错误**：把 LASSO 系数直接报显著性 P 值、在测试集上选 λ、连续变量不标准化。\n\n### E4 随机森林与 XGBoost\n\n**适用**：非线性、含复杂交互或高维的分类/回归预测任务，作为灵活强基线模型（风险预测、结局分类）。\n\n**要点**：随机森林 = 多棵决策树 bagging + 随机特征子集，抗过拟合、稳健，袋外（OOB）误差可作内部验证；XGBoost = 梯度提升迭代修正残差，精度常更高但对超参敏感、需早停。两者均须调参（树数/深度/学习率）并在独立测试集或交叉验证评估。完整调参、变量重要性与模型解释流程详见机器学习 skill。\n\n**代码**：\n\n```stata\n* ===== Stata =====\n* Stata 无内置实现；可经 PyStata 调用 Python（sklearn/xgboost）\n* ===== R =====\nlibrary(randomForest); library(xgboost); set.seed(2026)\nrf <- randomForest(y ~ ., data = train, ntree = 500, importance = TRUE)   # 分类/回归自动识别\nbst <- xgboost(data = xgb.DMatrix(as.matrix(train_x), label = train_y),\n               nrounds = 100, eta = 0.1, max_depth = 3, objective = \"binary:logistic\")\n```\n\n**报告要点**：报告调参范围、重抽样策略（CV/独立测试集）与区分度、校准指标（AUC、Brier）。\n**常见错误**：只在训练集报告精度（过拟合乐观），或不做调优直接套用默认参数。\n\n### E5 聚类分析（K-means/层次聚类）\n\n**适用**：无监督地将个体或变量分成同质组，用于人群分型（患者亚组、健康素养/就医行为类型）、变量降维与问卷条目归类。\n\n**要点**：分析前须标准化（量纲不同时）；K-means 将样本迭代分配到最近质心、最小化组内平方和，需预先指定 K；层次聚类（凝聚/分裂）不预设 K，按距离逐层合并并用树状图展示。K 的选择：肘部法（组内平方和随 K 的拐点）、轮廓系数（越接近 1 越好）或树状图截断，最终由专业可解释性定夺。结果依赖距离度量（欧氏/曼哈顿）、标准化与初始值，须设种子并比较多种方案验证稳定性。\n\n**代码**：\n\n```stata\n* ===== Stata =====\ncluster kmeans x1 x2, k(3) start(krandom(2026)) name(k3)   // K-means（x1 x2 宜先标准化）\ncluster stop, rule(duda)                      // 肘部指标辅助选 K（Duda-Hart）\ncluster wardslinkage x1 x2, name(hc)          // 层次聚类（Ward 法）\ncluster dendrogram hc, labels(id)             // 树状图\ncluster generate g = groups(k3)               // 保存 K-means 分组结果\n* ===== R =====\nset.seed(2026); km <- kmeans(scale(x), centers = 3, nstart = 25)   # K-means（scale 标准化）\nd <- dist(scale(x)); hc <- hclust(d, method = \"ward.D2\")           # 层次聚类（Ward 法）\nplot(hc); table(cutree(hc, k = 3), km$cluster)                     # 树状图；两法结果交叉\nlibrary(cluster)\nmean(silhouette(km$cluster, dist(scale(x)))[, 3])                  # 平均轮廓系数（选 K）\n```\n\n**报告要点**：报告标准化方式、距离度量与聚类方法、K 的选择依据（肘部/轮廓系数/树状图）与各组样本量；描述各组特征并验证稳定性（不同种子/子样本重复）。\n\n**常见错误**：不标准化直接聚类、凭主观随意定 K 不报告依据，或聚类后不验证稳定性与可解释性。\n\n### E6 贝叶斯统计入门\n\n**适用**：需要结合先验知识、小样本推断、或输出参数完整后验分布与不确定性区间的分析（网状 meta 分析、卫生经济学评价、适应性设计、层次模型）。\n\n**要点**：贝叶斯推断 = 后验 ∝ 先验 × 似然，先验（文献/专家/无信息弱信息）与数据结合更新信念；区间用 95% 可信区间（credible interval：参数有 95% 概率落于其中），区别于频率学派的置信区间。用 MCMC 抽样近似后验，必须检查收敛（链轨迹混合、R-hat < 1.01、有效样本量 ESS 充足）并报告链数、迭代数与预热期。先验选择需说明依据并做先验敏感性分析（不同先验下结论是否稳健）。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nbayes, mcmcsize(10000) rseed(2026): regress y x   // 默认弱信息先验的贝叶斯回归\nbayesgraph diagnostics {y:x}                      // 轨迹/自相关/R-hat 收敛诊断\nbayesstats ess                                   // 有效样本量\nbayesstats summary, credible                     // 后验均值与 95% 可信区间\nbayes, prior({y:x}, normal(0, 10)) rseed(2026): regress y x   // 自定义先验 → 敏感性分析\n* ===== R =====\nlibrary(brms)\nfit <- brm(y ~ x, data = d, chains = 4, iter = 2000, warmup = 1000, seed = 2026)\nsummary(fit)                                 # 后验均值、95% CI、R-hat、ESS\nplot(fit)                                    # 链轨迹与后验密度\nhypothesis(fit, \"x > 0\")                     # 后验概率 P(x > 0)\n```\n\n**报告要点**：报告先验分布及依据、MCMC 设置（链数/迭代/预热/种子）与收敛指标（R-hat、ESS）；效应量给出后验均值与 95% 可信区间，附先验敏感性分析。\n\n**常见错误**：把可信区间当置信区间解释、不检查收敛就下结论，或先验无依据且不做敏感性分析。\n\n---\n\n## 方法学速查手册 · F 类：社会医学与卫生经济学理论框架（10 个方法）\n\n> 理论框架类不做数值计算代码，统一用「论文应用模板」：变量清单 / 分析段落提纲 / 简化估计式，能配 Stata / R 示例的一律给出。每个理论都要落到「变量选择—分析—报告」的论文流程上，而不是停在名词介绍。\n\n### F1 Andersen 卫生服务利用行为模型\n\n**适用**：解释与预测个体卫生服务利用（是否就诊、就诊次数、慢病服务利用），为就医行为与卫生服务研究提供变量选择框架。\n\n**要点**：模型由三类因素构成——倾向特征（predisposing：年龄、性别、教育、健康信念）、使能资源（enabling：收入、医保、机构可及性）、需要因素（need：自评健康、疾病状况），其中需要因素通常对利用的解释力最强。初始模型（1960s）后经多轮扩展，加入卫生系统、环境与健康行为/结局（BMH 版）。论文用法：按三因素组织自变量并分组报告，检验「需要主导」假设。\n\n**代码**（论文应用模板：三因素变量清单 + 回归）：\n\n```stata\n* Andersen 三因素变量清单（就诊行为研究示例）\n* 倾向特征 predisposing：age 年龄、female 性别(0/1)、edu 教育年限\n* 使能资源 enabling：lnincome 家庭收入对数、insured 是否参保(0/1)、dist 距最近机构(km)\n* 需要因素 need：sahealth 自评健康(1-5)、chronic 慢病种数\nlogit visit age female edu lnincome insured dist sahealth chronic, or\n```\n\n**报告要点**：理论框架部分画三因素模型图，逐项说明本研究变量与模型的对应关系；讨论中用「需要因素是否主导、使能因素是否受限」呼应模型预测并推导政策含义。\n\n**常见错误**：三因素不齐却声称「基于 Andersen 模型」；只列变量不说明归属与依据，模型沦为装饰。\n\n### F2 Grossman 健康需求模型\n\n**适用**：把健康视为人力资本，分析健康投资的决定因素及健康与教育、收入、年龄的关系，用于健康不平等与老年健康研究。\n\n**要点**：健康既是消费品（健康本身产生效用）又是投资品（健康时间增加劳动供给）；健康存量随年龄折旧，个体通过医疗服务等投资维持存量，投资量由工资率、教育效率与医疗服务价格决定。论文用法：用模型说明健康方程为何必须控制收入、教育与年龄，并据此提出机制性假设（教育提高健康生产效率）。\n\n**代码**（论文应用模板：需求简化式示意）：\n\n```stata\n* 健康需求简化式：H = f(工资率 w, 教育 E, 年龄 t, 医疗服务价格 p)\n* 预期：教育、工资系数为正（效率与投资回报），年龄为负（折旧），价格为负\nreg health lnwage edu age age2 lnprice\n* 教育→健康的机制检验：加入知识/行为中介后看 edu 系数变化\nreg health edu age\nreg health edu age knowl healthbehavior\n```\n\n**报告要点**：在理论框架部分以「健康存量—投资」逻辑引出变量；讨论中解释教育与年龄的系数方向如何与折旧、效率机制一致。\n\n**常见错误**：与 Andersen 模型混用（前者是需求决定机制，后者是利用分析框架）；只做相关回归不交代机制。\n\n### F3 健康信念模型 HBM\n\n**适用**：解释与干预个体预防性健康行为（疫苗接种、筛查、体检、遵医服药），为问卷维度设计与干预设计提供依据。\n\n**要点**：行为由六个构念驱动——感知易感性、感知严重性、感知收益、感知障碍、行动线索、自我效能（后两者为修订版加入）。论文用法：按维度编制 Likert 量表条目（每维度 ≥ 3 题，取均值入模），以预防行为为因变量做 logistic 回归或结构方程。\n\n**代码**（论文应用模板：维度变量清单）：\n\n```stata\n* HBM 维度量表（Likert 1-5，各维度多题取均值）与预防行为模型\n* perc_sus 感知易感性 | perc_sev 感知严重性 | perc_ben 感知收益 | perc_bar 感知障碍\n* cue 行动线索(0/1) | selfeff 自我效能\nlogit vaccinate perc_sus perc_sev perc_ben perc_bar cue selfeff, or\n```\n\n**报告要点**：方法部分报各维度的信度（Cronbach's α）与效度；讨论按维度解释作用方向，并把「感知障碍最显著」等结果转化为干预靶点。\n\n**常见错误**：每维度只设 1 题且不报信效度；用 HBM 解释治疗行为（HBM 针对预防行为，治疗行为宜用 TPB）。\n\n### F4 计划行为理论 TPB\n\n**适用**：解释由行为意向驱动的健康行为（锻炼、戒烟、手卫生、用药依从），用于问卷编制与行为干预研究。\n\n**要点**：行为意向由态度、主观规范、知觉行为控制（PBC）三者决定；行为则由意向与 PBC 共同预测，且 PBC 可绕过意向直接作用于行为。论文用法：用结构方程模型（SEM）检验 TPB 全路径，报告路径系数与模型拟合指标。\n\n**代码**（论文应用模板：SEM 语法）：\n\n```stata\n* Stata sem：观测题项均值入模（单指标路径模型）\nsem (intention <- att sn pbc) (behavior <- intention pbc), standardized\nestat gof, stats(all)\n```\n\n```r\n## R lavaan 等价写法（多题项做潜变量时用）\nlibrary(lavaan)\nmodel <- 'intention ~ att + sn + pbc\n          behavior ~ intention + pbc'\nfit <- sem(model, data = d)\nsummary(fit, standardized = TRUE, fit.measures = TRUE)\n```\n\n**报告要点**：报告路径系数、R² 与 CFI/TLI/RMSEA；讨论中解释意向→行为缺口（intention-behavior gap）及 PBC 直接路径的干预含义。\n\n**常见错误**：用普通回归替代路径检验且不报拟合指标；遗漏「知觉行为控制→行为」这条直接路径。\n\n### F5 社会生态学模型\n\n**适用**：识别个体、人际、社区/组织、政策多层影响因素，为多水平干预设计（慢病管理、控烟、体力活动促进）提供框架。\n\n**要点**：行为由嵌套层级共同决定——个体内（知识、信念）、人际（家庭、同伴）、组织/社区（学校、工作场所、社区资源）、政策（法规、环境）。论文用法：按层级组织变量，用多水平模型同时估计个体层与群体层效应，干预建议对应各层级。\n\n**代码**（论文应用模板：分层变量 + 多水平模型）：\n\n```stata\n* 个体层：age sex edu | 人际层：fam_support 家庭支持(0-3) | 社区层：park_density 绿地密度\n* 随机截距两水平模型（个体嵌套于社区）\nmixed behavior age sex edu fam_support park_density || community: , mle\n* 跨层交互：社区环境调节个体因素\nmixed behavior age sex edu fam_support c.fam_support#c.park_density || community: , mle\n```\n\n**报告要点**：理论框架部分画出层级图并标出本研究落在哪几层；讨论按层级解读系数（个体层 vs 群体层）并给出分层干预建议。\n\n**常见错误**：声称用生态学框架却把社区变量当个体变量做 OLS（忽视组内相关、易犯生态谬误）；层级划分与数据结构不匹配。\n\n### F6 健康公平与正义理论（Rawls / Sen 可行能力）\n\n**适用**：为健康公平测量与卫生政策评价提供规范基础，用于健康不平等测量、医保待遇公平性与政策伦理分析。\n\n**要点**：Rawls 差别原则——社会经济不平等安排应使最不利者获益最大（maximin）；Sen 可行能力——评价的核心是「人实际能做到什么（functionings/capabilities）」而非资源占有。论文用法：理论部分明确采纳的正义观，并据此选择公平测量指标（如集中指数、公平缺口）与「有利变化」的判定标准。\n\n**代码**（论文应用模板：集中指数计算）：\n\n```stata\n* 集中指数 CI（便利回归法）：按收入排序后，2*σ²_rank*(h/μ) 对 rank 回归\nsort income\ngen rank = _n / _N\nquietly summarize h\nlocal mu = r(mean)\nquietly summarize rank\ngen lhs = 2 * r(Var) * (h / `mu')\nreg lhs rank\n* _b[rank] 即 CI（正=亲富，负=亲穷）；SSC 版：ssc install conindex; conindex h, rankvar(income)\n```\n\n**报告要点**：理论框架部分说明采纳 Rawls 或 Sen 的理由，并解释它如何决定公平指标选择与结果判定；讨论中回到所采纳的正义观检验结论。\n\n**常见错误**：公平测量指标与所宣称的正义理论脱节；把「公平」与「平等」混为一谈，未交代价值判断。\n\n### F7 健康社会决定因素框架（CSDH）\n\n**适用**：分析结构性/中间决定因素如何沿社会经济梯度造成健康不平等，用于健康不平等机制与「将健康融入所有政策」研究。\n\n**要点**：因果链为「社会经济政治环境 → 社会经济地位（SES）→ 中间决定因素（物质条件、行为、生物、心理社会）→ 健康」，卫生系统本身也是社会决定因素。论文用法：把变量明确归入结构层与中间层，用分解方法（Oaxaca-Blinder、集中指数分解）量化各因素对健康不平等的贡献。\n\n**代码**（论文应用模板：分层变量 + 分解）：\n\n```stata\n* 结构层：ses 教育年限/收入/职业 | 中间层：housing 居住条件、smoke 吸烟、stress 心理压力 | 结局：health\n* Oaxaca-Blinder 分解：高/低 SES 组健康差距分解为「禀赋效应 + 系数效应」\nssc install oaxaca\noaxaca health housing smoke stress, by(ses_group)\n```\n\n**报告要点**：在理论框架中画出 CSDH 因果链并把变量标注到对应层级；讨论中报告各决定因素的贡献排序，指出政策切入点（结构层优先）。\n\n**常见错误**：把 CSDH 当普通回归变量清单，不做分解或机制分析；结构层与中间层混排、因果方向写反。\n\n### F8 委托代理与供方诱导需求（SID）\n\n**适用**：解释医患信息不对称下供方诱导过度需求，用于卫生服务利用、过度医疗与医保控费研究。\n\n**要点**：医患信息不对称使医生同时充当患者代理人（决定需求）与服务提供者（获取收益），存在为自身利益诱导需求的动机；经典经验检验是「床位数/医生密度 → 利用量」正相关（Roemer 定律：造好的床位就会被填满）。论文用法：在控制需要与价格后，用供给资源变量的系数作为诱导需求的证据。\n\n**代码**（论文应用模板：简化估计式）：\n\n```stata\n* 供方诱导需求简化估计式：y = β0 + β1·beds + β2·need + β3·price + ε\n* β1 > 0 且在控制需要与价格后仍显著 → 诱导需求的证据\nreg visits beds_per1000 chronic lnprice insurance age\n* 稳健性：换供给指标（医生密度）重复\nreg visits doctors_per1000 chronic lnprice insurance age\n```\n\n**报告要点**：讨论中如实报告识别问题（供给与需求同时决定、地区未观测因素），说明缓解策略（工具变量、自然实验、滞后期）并谨慎表述因果。\n\n**常见错误**：把床位—利用正相关直接等同于诱导需求，忽视人口需要、价格与反向因果；不讨论内生性。\n\n### F9 市场失灵与政府干预（公共品 / 外部性 / 信息不对称）\n\n**适用**：论证政府干预卫生领域的必要性与方式选择，用于卫生政策分析、医保制度设计与公共卫生筹资研究。\n\n**要点**：卫生领域存在公共品（传染病防控、健康信息，非竞争非排他）、正外部性（疫苗接种、抗生素合理使用，私人收益低于社会收益）、信息不对称（医患关系诱导需求；保险市场逆选择与道德风险），市场配置失灵为政府干预提供理由。论文用法：把每条政策建议对应到具体失灵类型，形成「失灵—干预—机制」论证链。\n\n**代码**（论文应用模板：失灵—干预论证链提纲）：\n\n```text\n论文应用模板：市场失灵 → 对应卫生服务 → 干预手段（论证链）\n· 公共品：传染病监测、控烟立法（非竞争、非排他）→ 政府提供或强制\n· 正外部性：疫苗接种（私人收益 < 社会收益，私人最优接种量低于社会最优）→ 补贴、免疫规划\n· 信息不对称①：医患（供方诱导需求）→ 支付方式改革、监管、信息公开\n· 信息不对称②：保险市场（逆选择、道德风险）→ 强制参保、共付与起付线设计\n```\n\n**报告要点**：在政策建议部分逐条注明依据的是哪类市场失灵及其证据；讨论政府干预本身可能的失灵（政府失灵）以保持分析平衡。\n\n**常见错误**：只罗列失灵类型不落到具体干预设计；不区分市场失灵与政府失灵，政策建议缺乏对应机制。\n\n### F10 制度变迁与路径依赖（医改分析）\n\n**适用**：解释医改政策演进、制度为何难以变革及政策扩散规律，用于卫生体制改革与医保制度变迁研究。\n\n**要点**：制度变迁受历史路径约束——初始制度选择通过规模报酬递增、学习效应、协调效应、适应性预期与利益集团锁定而自我强化；制度突破依赖关键节点或外部冲击。论文用法：以政策时间线证据识别锁定机制与变迁触发点，解释医改反复或试点—推广过程。\n\n**代码**（论文应用模板：制度变迁分析提纲）：\n\n```text\n论文应用模板：医改制度变迁分析提纲\n1) 锁定起点：初始制度安排（如公费/劳保/新农合分轨起点）与关键节点（2009 新医改）\n2) 自我强化机制：规模报酬递增、沉没成本、利益集团、适应性预期\n3) 锁定证据：政策时间线——改革方案反复收敛于既有路径（如碎片化格局持续）\n4) 变迁触发：外部冲击（疫情、财政压力）或内部矛盾积累 → 制度突破/渐进调整\n```\n\n**报告要点**：用时间线表格或图呈现制度沿革，逐条对应锁定机制；讨论说明本案例中路径依赖的强度与突破条件，避免历史决定论。\n\n**常见错误**：把路径依赖当「历史决定论」只叙述不分析机制；时间线证据与机制脱节，未说明变迁触发条件。\n\n---\n\n\n## G 类：定性与结构方程专题（4 个）\n\n### G1 扎根理论（Grounded Theory）\n\n**适用**：从访谈/观察/文本材料中归纳建构理论（医患体验、政策执行过程、患者旅程）。\n\n**要点**：三级编码——开放式编码（贴标签）、主轴编码（范畴与关系）、选择性编码（核心范畴与故事线）；理论抽样与饱和（新资料不再产生新范畴）；备忘录贯穿全程。Strauss-Corbin 偏结构化程序，Charmaz 偏建构主义。质性分析以**人工编码为主**，软件（NVivo/MAXQDA）与文本挖掘只是辅助；报告遵循 COREQ。\n\n**代码**：R 文本辅助（词频与共现），编码本身仍由人完成：\n\n```r\nlibrary(quanteda)\ncorp <- corpus(data.frame(text = interviews$text, doc_id = interviews$id))\ndfm(corp, remove = stopwords(\"zh\"), remove_punct = TRUE) |>\n  textstat_frequency(n = 30)          # 高频词提示开放编码线索\n```\n\n**报告要点**：编码表（范畴-子范畴-原始引文）、理论模型图、饱和度说明、研究者立场。\n\n**常见错误**：只贴词频不做理论建构；样本量小却宣称饱和；混淆描述与理论。\n\n### G2 结构方程模型（SEM）\n\n**适用**：多个潜变量（如健康素养、自我效能）与其测量条目、结构路径的联合检验。\n\n**要点**：测量模型（CFA，效度：载荷、CR、AVE）与结构模型（路径）两步或联合估计；拟合指标 CFI/TLI ≥ 0.90、RMSEA < 0.08、SRMR < 0.08，修正指数仅用于理论允许的修正；样本量经验：每个自由参数 10–20 例。\n\n**代码**：\n\n```stata\n* Stata：三潜变量 SEM\nsem (HL -> x1 x2 x3) (SE -> y1 y2 y3) (AD -> z1 z2 z3) (HL -> SE) (SE -> AD), method(mlmv)\nestat gof\n```\n\n```r\n# R：lavaan 等价写法\nlibrary(lavaan)\nmodel <- ' HL =~ x1 + x2 + x3\n           SE =~ y1 + y2 + y3\n           AD =~ z1 + z2 + z3\n           SE ~ HL\n           AD ~ SE '\nfit <- sem(model, data = dat)\nfitMeasures(fit, c(\"cfi\",\"tli\",\"rmsea\",\"srmr\"))\n```\n\n**报告要点**：模型图、载荷与路径系数表（标准化）、拟合指标与判断标准、测量不变性（如做群组比较）。\n\n**常见错误**：把探索性结果当验证性模型报告；只看卡方显著性；无理论依据乱加修正路径。\n\n### G3 多层 / 多重嵌套 SEM（Multilevel SEM）\n\n**适用**：嵌套数据（患者—科室—医院、居民—社区）的潜变量与结构关系，区分组内与组间效应。\n\n**要点**：先检验 ICC 判断是否需要多层；组内/组间变异分解；样本量经验：组数至少 30–50，组内个体可少；随机截距与随机斜率设定要有理论依据。\n\n**代码**：\n\n```stata\n* Stata：gsem 多层（医院随机截距）\ngsem (HL -> x1 x2 x3) (SE -> y1 y2 y3) (SE <- HL M1[hospital]), vce(cluster hospital)\n```\n\n```r\n# R：lavaan 双层写法\nlibrary(lavaan)\nmodel2 <- '\n  level: 1\n    SEw =~ y1 + y2 + y3\n    SEw ~ HLw\n  level: 2\n    SEb =~ y1 + y2 + y3\n    SEb ~ HLb '\nfit2 <- sem(model2, data = dat, cluster = \"hospital\")\n```\n\n**报告要点**：ICC、组内与组间两层结果分表、层级说明与软件语法对照。\n\n**常见错误**：组数很少却做多层 SEM；把组间结论套到个体；忽略聚类稳健标准误。\n\n### G4 fsQCA 模糊集定性比较分析\n\n**适用**：中小样本、多条路径达到同一结果（组态视角），如「基层能力建设成效」的条件组合：资源投入、人员配置、激励机制、信息化。\n\n**要点**：校准（直接法三锚点：完全隶属/交叉点/完全不隶属）；必要条件（一致性 ≥ 0.9）与充分条件（一致性 ≥ 0.75–0.8）；真值表与最小化得到复杂解/简约解/中间解；覆盖率（原始/唯一）；与回归互补：fsQCA 问「哪些组合」，回归问「净效应」。\n\n**代码**：\n\n```r\nlibrary(QCA)\ndat2 <- data.frame(成效 = calibrate(score, c(60, 75, 90)),\n                   资源 = calibrate(res, c(10, 20, 30)),\n                   人员 = calibrate(staff, c(5, 10, 15)),\n                   激励 = calibrate(inc, c(0.2, 0.5, 0.8)),\n                   信息化 = calibrate(it, c(1, 3, 5)))\ntt <- truthTable(dat2, outcome = \"成效\", n.cut = 1, incl.cut = 0.8)\nminimize(tt, include = \"?\")            # 中间解\n```\n\n```stata\n* Stata：SSC 的 fuzzy 包做校准等（以 help fuzzy 为准）\nssc install fuzzy\nfzcalc zy, low(60) cross(75) high(90)\n```\n\n**报告要点**：校准锚点与理由、真值表、各解的组态表、一致性与覆盖率、稳健性（锚点微调/阈值变动）。\n\n**常见错误**：校准锚点拍脑袋不给理由；把组态解当净效应做因果推断；样本与条件数比例失衡。\n\n> 以上 4 条的完整教程（含完整模拟数据与更多代码）见 **sem-qca-qualitative-skill.md**。\n\n\n---\n\n## 深度 Skill 目录（配套教程）\n\n| Skill | 文件 | 一句话 |\n| --- | --- | --- |\n| 卫生经济学评价教程 | health-economics-tutorial-skill.md | CEA/CUA 总纲（9 步，含 CHEERS 报告） |\n| DCE 设计助手 | dce-design-assistant-skill.md | 离散选择实验正交/D-效率设计与 clogit/mixlogit/潜类别 |\n| Markov 模型助手 | markov-model-skill.md | 队列模拟成本-效果分析（ICER、PSA） |\n| 机器学习助手 | machine-learning-skill.md | 费用预测与风险分层（LASSO/RF/XGBoost/SHAP） |\n| DID 双重差分助手 | did-skill.md | 政策效果评估（含多期 csdid） |\n| ITS 间断时间序列助手 | its-skill.md | 单序列干预的水平与趋势变化 |\n| DDD 三重差分助手 | ddd-skill.md | 双重差分之差 |\n| Meta 分析助手 | meta-analysis-skill.md | 系统评价效应合并与异质性 |\n| 定性方法与结构方程专题 | sem-qca-qualitative-skill.md | 扎根理论 / SEM / 多层SEM / fsQCA |\n| 学术写作规范润色 | academic-copyediting-skill.md | 中文学术文本规范润色 |\n\n## 常见错误清单（跨方法通用）\n\n- [ ] 方法选择先于研究问题（应该反过来：问题 → 设计 → 方法）\n- [ ] 只报告系数不报告假设检验（平行趋势、IIA、比例风险等）\n- [ ] 标准误层级与数据层级不符（未 cluster 到合适的层级）\n- [ ] 把「相关/预测」当「因果」表述\n- [ ] 第三方命令未注明来源与版本（ssc / R 包版本）\n- [ ] 敏感性分析只做一种或不做\n- [ ] 效应量只报 p 值不报置信区间与实际意义（临床/经济意义）\n- [ ] 理论与变量脱节（理论框架当装饰）\n- [ ] 代码不可复现（未设种子、未保存 do/R 脚本与数据版本）\n- [ ] 报告模板生搬硬套，未按目标期刊调整\n\n## 使用提示\n\n- 先查「适用」定方法，再读「要点」定口径，最后照「代码」落地；报告前过一遍对应深度 Skill 的报告清单。\n- 本手册与十份深度 Skill 均为 MIT 许可的可移植提示词，可直接喂给大语言模型辅助起草方法学章节（AI 参与请如实披露）。\n\n---\n\n## 引用 / Citation\n\n中文：杜思娴. socialmedicineskills：社医卫管方法学速查手册 [EB/OL]. (2026). https://naturedu.github.io/socialmedicine/.\n\nEnglish: Du, S. (2026). *socialmedicineskills: A methods quick-reference for social medicine and health management* [Skill definition]. https://naturedu.github.io/socialmedicine/.","kind":"方法 Skill","locator":"方法手册","license":"MIT（仓库原有技能）","verified":"原有公开仓库"}},{"id":"studio-library:science-writing-2025","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"Delving into LLM-assisted writing in biomedical publications through excess vocabulary","summary":"该研究考察大量生物医学摘要中的词汇变化，发现部分风格词使用增多。它不是逐篇作者身份检测器，也没有证明全部使用短句能降低检测分数。本站将其作为警惕模板化词汇的背景，短句与克制表达是用户偏好和编辑原则。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"science-writing-2025","title":"Delving into LLM-assisted writing in biomedical publications through excess vocabulary","journal":"Science Advances","year":"2025","doi":"10.1126/sciadv.adt3813","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12219543/","text":"该研究考察大量生物医学摘要中的词汇变化，发现部分风格词使用增多。它不是逐篇作者身份检测器，也没有证明全部使用短句能降低检测分数。本站将其作为警惕模板化词汇的背景，短句与克制表达是用户偏好和编辑原则。","kind":"写作研究导读","locator":"本站原创导读","license":"导读为本站撰写；原论文见来源","verified":"2026-10-23"}},{"id":"studio-library:classic-1963-Kenneth","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"医疗的不确定性与福利经济学 / Uncertainty and the Welfare Economics of Medical Care","summary":"疾病和疗效的不确定性、信息差异及信任使医疗市场不同于一般商品市场。 用于分析保险、专业规范与制度安排；理论不能直接当作实证因果证据。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"classic-1963-Kenneth","title":"医疗的不确定性与福利经济学 / Uncertainty and the Welfare Economics of Medical Care","journal":"经典理论","year":"1963","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC2585909/","text":"疾病和疗效的不确定性、信息差异及信任使医疗市场不同于一般商品市场。 用于分析保险、专业规范与制度安排；理论不能直接当作实证因果证据。","kind":"经典导读","locator":"原作 AER 1963；链接为公开重印版","license":"本站原创导读；原作版权归原权利人","verified":"2026-10-23"}},{"id":"studio-library:classic-1942-William","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"社会保险及相关服务（Beveridge 报告） / Social Insurance and Allied Services","summary":"以系统性社会保障安排回应生活风险，构成理解英国战后福利制度的重要历史文本。 比较筹资、覆盖、待遇和治理时先明确历史背景，不把所有税收筹资制度等同一种模式。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"classic-1942-William","title":"社会保险及相关服务（Beveridge 报告） / Social Insurance and Allied Services","journal":"经典理论","year":"1942","url":"https://www.parliament.uk/about/living-heritage/transformingsociety/livinglearning/coll-9-health1/coll-9-health/","text":"以系统性社会保障安排回应生活风险，构成理解英国战后福利制度的重要历史文本。 比较筹资、覆盖、待遇和治理时先明确历史背景，不把所有税收筹资制度等同一种模式。","kind":"经典导读","locator":"英国议会档案入口","license":"本站原创导读；原作版权归原权利人","verified":"2026-10-23"}},{"id":"studio-library:classic-1966-Avedis","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"医疗质量评价 / Evaluating the Quality of Medical Care","summary":"从结构、过程和结果组织医疗质量评价。 把机构资源、诊疗行为与患者结局分别转成指标；不以结构指标替代实际效果。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"classic-1966-Avedis","title":"医疗质量评价 / Evaluating the Quality of Medical Care","journal":"经典理论","year":"1966","url":"https://www.milbank.org/quarterly/articles/evaluating-quality-medical-care/","text":"从结构、过程和结果组织医疗质量评价。 把机构资源、诊疗行为与患者结局分别转成指标；不以结构指标替代实际效果。","kind":"经典导读","locator":"1966 原作；出版方重印入口","license":"本站原创导读；原作版权归原权利人","verified":"2026-10-23"}},{"id":"studio-library:classic-1972-Michael","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"健康资本概念与健康需求 / On the Concept of Health Capital and the Demand for Health","summary":"将健康视为会折旧、可投资的资本，区分健康需求与医疗服务需求。 适合讨论教育、收入、时间投入与健康；具体测量和识别需要额外设计。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"classic-1972-Michael","title":"健康资本概念与健康需求 / On the Concept of Health Capital and the Demand for Health","journal":"经典理论","year":"1972","url":"https://www.journals.uchicago.edu/doi/abs/10.1086/259880","text":"将健康视为会折旧、可投资的资本，区分健康需求与医疗服务需求。 适合讨论教育、收入、时间投入与健康；具体测量和识别需要额外设计。","kind":"经典导读","locator":"Journal of Political Economy 80(2)，1972","license":"本站原创导读；原作版权归原权利人","verified":"2026-10-23"}},{"id":"studio-library:classic-1974-Marc","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"加拿大人健康的新视角（Lalonde 报告） / A New Perspective on the Health of Canadians","summary":"把生物、环境、生活方式与卫生服务组织共同纳入健康视野。 用于主动健康的多因素框架；避免把健康问题全部归因于个人行为。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"classic-1974-Marc","title":"加拿大人健康的新视角（Lalonde 报告） / A New Perspective on the Health of Canadians","journal":"经典理论","year":"1974","url":"https://www.canada.ca/en/public-health/services/health-promotion/population-health/a-new-perspective-on-health-canadians.html","text":"把生物、环境、生活方式与卫生服务组织共同纳入健康视野。 用于主动健康的多因素框架；避免把健康问题全部归因于个人行为。","kind":"经典导读","locator":"加拿大政府原始报告入口","license":"本站原创导读；原作版权归原权利人","verified":"2026-10-23"}},{"id":"studio-library:classic-1995-Ronald","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"再访卫生服务利用行为模型 / Revisiting the Behavioral Model and Access to Medical Care: Does It Matter?","summary":"以倾向、使能与需要因素组织卫生服务利用及可及性问题。 先映射构念与变量，再确定混杂、中介和结局；模型框架不能替代因果图。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"classic-1995-Ronald","title":"再访卫生服务利用行为模型 / Revisiting the Behavioral Model and Access to Medical Care: Does It Matter?","journal":"经典理论","year":"1995","url":"https://pubmed.ncbi.nlm.nih.gov/7738325/","text":"以倾向、使能与需要因素组织卫生服务利用及可及性问题。 先映射构念与变量，再确定混杂、中介和结局；模型框架不能替代因果图。","kind":"经典导读","locator":"1995 修订论述；不要误当作模型首次提出年份","license":"本站原创导读；原作版权归原权利人","verified":"2026-10-23"}},{"id":"studio-library:classic-2008-WHO","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"用一代人的时间弥合差距 / Closing the Gap in a Generation","summary":"把日常生活条件以及权力、资金和资源分配纳入健康公平行动。 适合构建结构因素—中间因素—健康结果路径，并提出可测量的公平指标。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"classic-2008-WHO","title":"用一代人的时间弥合差距 / Closing the Gap in a Generation","journal":"经典理论","year":"2008","url":"https://www.who.int/publications/i/item/9789241563703","text":"把日常生活条件以及权力、资金和资源分配纳入健康公平行动。 适合构建结构因素—中间因素—健康结果路径，并提出可测量的公平指标。","kind":"经典导读","locator":"WHO 2008 委员会报告","license":"本站原创导读；原作版权归原权利人","verified":"2026-10-23"}},{"id":"studio-library:skill-binary-studio","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"二元结局与边际效应","summary":"# 二元结局与边际效应\n\n区分优势比、风险比与风险差；报告有解释性的绝对概率。\n\n## 边界\n所有代码为教学模板，未在真实数据执行；请核对数据字典、版本和包依赖。\n\n## R 模板\n\n```r\n# df包含二元y、exposure、age\nfit <- glm(y ~ exposure + age, family=binomial(), data=df)\nsummary(fit)\nexp(cbind(OR=coef(fit), confint(fit)))\ndf0 <- transform(df,exposure=0); df1 <- transform(df,exposure=1)\nmean(predict(fit,df1,type=\"response\")-predict(fit,df0,type=\"response\"))\n# 上式为模型标准化差异，因果解释需额外识别假设；区间另用适当bootstrap\n```\n\n## STATA 模板\n\n```stata\nlogit y i.exposure c.age, vce(robust)\nmargins exposure\nmargins, dydx(exposure)\n* 优势比不等于风险比；设计、混杂控制和聚类层级需先确定\n```\n\n","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-binary-studio","title":"二元结局与边际效应","journal":"方法 Skill","text":"# 二元结局与边际效应\n\n区分优势比、风险比与风险差；报告有解释性的绝对概率。\n\n## 边界\n所有代码为教学模板，未在真实数据执行；请核对数据字典、版本和包依赖。\n\n## R 模板\n\n```r\n# df包含二元y、exposure、age\nfit <- glm(y ~ exposure + age, family=binomial(), data=df)\nsummary(fit)\nexp(cbind(OR=coef(fit), confint(fit)))\ndf0 <- transform(df,exposure=0); df1 <- transform(df,exposure=1)\nmean(predict(fit,df1,type=\"response\")-predict(fit,df0,type=\"response\"))\n# 上式为模型标准化差异，因果解释需额外识别假设；区间另用适当bootstrap\n```\n\n## STATA 模板\n\n```stata\nlogit y i.exposure c.age, vce(robust)\nmargins exposure\nmargins, dydx(exposure)\n* 优势比不等于风险比；设计、混杂控制和聚类层级需先确定\n```\n\n","kind":"方法 Skill","locator":"教学模板","license":"本站原创模板","url":"skills/binary-studio.md"}},{"id":"studio-library:skill-event-study-studio","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"交错 DID 与多时点事件研究","summary":"# 交错 DID 与多时点事件研究\n\n区分分组和时间效应；常规双向固定效应并不适用于所有异质交错处理。\n\n## 边界\n所有代码为教学模板，未在真实数据执行；请核对数据字典、版本和包依赖。\n\n## R 模板\n\n```r\n# 依赖 did；g 为首次处理年，未处理者为 0\nlibrary(did)\nfit <- att_gt(yname=\"y\", tname=\"year\", idname=\"id\", gname=\"g\",\n              data=df, control_group=\"notyettreated\", panel=TRUE,\n              clustervars=\"id\", bstrap=TRUE)\nes <- aggte(fit, type=\"dynamic\")\nsummary(es); ggdid(es)\n# 另检查提前反应、样本组成、同期政策与平行趋势；更高政策层级需相应聚类\n```\n\n## STATA 模板\n\n```stata\n* 需 csdid 与 drdid；g=首次处理年份，未处理为0\ncsdid y, ivar(id) time(year) gvar(g) notyet method(dripw)\nestat event\ncsdid_plot\n* 聚类层级、控制组与协变量需按具体政策分配结构设置\n```\n\n","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-event-study-studio","title":"交错 DID 与多时点事件研究","journal":"方法 Skill","text":"# 交错 DID 与多时点事件研究\n\n区分分组和时间效应；常规双向固定效应并不适用于所有异质交错处理。\n\n## 边界\n所有代码为教学模板，未在真实数据执行；请核对数据字典、版本和包依赖。\n\n## R 模板\n\n```r\n# 依赖 did；g 为首次处理年，未处理者为 0\nlibrary(did)\nfit <- att_gt(yname=\"y\", tname=\"year\", idname=\"id\", gname=\"g\",\n              data=df, control_group=\"notyettreated\", panel=TRUE,\n              clustervars=\"id\", bstrap=TRUE)\nes <- aggte(fit, type=\"dynamic\")\nsummary(es); ggdid(es)\n# 另检查提前反应、样本组成、同期政策与平行趋势；更高政策层级需相应聚类\n```\n\n## STATA 模板\n\n```stata\n* 需 csdid 与 drdid；g=首次处理年份，未处理为0\ncsdid y, ivar(id) time(year) gvar(g) notyet method(dripw)\nestat event\ncsdid_plot\n* 聚类层级、控制组与协变量需按具体政策分配结构设置\n```\n\n","kind":"方法 Skill","locator":"教学模板","license":"本站原创模板","url":"skills/event-study-studio.md"}},{"id":"studio-library:skill-longitudinal-studio","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"纵向轨迹、混合模型与多时点作图","summary":"# 纵向轨迹、混合模型与多时点作图\n\n重复测量应处理个体内相关；轨迹图、混合效应和 GEE 的目标不同。\n\n## 边界\n所有代码为教学模板，未在真实数据执行；请核对数据字典、版本和包依赖。\n\n## R 模板\n\n```r\n# 依赖 lme4 / ggplot2；df包含id time y group\nlibrary(lme4); library(ggplot2)\nfit <- lmer(y ~ time * group + (1 + time | id), data=df)\nsummary(fit)\nggplot(df, aes(time,y,group=id,colour=factor(group))) +\n geom_line(alpha=.10) +\n stat_summary(aes(group=group),fun=mean,geom=\"line\",linewidth=1.2) +\n theme_classic()\n# 检查非线性、缺失机制及随机效应结构；均值轨迹不等同潜在类别\n```\n\n## STATA 模板\n\n```stata\n* id time y group；先检查时间尺度与重复记录\nisid id time\nxtset id time\nmixed y c.time##i.group || id: time, covariance(unstructured)\nmargins group, at(time=(0 1 2 3))\nmarginsplot\n* 非正态结局","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-longitudinal-studio","title":"纵向轨迹、混合模型与多时点作图","journal":"方法 Skill","text":"# 纵向轨迹、混合模型与多时点作图\n\n重复测量应处理个体内相关；轨迹图、混合效应和 GEE 的目标不同。\n\n## 边界\n所有代码为教学模板，未在真实数据执行；请核对数据字典、版本和包依赖。\n\n## R 模板\n\n```r\n# 依赖 lme4 / ggplot2；df包含id time y group\nlibrary(lme4); library(ggplot2)\nfit <- lmer(y ~ time * group + (1 + time | id), data=df)\nsummary(fit)\nggplot(df, aes(time,y,group=id,colour=factor(group))) +\n geom_line(alpha=.10) +\n stat_summary(aes(group=group),fun=mean,geom=\"line\",linewidth=1.2) +\n theme_classic()\n# 检查非线性、缺失机制及随机效应结构；均值轨迹不等同潜在类别\n```\n\n## STATA 模板\n\n```stata\n* id time y group；先检查时间尺度与重复记录\nisid id time\nxtset id time\nmixed y c.time##i.group || id: time, covariance(unstructured)\nmargins group, at(time=(0 1 2 3))\nmarginsplot\n* 非正态结局可考虑GEE：xtgee y c.time##i.group, family(binomial) link(logit) corr(exchangeable) vce(robust)\n```\n\n","kind":"方法 Skill","locator":"教学模板","license":"本站原创模板","url":"skills/longitudinal-studio.md"}},{"id":"studio-library:skill-microsimulation-studio","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"个体微观模拟 · Microsimulation","summary":"# 个体微观模拟 · Microsimulation\n\n个体异质性、路径依赖与随机转移；演示仅使用虚构参数。\n\n## 边界\n所有代码为教学模板，未在真实数据执行；请核对数据字典、版本和包依赖。\n\n## R 模板\n\n```r\n# 教学模拟；参数不来自真实疾病，禁止直接作临床或政策结论\nset.seed(2026)\nN <- 10000; T <- 20\nstate <- rep(1L, N) # 1健康、2患病、3死亡\nage <- runif(N, 40, 60); qaly <- numeric(N)\nfor (t in seq_len(T)) {\n  old <- state\n  pdeath <- pmin(0.20, 0.005 * exp(0.06 * (age + t - 40)))\n  for (s in 1:2) {\n    ids <- which(old == s); u <- runif(length(ids))\n    pd <- pdeath[ids] * ifelse(s == 2, 1.5, 1)\n    psick <- if (s == 1) 0.03 else 0\n    stopifnot(all(pd + psick <= 1))\n    state[ids[u < pd]] <- 3L\n    state[ids[u >= pd & u < pd + psick]] <- 2L\n  }\n  # 半周期近似；贴现率仅为教学输入，实际需依据指","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-microsimulation-studio","title":"个体微观模拟 · Microsimulation","journal":"方法 Skill","text":"# 个体微观模拟 · Microsimulation\n\n个体异质性、路径依赖与随机转移；演示仅使用虚构参数。\n\n## 边界\n所有代码为教学模板，未在真实数据执行；请核对数据字典、版本和包依赖。\n\n## R 模板\n\n```r\n# 教学模拟；参数不来自真实疾病，禁止直接作临床或政策结论\nset.seed(2026)\nN <- 10000; T <- 20\nstate <- rep(1L, N) # 1健康、2患病、3死亡\nage <- runif(N, 40, 60); qaly <- numeric(N)\nfor (t in seq_len(T)) {\n  old <- state\n  pdeath <- pmin(0.20, 0.005 * exp(0.06 * (age + t - 40)))\n  for (s in 1:2) {\n    ids <- which(old == s); u <- runif(length(ids))\n    pd <- pdeath[ids] * ifelse(s == 2, 1.5, 1)\n    psick <- if (s == 1) 0.03 else 0\n    stopifnot(all(pd + psick <= 1))\n    state[ids[u < pd]] <- 3L\n    state[ids[u >= pd & u < pd + psick]] <- 2L\n  }\n  # 半周期近似；贴现率仅为教学输入，实际需依据指南\n  utility <- c(1, 0.7, 0)\n  qaly <- qaly + (utility[old] + utility[state])/2 / 1.03^(t-0.5)\n}\nprint(table(state)); print(mean(qaly))\n# 政策比较需共同随机数、多次重复、参数与一阶不确定性分开评估\n```\n\n## STATA 模板\n\n```stata\n* 教学微模拟；虚构参数，年度周期\nclear\nset seed 2026\nset obs 10000\ngen id = _n\ngen age = 40 + 20*runiform()\ngen state = 1\ngen qaly = 0\nforvalues t = 1/20 {\n  gen old = state\n  gen pd = min(.20, .005*exp(.06*(age+`t'-40)))\n  replace pd = 1.5*pd if old==2\n  gen u = runiform()\n  replace state = 3 if old<3 & u<pd\n  replace state = 2 if old==1 & u>=pd & u<pd+.03\n  gen u0 = cond(old==1,1,cond(old==2,.7,0))\n  gen u1 = cond(state==1,1,cond(state==2,.7,0))\n  replace qaly = qaly + (u0+u1)/2/1.03^(`t'-.5)\n  drop old pd u u0 u1\n}\ntab state\nsummarize qaly\n```\n\n","kind":"方法 Skill","locator":"教学模板","license":"本站原创模板","url":"skills/microsimulation-studio.md"}},{"id":"studio-library:skill-rdd-studio","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"断点回归 · RDD","summary":"# 断点回归 · RDD\n\n适用明确阈值分配规则；检查操纵、连续性、带宽与局部解释。\n\n## 边界\n所有代码为教学模板，未在真实数据执行；请核对数据字典、版本和包依赖。\n\n## R 模板\n\n```r\n# 依赖 rdrobust / rddensity；df 包含 y、running\nlibrary(rdrobust)\nlibrary(rddensity)\ncutoff <- 0 # 替换为真实制度阈值\nfit <- rdrobust(y=df$y, x=df$running, c=cutoff, p=1)\nsummary(fit)\nsummary(rddensity(X=df$running, c=cutoff))\nrdplot(y=df$y, x=df$running, c=cutoff)\n# 模糊断点需另传 fuzzy=df$treatment；不得用高阶全局多项式代替局部识别\n```\n\n## STATA 模板\n\n```stata\n* 依赖：ssc install rdrobust / rddensity（自行确认安装）\n* 数据含 y running；阈值 0 仅为模板\nrdrobust y running, c(0) p(1)\nrddensity running, c(0)\nrdplot y running, c(0)\n* 模糊断点：rdrobust y running, c(0) fuzzy(treatment)\n```\n\n","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-rdd-studio","title":"断点回归 · RDD","journal":"方法 Skill","text":"# 断点回归 · RDD\n\n适用明确阈值分配规则；检查操纵、连续性、带宽与局部解释。\n\n## 边界\n所有代码为教学模板，未在真实数据执行；请核对数据字典、版本和包依赖。\n\n## R 模板\n\n```r\n# 依赖 rdrobust / rddensity；df 包含 y、running\nlibrary(rdrobust)\nlibrary(rddensity)\ncutoff <- 0 # 替换为真实制度阈值\nfit <- rdrobust(y=df$y, x=df$running, c=cutoff, p=1)\nsummary(fit)\nsummary(rddensity(X=df$running, c=cutoff))\nrdplot(y=df$y, x=df$running, c=cutoff)\n# 模糊断点需另传 fuzzy=df$treatment；不得用高阶全局多项式代替局部识别\n```\n\n## STATA 模板\n\n```stata\n* 依赖：ssc install rdrobust / rddensity（自行确认安装）\n* 数据含 y running；阈值 0 仅为模板\nrdrobust y running, c(0) p(1)\nrddensity running, c(0)\nrdplot y running, c(0)\n* 模糊断点：rdrobust y running, c(0) fuzzy(treatment)\n```\n\n","kind":"方法 Skill","locator":"教学模板","license":"本站原创模板","url":"skills/rdd-studio.md"}},{"id":"studio-library:skill-sdid-studio","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"合成双重差分 · Synthetic DID","summary":"# 合成双重差分 · Synthetic DID\n\n结合单位与时间权重；要求适合的面板结构和有效对照。\n\n## 边界\n所有代码为教学模板，未在真实数据执行；请核对数据字典、版本和包依赖。\n\n## R 模板\n\n```r\n# 依赖 synthdid；df: unit,time,outcome,treated（处理后为1）\nlibrary(synthdid)\npm <- panel.matrices(df, unit=\"unit\", time=\"time\",\n                     outcome=\"outcome\", treatment=\"treated\")\ntau <- synthdid_estimate(pm$Y, pm$N0, pm$T0)\nprint(tau); plot(tau)\nse <- sqrt(vcov(tau, method=\"placebo\"))\nprint(se)\n# 此模板针对包支持的块状处理，不能直接套用任意交错实施\n```\n\n## STATA 模板\n\n```stata\n* 依赖 sdid；balanced panel，treated为实际受处理的单位-时间状态\nsdid outcome unit time treated, vce(placebo) seed(2026)\n* 检查对照池、处理前拟合、同期冲击和推断方法有效性\n```\n\n","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-sdid-studio","title":"合成双重差分 · Synthetic DID","journal":"方法 Skill","text":"# 合成双重差分 · Synthetic DID\n\n结合单位与时间权重；要求适合的面板结构和有效对照。\n\n## 边界\n所有代码为教学模板，未在真实数据执行；请核对数据字典、版本和包依赖。\n\n## R 模板\n\n```r\n# 依赖 synthdid；df: unit,time,outcome,treated（处理后为1）\nlibrary(synthdid)\npm <- panel.matrices(df, unit=\"unit\", time=\"time\",\n                     outcome=\"outcome\", treatment=\"treated\")\ntau <- synthdid_estimate(pm$Y, pm$N0, pm$T0)\nprint(tau); plot(tau)\nse <- sqrt(vcov(tau, method=\"placebo\"))\nprint(se)\n# 此模板针对包支持的块状处理，不能直接套用任意交错实施\n```\n\n## STATA 模板\n\n```stata\n* 依赖 sdid；balanced panel，treated为实际受处理的单位-时间状态\nsdid outcome unit time treated, vce(placebo) seed(2026)\n* 检查对照池、处理前拟合、同期冲击和推断方法有效性\n```\n\n","kind":"方法 Skill","locator":"教学模板","license":"本站原创模板","url":"skills/sdid-studio.md"}},{"id":"studio-library:skill-survival-studio","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"生存分析与竞争风险","summary":"# 生存分析与竞争风险\n\n定义时间零点、事件与删失；竞争风险不是简单独立删失。\n\n## 边界\n所有代码为教学模板，未在真实数据执行；请核对数据字典、版本和包依赖。\n\n## R 模板\n\n```r\nlibrary(survival)\n# event: 1事件 0删失；df为研究者数据\nfit <- coxph(Surv(time,event) ~ exposure + age, data=df)\nsummary(fit); cox.zph(fit)\nplot(survfit(Surv(time,event) ~ exposure,data=df))\n# 竞争事件用累积发生函数/适当竞争风险模型，不将1-KM当作累积发生率\n```\n\n## STATA 模板\n\n```stata\nstset time, failure(event==1)\nsts graph, by(exposure)\nstcox exposure age\nestat phtest, detail\n* 若 event==2 为竞争事件，明确目标后考虑：\n* stcrreg exposure age, compete(event==2)\n```\n\n","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-survival-studio","title":"生存分析与竞争风险","journal":"方法 Skill","text":"# 生存分析与竞争风险\n\n定义时间零点、事件与删失；竞争风险不是简单独立删失。\n\n## 边界\n所有代码为教学模板，未在真实数据执行；请核对数据字典、版本和包依赖。\n\n## R 模板\n\n```r\nlibrary(survival)\n# event: 1事件 0删失；df为研究者数据\nfit <- coxph(Surv(time,event) ~ exposure + age, data=df)\nsummary(fit); cox.zph(fit)\nplot(survfit(Surv(time,event) ~ exposure,data=df))\n# 竞争事件用累积发生函数/适当竞争风险模型，不将1-KM当作累积发生率\n```\n\n## STATA 模板\n\n```stata\nstset time, failure(event==1)\nsts graph, by(exposure)\nstcox exposure age\nestat phtest, detail\n* 若 event==2 为竞争事件，明确目标后考虑：\n* stcrreg exposure age, compete(event==2)\n```\n\n","kind":"方法 Skill","locator":"教学模板","license":"本站原创模板","url":"skills/survival-studio.md"}},{"id":"studio-library:dataset-CHARLS","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"CHARLS · 中国健康与养老追踪调查","summary":"以45岁及以上中老年人及其家庭为主要研究对象。\n['慢性病、心理健康与生活质量', '医疗服务利用、医疗保险与经济负担', '退休、养老金、家庭支持与健康老龄化']\n['方法建议：先核对各轮变量与权重，再开展加权描述和回归分析。', '方法建议：同一受访者跨期匹配后可考虑固定效应或混合效应模型；政策评估需额外核实政策时点、地区匹配权限和识别假设。']\n['在官方平台注册、签署数据使用协议并按轮次申请；获得授权后再使用。', '全国调查、两省试调查与生命历程调查的样本和问题不同，不能直接混为同一面板。', '需处理失访、代理回答和跨轮测量差异；完成调查不等于数据已公开。']","url":"index.html#library","source":"data/studio-library.json","details":{"id":"dataset-CHARLS","title":"CHARLS · 中国健康与养老追踪调查","journal":"数据源导航","year":"","kind":"数据源导航","locator":"官方数据目录；不含微观数据","text":"以45岁及以上中老年人及其家庭为主要研究对象。\n['慢性病、心理健康与生活质量', '医疗服务利用、医疗保险与经济负担', '退休、养老金、家庭支持与健康老龄化']\n['方法建议：先核对各轮变量与权重，再开展加权描述和回归分析。', '方法建议：同一受访者跨期匹配后可考虑固定效应或混合效应模型；政策评估需额外核实政策时点、地区匹配权限和识别假设。']\n['在官方平台注册、签署数据使用协议并按轮次申请；获得授权后再使用。', '全国调查、两省试调查与生命历程调查的样本和问题不同，不能直接混为同一面板。', '需处理失访、代理回答和跨轮测量差异；完成调查不等于数据已公开。']","url":"https://charls.pku.edu.cn/gy/gyxm.htm","license":"本站原创概述；数据使用遵循原项目条件"}},{"id":"studio-library:dataset-CLHLS","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"CLHLS · 中国老年健康影响因素跟踪调查（中国老年健康调查）","summary":"基线侧重80岁及以上高龄老人，2002年起扩展至65岁及以上；具体年龄范围和子样本须核对所用轮次。\n['高龄人群失能、认知与死亡风险', '家庭支持、居住安排与长期照护', '健康寿命与老年健康不平等']\n['方法建议：有可靠随访与死亡信息时可考虑生存分析、竞争风险或多状态模型。', '方法建议：对认知和功能变化可考虑纵向模型；先评估高龄样本结构、死亡选择和失访。']\n['官方2026年7月公告明确CLHLS 2021经北大开放研究数据平台提供，注册、申请并审核后使用；其他版本按平台说明申请。', '高龄样本结构和补充样本影响总体推断，不能直接把原始样本比例解释为全国老人患病率。', '分清历次调查版本、存活者调查和死亡者家属问卷。']","url":"index.html#library","source":"data/studio-library.json","details":{"id":"dataset-CLHLS","title":"CLHLS · 中国老年健康影响因素跟踪调查（中国老年健康调查）","journal":"数据源导航","year":"","kind":"数据源导航","locator":"官方数据目录；不含微观数据","text":"基线侧重80岁及以上高龄老人，2002年起扩展至65岁及以上；具体年龄范围和子样本须核对所用轮次。\n['高龄人群失能、认知与死亡风险', '家庭支持、居住安排与长期照护', '健康寿命与老年健康不平等']\n['方法建议：有可靠随访与死亡信息时可考虑生存分析、竞争风险或多状态模型。', '方法建议：对认知和功能变化可考虑纵向模型；先评估高龄样本结构、死亡选择和失访。']\n['官方2026年7月公告明确CLHLS 2021经北大开放研究数据平台提供，注册、申请并审核后使用；其他版本按平台说明申请。', '高龄样本结构和补充样本影响总体推断，不能直接把原始样本比例解释为全国老人患病率。', '分清历次调查版本、存活者调查和死亡者家属问卷。']","url":"https://chads.nsd.pku.edu.cn/xwdt/7bebe41b0be3414abcf58e5b7211a05b.htm","license":"本站原创概述；数据使用遵循原项目条件"}},{"id":"studio-library:dataset-CFPS","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"CFPS · 中国家庭追踪调查","summary":"家庭及其成员，含成人与少儿；不同年龄适用不同问卷或代答问卷。\n['家庭经济、教育与健康的代际联系', '收入、就业、社会保障与心理健康', '城乡差异、家庭变化与儿童发展']\n['方法建议：明确个人或家庭分析单位后，考虑面板固定效应、成长曲线或多层模型。', '方法建议：利用政策变化开展DID前，先验证处理组定义、样本可比性与平行趋势。']\n['官方数据平台注册并提交申请，审核后使用；备用入口为北大开放研究数据平台，两个平台账户不互通。', '公开数据与限制数据申请不同；具体地理标识和敏感变量权限需要单独核实。', '分家、家庭成员变化与问卷调整影响跨期匹配；必须查阅技术报告和权重说明。']","url":"index.html#library","source":"data/studio-library.json","details":{"id":"dataset-CFPS","title":"CFPS · 中国家庭追踪调查","journal":"数据源导航","year":"","kind":"数据源导航","locator":"官方数据目录；不含微观数据","text":"家庭及其成员，含成人与少儿；不同年龄适用不同问卷或代答问卷。\n['家庭经济、教育与健康的代际联系', '收入、就业、社会保障与心理健康', '城乡差异、家庭变化与儿童发展']\n['方法建议：明确个人或家庭分析单位后，考虑面板固定效应、成长曲线或多层模型。', '方法建议：利用政策变化开展DID前，先验证处理组定义、样本可比性与平行趋势。']\n['官方数据平台注册并提交申请，审核后使用；备用入口为北大开放研究数据平台，两个平台账户不互通。', '公开数据与限制数据申请不同；具体地理标识和敏感变量权限需要单独核实。', '分家、家庭成员变化与问卷调整影响跨期匹配；必须查阅技术报告和权重说明。']","url":"https://isss.pku.edu.cn/sjsj/cfpsxm/index.htm","license":"本站原创概述；数据使用遵循原项目条件"}},{"id":"studio-library:dataset-CGSS","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"CGSS · 中国综合社会调查","summary":"中国城乡居民；具体年龄条件、抽样范围及附加模块子样本以各轮说明为准。\n['社会阶层、公平感、信任与主观幸福感', '社会保障、健康差异与服务获得', '社会态度的年代变化与群体差异']\n['方法建议：考虑加权描述、广义线性模型及重复横截面趋势比较。', '方法建议：合并年份前检查题项、编码和抽样可比性；不要把不同年份受访者当作同一人的追踪面板。']\n['2026年官网公告指向人大CSSD新平台；实名认证并签署协议后获取公开数据，受限数据需申请审批。', '主题模块并非每轮都有，附加模块可能仅覆盖部分受访者。', '跨期趋势通常反映群体变化，不能直接解释为个人变化或因果效应。']","url":"index.html#library","source":"data/studio-library.json","details":{"id":"dataset-CGSS","title":"CGSS · 中国综合社会调查","journal":"数据源导航","year":"","kind":"数据源导航","locator":"官方数据目录；不含微观数据","text":"中国城乡居民；具体年龄条件、抽样范围及附加模块子样本以各轮说明为准。\n['社会阶层、公平感、信任与主观幸福感', '社会保障、健康差异与服务获得', '社会态度的年代变化与群体差异']\n['方法建议：考虑加权描述、广义线性模型及重复横截面趋势比较。', '方法建议：合并年份前检查题项、编码和抽样可比性；不要把不同年份受访者当作同一人的追踪面板。']\n['2026年官网公告指向人大CSSD新平台；实名认证并签署协议后获取公开数据，受限数据需申请审批。', '主题模块并非每轮都有，附加模块可能仅覆盖部分受访者。', '跨期趋势通常反映群体变化，不能直接解释为个人变化或因果效应。']","url":"https://cgss.ruc.edu.cn/xmjs/xmgk.htm","license":"本站原创概述；数据使用遵循原项目条件"}},{"id":"studio-library:dataset-CLASS","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"CLASS · 中国老年社会追踪调查","summary":"60周岁及以上老年人；2014年全国基线后开展多轮追踪。\n['养老服务需求、养老安排与社会支持', '社会参与、数字融入与心理健康', '代际关系、隔代照料和认知变化']\n['方法建议：针对个人与社区嵌套结构可考虑多层模型。', '方法建议：跨期匹配后可分析健康或照护需求变化；中介分析需说明时序与未测混杂限制。']\n['优先使用官网所指CSSD平台，按公开或受限级别取得权限；特定轮次也有研究计划与签署协议的申请说明。', '各轮存在模块简化或隔次调查，不能假定每轮变量齐全。', '数据协议限制原始微观数据再分发，获得个人使用权限不等于可嵌入公共网页。']","url":"index.html#library","source":"data/studio-library.json","details":{"id":"dataset-CLASS","title":"CLASS · 中国老年社会追踪调查","journal":"数据源导航","year":"","kind":"数据源导航","locator":"官方数据目录；不含微观数据","text":"60周岁及以上老年人；2014年全国基线后开展多轮追踪。\n['养老服务需求、养老安排与社会支持', '社会参与、数字融入与心理健康', '代际关系、隔代照料和认知变化']\n['方法建议：针对个人与社区嵌套结构可考虑多层模型。', '方法建议：跨期匹配后可分析健康或照护需求变化；中介分析需说明时序与未测混杂限制。']\n['优先使用官网所指CSSD平台，按公开或受限级别取得权限；特定轮次也有研究计划与签署协议的申请说明。', '各轮存在模块简化或隔次调查，不能假定每轮变量齐全。', '数据协议限制原始微观数据再分发，获得个人使用权限不等于可嵌入公共网页。']","url":"https://jkzgyjy.ruc.edu.cn/sjzy/CLASSsjsq/ade1ba3977354553a798c8f10221c2ad.htm","license":"本站原创概述；数据使用遵循原项目条件"}},{"id":"studio-library:dataset-CEPS","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"CEPS · 中国教育追踪调查","summary":"调查学生及其家长、教师、校领导；基线学校样本覆盖112所学校、438个班级。\n['家庭背景与教育、健康不平等', '学校环境、同伴关系与青少年发展', '教育经历、心理健康与生命历程']\n['方法建议：考虑学生—班级—学校的多层模型与聚类标准误。', '方法建议：分析追踪变化时核对学生标识及转学失访；同伴效应研究需论证分班机制，不能直接假定随机分班。']\n['通过人大CSSD平台注册认证、签署协议，按数据分级申请。', '是特定学校与队列调查，不能直接推广至所有年龄儿童或全部青年。', '公开轮次与问卷、追访范围可能不同，所需变量必须逐轮核对。']","url":"index.html#library","source":"data/studio-library.json","details":{"id":"dataset-CEPS","title":"CEPS · 中国教育追踪调查","journal":"数据源导航","year":"","kind":"数据源导航","locator":"官方数据目录；不含微观数据","text":"调查学生及其家长、教师、校领导；基线学校样本覆盖112所学校、438个班级。\n['家庭背景与教育、健康不平等', '学校环境、同伴关系与青少年发展', '教育经历、心理健康与生命历程']\n['方法建议：考虑学生—班级—学校的多层模型与聚类标准误。', '方法建议：分析追踪变化时核对学生标识及转学失访；同伴效应研究需论证分班机制，不能直接假定随机分班。']\n['通过人大CSSD平台注册认证、签署协议，按数据分级申请。', '是特定学校与队列调查，不能直接推广至所有年龄儿童或全部青年。', '公开轮次与问卷、追访范围可能不同，所需变量必须逐轮核对。']","url":"https://ceps.ruc.edu.cn/","license":"本站原创概述；数据使用遵循原项目条件"}},{"id":"studio-library:dataset-CHFS","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"CHFS · 中国家庭金融调查","summary":"中国家庭及其成员；具体轮次全国、省级代表性不完全相同。\n['医疗支出与家庭财务脆弱性', '养老、商业保险和社会保障', '收入财富不平等与家庭风险应对']\n['方法建议：分布分析可考虑加权分位数、贫困或不平等指标。', '方法建议：医疗支出等零值多且右偏的结果可评估两部模型；跨期分析先核对追踪样本和价格平减。']\n['通过中心官方数据网站注册并提交学术研究申请，配套问卷和说明以获批版本为准。', '2026年4月申请页列明2011—2021年开放申请，2023和2025年暂未对外开放；后续以新公告为准，校内绿色通道不等于全社会开放。', '健康维度需逐题查问卷，不能把家庭金融调查当作临床或疾病专门数据库。']","url":"index.html#library","source":"data/studio-library.json","details":{"id":"dataset-CHFS","title":"CHFS · 中国家庭金融调查","journal":"数据源导航","year":"","kind":"数据源导航","locator":"官方数据目录；不含微观数据","text":"中国家庭及其成员；具体轮次全国、省级代表性不完全相同。\n['医疗支出与家庭财务脆弱性', '养老、商业保险和社会保障', '收入财富不平等与家庭风险应对']\n['方法建议：分布分析可考虑加权分位数、贫困或不平等指标。', '方法建议：医疗支出等零值多且右偏的结果可评估两部模型；跨期分析先核对追踪样本和价格平减。']\n['通过中心官方数据网站注册并提交学术研究申请，配套问卷和说明以获批版本为准。', '2026年4月申请页列明2011—2021年开放申请，2023和2025年暂未对外开放；后续以新公告为准，校内绿色通道不等于全社会开放。', '健康维度需逐题查问卷，不能把家庭金融调查当作临床或疾病专门数据库。']","url":"https://chfs.swufe.edu.cn/sjzx/gksjk.htm","license":"本站原创概述；数据使用遵循原项目条件"}},{"id":"studio-library:dataset-CHNS","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"CHNS · 中国健康与营养调查","summary":"调查地区的家庭及成员，涵盖多个省份；地域范围和纳入对象须依所用轮次核对。\n['饮食转型、肥胖与慢性病风险', '城乡环境、收入与营养不平等', '生活方式、健康服务与生命历程']\n['方法建议：纵向重复测量可考虑混合效应或GEE模型。', '方法建议：营养暴露可探索膳食模式及剂量反应，需处理测量误差和随时间变化的混杂。']\n['个人、家庭等公开数据依项目网站规则获取；社区、县级和生物样本数据可能需协议、伦理审批及处理费用。', '地理坐标不直接发布，地理匹配需按项目规则申请服务；费用与流程以现行说明为准。', '不能因覆盖多个省份就声称全国代表性；不同轮次测量和开放范围需核实。']","url":"index.html#library","source":"data/studio-library.json","details":{"id":"dataset-CHNS","title":"CHNS · 中国健康与营养调查","journal":"数据源导航","year":"","kind":"数据源导航","locator":"官方数据目录；不含微观数据","text":"调查地区的家庭及成员，涵盖多个省份；地域范围和纳入对象须依所用轮次核对。\n['饮食转型、肥胖与慢性病风险', '城乡环境、收入与营养不平等', '生活方式、健康服务与生命历程']\n['方法建议：纵向重复测量可考虑混合效应或GEE模型。', '方法建议：营养暴露可探索膳食模式及剂量反应，需处理测量误差和随时间变化的混杂。']\n['个人、家庭等公开数据依项目网站规则获取；社区、县级和生物样本数据可能需协议、伦理审批及处理费用。', '地理坐标不直接发布，地理匹配需按项目规则申请服务；费用与流程以现行说明为准。', '不能因覆盖多个省份就声称全国代表性；不同轮次测量和开放范围需核实。']","url":"https://www.cpc.unc.edu/projects/china","license":"本站原创概述；数据使用遵循原项目条件"}},{"id":"studio-library:dataset-CLDS","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"CLDS · 中国劳动力动态调查","summary":"核心调查对象为样本家庭中15—64岁劳动力；具体轮次以技术手册为准。\n['就业质量、工作压力与健康', '迁移、社会保障与劳动参与', '社区环境、社会参与与劳动者福祉']\n['方法建议：分析工作与健康可考虑加权回归、多层模型或可匹配追踪样本的固定效应。', '方法建议：轮换面板需检查样本进入退出机制；就业选择与健康之间的双向关系需要单独处理。']\n['中山大学官方历史说明要求在社会科学调查中心网站注册申请；当前可申请轮次、入口可用性需联系项目组核实。', '历史的校内开放通知不代表相应年份已向社会开放。', '不是所有调查对象都在所有年份持续跟踪，不能未经核对生成平衡面板。']","url":"index.html#library","source":"data/studio-library.json","details":{"id":"dataset-CLDS","title":"CLDS · 中国劳动力动态调查","journal":"数据源导航","year":"","kind":"数据源导航","locator":"官方数据目录；不含微观数据","text":"核心调查对象为样本家庭中15—64岁劳动力；具体轮次以技术手册为准。\n['就业质量、工作压力与健康', '迁移、社会保障与劳动参与', '社区环境、社会参与与劳动者福祉']\n['方法建议：分析工作与健康可考虑加权回归、多层模型或可匹配追踪样本的固定效应。', '方法建议：轮换面板需检查样本进入退出机制；就业选择与健康之间的双向关系需要单独处理。']\n['中山大学官方历史说明要求在社会科学调查中心网站注册申请；当前可申请轮次、入口可用性需联系项目组核实。', '历史的校内开放通知不代表相应年份已向社会开放。', '不是所有调查对象都在所有年份持续跟踪，不能未经核对生成平衡面板。']","url":"https://isg.sysu.edu.cn/article/210","license":"本站原创概述；数据使用遵循原项目条件"}},{"id":"studio-library:dataset-CSS","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"CSS · 中国社会状况综合调查","summary":"中国城乡居民；各期对象年龄与抽样条件以调查问卷和技术文件为准。\n['社会公平、社会信任与公共服务评价', '就业、社会保障与生活质量', '阶层认同、社会参与和社会变迁']\n['方法建议：考虑调查加权回归、群体差异和重复横截面比较。', '方法建议：连续或纵贯调查不自动等于同一个体面板；拟用固定效应前先核实受访者跨期可链接性。']\n['官方历年说明指向中国社会质量基础数据库，需注册实名认证并申请；具体权限和轮次以平台为准。', '更替模块和编码随轮次变化，合并前核对可比性。', '本次项目正文和申请平台抓取超时，介绍及申请路径由中国社会科学网官方索引核对，未验证登录后下载功能。']","url":"index.html#library","source":"data/studio-library.json","details":{"id":"dataset-CSS","title":"CSS · 中国社会状况综合调查","journal":"数据源导航","year":"","kind":"数据源导航","locator":"官方数据目录；不含微观数据","text":"中国城乡居民；各期对象年龄与抽样条件以调查问卷和技术文件为准。\n['社会公平、社会信任与公共服务评价', '就业、社会保障与生活质量', '阶层认同、社会参与和社会变迁']\n['方法建议：考虑调查加权回归、群体差异和重复横截面比较。', '方法建议：连续或纵贯调查不自动等于同一个体面板；拟用固定效应前先核实受访者跨期可链接性。']\n['官方历年说明指向中国社会质量基础数据库，需注册实名认证并申请；具体权限和轮次以平台为准。', '更替模块和编码随轮次变化，合并前核对可比性。', '本次项目正文和申请平台抓取超时，介绍及申请路径由中国社会科学网官方索引核对，未验证登录后下载功能。']","url":"http://sociology.cssn.cn/css_sy/xmjs/202301/t20230128_5584409.html","license":"本站原创概述；数据使用遵循原项目条件"}},{"id":"studio-library:dataset-CMDS","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"CMDS · 中国流动人口动态监测调查","summary":"流入地流动人口及其家庭；不同年份和专题的年龄、流动及居住条件需要分别核对。\n['流动人口基本公共卫生服务利用', '医疗保障、健康档案与社会融合', '迁移、就业和居留意愿']\n['方法建议：可考虑加权回归与地区多层模型，明确流动人口选择机制。', '方法建议：跨年合并先确认问卷可比性，不能从“动态监测”名称推断同人追踪；宏观匹配先确认地区标识权限。']\n['按流动人口数据平台现行申请说明获取；本次官方转出平台返回502，当前受理安排与可申请年份未确认。', '国家数据中心的公开汇总产品与个体微观数据权限不同，不能相互替代。', '样本代表的是指定条件的流动人口，不能直接估计全国全部居民指标。']","url":"index.html#library","source":"data/studio-library.json","details":{"id":"dataset-CMDS","title":"CMDS · 中国流动人口动态监测调查","journal":"数据源导航","year":"","kind":"数据源导航","locator":"官方数据目录；不含微观数据","text":"流入地流动人口及其家庭；不同年份和专题的年龄、流动及居住条件需要分别核对。\n['流动人口基本公共卫生服务利用', '医疗保障、健康档案与社会融合', '迁移、就业和居留意愿']\n['方法建议：可考虑加权回归与地区多层模型，明确流动人口选择机制。', '方法建议：跨年合并先确认问卷可比性，不能从“动态监测”名称推断同人追踪；宏观匹配先确认地区标识权限。']\n['按流动人口数据平台现行申请说明获取；本次官方转出平台返回502，当前受理安排与可申请年份未确认。', '国家数据中心的公开汇总产品与个体微观数据权限不同，不能相互替代。', '样本代表的是指定条件的流动人口，不能直接估计全国全部居民指标。']","url":"https://www.geodata.cn/wjw/","license":"本站原创概述；数据使用遵循原项目条件"}},{"id":"studio-library:dataset-NHSS","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"NHSS · 全国卫生服务调查","summary":"全国抽样地区居民家庭；2023年第七次调查国家级样本覆盖31省156个样本县区。\n['医疗服务需要、利用与未满足需要', '卫生服务公平性和重点人群覆盖', '慢性疾病管理与老年照护需求']\n['方法建议：使用获准微观数据时可进行复杂抽样估计、两部模型或利用公平性分析。', '方法建议：跨轮比较需统一卫生服务指标、调查时间窗口及城乡口径；政策评价需额外构建有效对照。']\n['本次未核实到面向所有个人的微观数据直接下载渠道，不能承诺注册即获数据。', '获取链接为统计信息中心的研究公告入口；申请合作或委托研究需按当期公告及机构要求办理。', '公开报告、汇总表与微观调查原始数据是不同层级的资源。']","url":"index.html#library","source":"data/studio-library.json","details":{"id":"dataset-NHSS","title":"NHSS · 全国卫生服务调查","journal":"数据源导航","year":"","kind":"数据源导航","locator":"官方数据目录；不含微观数据","text":"全国抽样地区居民家庭；2023年第七次调查国家级样本覆盖31省156个样本县区。\n['医疗服务需要、利用与未满足需要', '卫生服务公平性和重点人群覆盖', '慢性疾病管理与老年照护需求']\n['方法建议：使用获准微观数据时可进行复杂抽样估计、两部模型或利用公平性分析。', '方法建议：跨轮比较需统一卫生服务指标、调查时间窗口及城乡口径；政策评价需额外构建有效对照。']\n['本次未核实到面向所有个人的微观数据直接下载渠道，不能承诺注册即获数据。', '获取链接为统计信息中心的研究公告入口；申请合作或委托研究需按当期公告及机构要求办理。', '公开报告、汇总表与微观调查原始数据是不同层级的资源。']","url":"https://www.nhc.gov.cn/mohwsbwstjxxzx/s8561/202309/9f4af2272bef48a1b83e56e194d3640d.shtml","license":"本站原创概述；数据使用遵循原项目条件"}},{"id":"studio-library:dataset-NBS","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"NBS · 国家统计局国家数据与统计年鉴","summary":"全国或相应地区、行业等统计单元；这是汇总数据平台，不是单一微观追踪队列。\n['地区卫生资源与经济社会发展差异', '人口老龄化、城镇化和公共服务配置', '政策实施前后的地区指标变化']\n['方法建议：口径可比时可构建地区面板、趋势分析或资源分布指标。', '方法建议：政策时点清晰且观测充足时可评估ITS、DID或合成控制适用性；先检查共同冲击和对照有效性。']\n['国家数据及年鉴可公开查询；具体下载能力以指标页面为准。', '注意统计修订、行政区划、单位与不变价口径，保留下载日期与原始注释。', '地区层面的关系不能直接解释为个人因果效应；与微观调查匹配还需要合法可用的地区标识。']","url":"index.html#library","source":"data/studio-library.json","details":{"id":"dataset-NBS","title":"NBS · 国家统计局国家数据与统计年鉴","journal":"数据源导航","year":"","kind":"数据源导航","locator":"官方数据目录；不含微观数据","text":"全国或相应地区、行业等统计单元；这是汇总数据平台，不是单一微观追踪队列。\n['地区卫生资源与经济社会发展差异', '人口老龄化、城镇化和公共服务配置', '政策实施前后的地区指标变化']\n['方法建议：口径可比时可构建地区面板、趋势分析或资源分布指标。', '方法建议：政策时点清晰且观测充足时可评估ITS、DID或合成控制适用性；先检查共同冲击和对照有效性。']\n['国家数据及年鉴可公开查询；具体下载能力以指标页面为准。', '注意统计修订、行政区划、单位与不变价口径，保留下载日期与原始注释。', '地区层面的关系不能直接解释为个人因果效应；与微观调查匹配还需要合法可用的地区标识。']","url":"https://data.stats.gov.cn/staticreq.htm?eqid=8342caf1001611ab0000000464568341&m=aboutctryinfo","license":"本站原创概述；数据使用遵循原项目条件"}},{"id":"studio-library:thesis-ruc-2025-wangxin","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"中国青年的独居与健康：影响、差异与机制","summary":"王鑫\n中国人民大学\n社会医学与卫生事业管理\n题名所示主题为中国青年独居与健康之间的关系、群体差异及作用机制；公告未提供摘要。\n题录（官方博士答辩公告；未取得摘要或全文）\n公告列明作者、题名、专业及2025年5月13日博士答辩安排；不等同于确认学位已授予。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"thesis-ruc-2025-wangxin","title":"中国青年的独居与健康：影响、差异与机制","journal":"博士论文题录","year":2025,"kind":"博士论文题录","locator":"博士答辩公告；未导入全文","text":"王鑫\n中国人民大学\n社会医学与卫生事业管理\n题名所示主题为中国青年独居与健康之间的关系、群体差异及作用机制；公告未提供摘要。\n题录（官方博士答辩公告；未取得摘要或全文）\n公告列明作者、题名、专业及2025年5月13日博士答辩安排；不等同于确认学位已授予。","url":"https://sph.ruc.edu.cn/xydt/tzgg/7d57ed82cf004b3b939a7417a630acd4.htm","license":"题录与核实范围内的概述；未导入论文全文"}},{"id":"studio-library:thesis-ruc-2025-liyifei","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"社会资本对流动人口健康的影响研究","summary":"李怡霏\n中国人民大学\n社会医学与卫生事业管理\n题名所示主题为社会资本与流动人口健康之间的关系；公告未提供摘要。\n题录（官方博士答辩公告；未取得摘要或全文）\n2025年博士答辩公告确认作者及题名；人大党委研究生工作部名单另列其为社会医学与卫生事业管理博士生；不等同于确认学位已授予。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"thesis-ruc-2025-liyifei","title":"社会资本对流动人口健康的影响研究","journal":"博士论文题录","year":2025,"kind":"博士论文题录","locator":"博士答辩公告；未导入全文","text":"李怡霏\n中国人民大学\n社会医学与卫生事业管理\n题名所示主题为社会资本与流动人口健康之间的关系；公告未提供摘要。\n题录（官方博士答辩公告；未取得摘要或全文）\n2025年博士答辩公告确认作者及题名；人大党委研究生工作部名单另列其为社会医学与卫生事业管理博士生；不等同于确认学位已授予。","url":"https://sph.ruc.edu.cn/xydt/tzgg/7d57ed82cf004b3b939a7417a630acd4.htm","license":"题录与核实范围内的概述；未导入论文全文"}},{"id":"studio-library:thesis-gxmu-2025-wangshuyun","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"农村基层医疗卫生服务能力评价指标体系的构建与实证研究","summary":"王舒云\n广西医科大学\n社会医学与公共卫生管理\n题名所示主题为农村基层医疗卫生服务能力的评价指标体系及实证应用；公告未提供摘要。\n题录（官方博士答辩公告；未取得摘要或全文）\n公告明确标为博士学位，答辩安排为2025年6月3日；专业名称按公告原文保留，不等同于确认学位已授予。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"thesis-gxmu-2025-wangshuyun","title":"农村基层医疗卫生服务能力评价指标体系的构建与实证研究","journal":"博士论文题录","year":2025,"kind":"博士论文题录","locator":"博士答辩公告；未导入全文","text":"王舒云\n广西医科大学\n社会医学与公共卫生管理\n题名所示主题为农村基层医疗卫生服务能力的评价指标体系及实证应用；公告未提供摘要。\n题录（官方博士答辩公告；未取得摘要或全文）\n公告明确标为博士学位，答辩安排为2025年6月3日；专业名称按公告原文保留，不等同于确认学位已授予。","url":"https://www.gxmu.edu.cn/xwgk/jzhy/P020250531545354806578.pdf","license":"题录与核实范围内的概述；未导入论文全文"}},{"id":"studio-library:thesis-gxmu-2025-yanglan","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"患者体验视角下三甲综合医院医疗质量评价体系构建与实证研究","summary":"杨兰\n广西医科大学\n社会医学与公共卫生管理\n题名所示主题为从患者体验角度建立并应用三甲综合医院医疗质量评价体系；公告未提供摘要。\n题录（官方博士答辩公告；未取得摘要或全文）\n公告明确标为博士学位，答辩安排为2025年6月3日；专业名称按公告原文保留，不等同于确认学位已授予。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"thesis-gxmu-2025-yanglan","title":"患者体验视角下三甲综合医院医疗质量评价体系构建与实证研究","journal":"博士论文题录","year":2025,"kind":"博士论文题录","locator":"博士答辩公告；未导入全文","text":"杨兰\n广西医科大学\n社会医学与公共卫生管理\n题名所示主题为从患者体验角度建立并应用三甲综合医院医疗质量评价体系；公告未提供摘要。\n题录（官方博士答辩公告；未取得摘要或全文）\n公告明确标为博士学位，答辩安排为2025年6月3日；专业名称按公告原文保留，不等同于确认学位已授予。","url":"https://www.gxmu.edu.cn/xwgk/jzhy/P020250531545354806578.pdf","license":"题录与核实范围内的概述；未导入论文全文"}},{"id":"studio-library:thesis-gxmu-2025-kangjing","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"东盟十国卫生体系比较研究","summary":"康静\n广西医科大学\n社会医学与公共卫生管理\n题名所示主题为东盟十国卫生体系的比较；公告未提供摘要或具体比较方法。\n题录（官方博士答辩公告；未取得摘要或全文）\n公告明确标为博士学位，答辩安排为2025年6月3日；专业名称按公告原文保留，不等同于确认学位已授予。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"thesis-gxmu-2025-kangjing","title":"东盟十国卫生体系比较研究","journal":"博士论文题录","year":2025,"kind":"博士论文题录","locator":"博士答辩公告；未导入全文","text":"康静\n广西医科大学\n社会医学与公共卫生管理\n题名所示主题为东盟十国卫生体系的比较；公告未提供摘要或具体比较方法。\n题录（官方博士答辩公告；未取得摘要或全文）\n公告明确标为博士学位，答辩安排为2025年6月3日；专业名称按公告原文保留，不等同于确认学位已授予。","url":"https://www.gxmu.edu.cn/xwgk/jzhy/P020250531545354806578.pdf","license":"题录与核实范围内的概述；未导入论文全文"}},{"id":"studio-library:thesis-pku-2024-wangqingbo","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"中国卫生筹资转型背景下政府卫生支出流向及其效应研究","summary":"王清波\n北京大学医学部\n社会医学与卫生事业管理\n将省级卫生筹资数据与CFPS家庭和个体数据相结合，研究政府卫生支出投向供方或需方与卫生资源配置、服务利用及费用负担的关系。\n题录及摘要（公开检索可读；未取得全文）\n题录明确学位为博士，完成及答辩日期为2024年5月20日；方法来自摘要的方法段。页面直连时有访问错误，题录与摘要由公开检索结果核实。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"thesis-pku-2024-wangqingbo","title":"中国卫生筹资转型背景下政府卫生支出流向及其效应研究","journal":"博士论文题录","year":2024,"kind":"博士论文题录","locator":"学位论文库；未导入全文","text":"王清波\n北京大学医学部\n社会医学与卫生事业管理\n将省级卫生筹资数据与CFPS家庭和个体数据相结合，研究政府卫生支出投向供方或需方与卫生资源配置、服务利用及费用负担的关系。\n题录及摘要（公开检索可读；未取得全文）\n题录明确学位为博士，完成及答辩日期为2024年5月20日；方法来自摘要的方法段。页面直连时有访问错误，题录与摘要由公开检索结果核实。","url":"https://xuewei.bjmu.edu.cn/docinfo.action?id1=e24596fb8031e604ad75d0db262801ae&id2=xp%252FPP%252Beb07M%253D","license":"题录与核实范围内的概述；未导入论文全文"}},{"id":"studio-library:thesis-sdu-2022-wangyi","collection":"studi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Stream）\n这是香港中文大学沙田校本部项目，不是香港中文大学（深圳）。研究院官网已公布2027/28入学申请：主轮截至2026年12月1日；补录轮截至2027年3月31日，是否考虑取决于余位。HKPFS另有RGC初申请及CUHK完整申请要求，具体时间见项目官网。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"school-cuhk-public-health","title":"香港中文大学 · 医学院·赛马会公共卫生及基层医疗学院","journal":"院系方向","year":"","kind":"院系方向","locator":"院系与项目官方资料","text":"卫生体系与政策\n卫生服务管理\n基层医疗\n健康公平\n患者体验与健康相关生命质量\nMPhil-PhD in Public Health（PhD Stream）\n这是香港中文大学沙田校本部项目，不是香港中文大学（深圳）。研究院官网已公布2027/28入学申请：主轮截至2026年12月1日；补录轮截至2027年3月31日，是否考虑取决于余位。HKPFS另有RGC初申请及CUHK完整申请要求，具体时间见项目官网。","url":"https://sphpc.cuhk.edu.hk/rpg-programme/","license":"本站原创整理，招生需查当年官方通知"}},{"id":"studio-library:school-hku-public-health","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"香港大学 · 李嘉诚医学院·公共卫生学院；卫生经济、政策与管理学分部","summary":"卫生经济与卫生技术评估\n卫生筹资\n基层医疗\n健康公平\n卫生资源配置\nDoctor of Philosophy 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卫生管理学院","summary":"公共卫生政策与管理\n基层卫生服务质量\n实施科学\n健康管理与健康促进\n医疗保险与长期照护\n公共卫生政策与管理（二级学科博士学位授权点；学院官网已核实）\n本条对应卫生管理学院，不与公共卫生学院混用。学院另有与葡萄牙里斯本大学学院合作培养的博士项目，须区分普通博士与中外合作项目。已核实学位授权点；本次未核实2027年招生名额、报名截止日期，不据官网旧培养方案填报当年条件。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"school-smu-health-management","title":"南方医科大学 · 卫生管理学院","journal":"院系方向","year":"","kind":"院系方向","locator":"院系与项目官方资料","text":"公共卫生政策与管理\n基层卫生服务质量\n实施科学\n健康管理与健康促进\n医疗保险与长期照护\n公共卫生政策与管理（二级学科博士学位授权点；学院官网已核实）\n本条对应卫生管理学院，不与公共卫生学院混用。学院另有与葡萄牙里斯本大学学院合作培养的博士项目，须区分普通博士与中外合作项目。已核实学位授权点；本次未核实2027年招生名额、报名截止日期，不据官网旧培养方案填报当年条件。","url":"https://portal.smu.edu.cn/wsglxy/xygk1/xyjj.htm","license":"本站原创整理，招生需查当年官方通知"}},{"id":"studio-library:school-csu-xiangya-public-health","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"中南大学 · 湘雅公共卫生学院·社会医学与卫生事业管理学系","summary":"社会行为与健康\n公共精神卫生\n卫生政策与管理\n社会流行病学\n皮肤病疾病负担\n公共卫生与预防医学（博士学位授权点）\n公共卫生（博士学位授权点）\n学院官网确认“公共卫生与预防医学”“公共卫生”博士学位授权点；本次未确认当年度是否以“社会医学与卫生事业管理”这一独立博士专业名称招生，须按招生目录选择。2027年名额与申请期限待当年度公告。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"school-csu-xiangya-public-health","title":"中南大学 · 湘雅公共卫生学院·社会医学与卫生事业管理学系","journal":"院系方向","year":"","kind":"院系方向","locator":"院系与项目官方资料","text":"社会行为与健康\n公共精神卫生\n卫生政策与管理\n社会流行病学\n皮肤病疾病负担\n公共卫生与预防医学（博士学位授权点）\n公共卫生（博士学位授权点）\n学院官网确认“公共卫生与预防医学”“公共卫生”博士学位授权点；本次未确认当年度是否以“社会医学与卫生事业管理”这一独立博士专业名称招生，须按招生目录选择。2027年名额与申请期限待当年度公告。","url":"https://sph.csu.edu.cn/xszx2/shyxywssyglxx/xsjj.htm","license":"本站原创整理，招生需查当年官方通知"}},{"id":"studio-library:school-sjtu-public-health","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"上海交通大学 · 公共卫生学院·社会医学与卫生事业管理系","summary":"卫生经济与政策评估\n健康的社会决定因素\n健康老龄化与养老照护\n医保支付\n安宁疗护\n公共卫生与预防医学（100400，博士）\n公共卫生（105300，博士）\n2026年医学院博士招生简章已列公共卫生与预防医学和公共卫生两个博士专业；不把系名直接当成当年度独立博士招生专业。2027年具体方向、招生名额与截止时间尚未在本次核实。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"school-sjtu-public-health","title":"上海交通大学 · 公共卫生学院·社会医学与卫生事业管理系","journal":"院系方向","year":"","kind":"院系方向","locator":"院系与项目官方资料","text":"卫生经济与政策评估\n健康的社会决定因素\n健康老龄化与养老照护\n医保支付\n安宁疗护\n公共卫生与预防医学（100400，博士）\n公共卫生（105300，博士）\n2026年医学院博士招生简章已列公共卫生与预防医学和公共卫生两个博士专业；不把系名直接当成当年度独立博士招生专业。2027年具体方向、招生名额与截止时间尚未在本次核实。","url":"https://www.shsmu.edu.cn/sph/","license":"本站原创整理，招生需查当年官方通知"}},{"id":"studio-library:school-sysu-shenzhen-biosafety","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"中山大学（深圳校区） · 生物安全学院（原公共卫生学院〔深圳〕）","summary":"传染病防控\n生物安全\n传染病系统建模\n公共卫生与医学大数据\n更名后博士专业完整名称与招生代码本次未完整核实；请以学校当年度博士专业目录为准\n2026年5月25日会议宣布原公共卫生学院（深圳）更名为生物安全学院，官网于5月30日发布公告。本条作为邻近公共卫生方向与院系沿革入口，不将其等同于广州卫生管理学系，也不据旧硕士目录推断有同名社医卫管博士招生。","url":"index.html#library","source":"data/studio-library.json","details":{"id":"school-sysu-shenzhen-biosafety","title":"中山大学（深圳校区） · 生物安全学院（原公共卫生学院〔深圳〕）","journal":"院系方向","year":"","kind":"院系方向","locator":"院系与项目官方资料","text":"传染病防控\n生物安全\n传染病系统建模\n公共卫生与医学大数据\n更名后博士专业完整名称与招生代码本次未完整核实；请以学校当年度博士专业目录为准\n2026年5月25日会议宣布原公共卫生学院（深圳）更名为生物安全学院，官网于5月30日发布公告。本条作为邻近公共卫生方向与院系沿革入口，不将其等同于广州卫生管理学系，也不据旧硕士目录推断有同名社医卫管博士招生。","url":"https://phs.sysu.edu.cn/zh-hans/node/1378","license":"本站原创整理，招生需查当年官方通知"}},{"id":"studio-library:skill-standardized-patient-skill","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"模拟与标准化病人 SP / USP","summary":"用训练过的扮演者在统一情境下测量服务过程，区分教学考核与未预先告知的质量研究。\n先明确场景、剧本、评分依据、伦理安全、机构抽样、重复访问与识别记录。\n---\nname: standardized-patient\ndescription: 模拟/标准化病人SP与未预先告知标准化病人USP：教学考核、医疗服务质量测量、剧本与评分表、训练一致性、伦理安全、抽样与机构聚类分析，配R/Stata教学代码。\n---\n\n# 模拟与标准化病人：把服务过程变成可比较的证据\n\n标准化病人（standardized patient, SP）通常由经过训练的人按剧本稳定呈现病史、行为和反应；模拟病人（simulated patient）在文献中有时与之互用。这里强调可重复呈现和测量一致性。它不是 Markov 微观模拟，也不是让大模型生成“真实患者”。\n\n## 1. 先选研究用途\n\n| 用途 | 主要问题 | 能测量什么 | 不能直接声称什么 |\n|---|---|---|---|\n| 教学、沟通训练与OSCE | 学员在统一情境下如何表现 | 问诊、沟通、决策和程序表现；按站点评分 | 考场表现等于长期真实临床表现 |\n| 告知式服务评估 | 机构在知道评估存在时怎样服务 | 统一情境的质量与流程 | 不存在观察反应 |\n| 未预先告知标准化病人 USP | 常规服务中的实际行为如何 | 适当处置、询问检查、转诊、费用及沟通 | 覆盖所有疾病、重症体征和后续健康结局 |\n| 标准化病例/临床情境问卷 ","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-standardized-patient-skill","title":"模拟与标准化病人 SP / USP","journal":"方法 Skill","year":"2026","kind":"方法 Skill","text":"用训练过的扮演者在统一情境下测量服务过程，区分教学考核与未预先告知的质量研究。\n先明确场景、剧本、评分依据、伦理安全、机构抽样、重复访问与识别记录。\n---\nname: standardized-patient\ndescription: 模拟/标准化病人SP与未预先告知标准化病人USP：教学考核、医疗服务质量测量、剧本与评分表、训练一致性、伦理安全、抽样与机构聚类分析，配R/Stata教学代码。\n---\n\n# 模拟与标准化病人：把服务过程变成可比较的证据\n\n标准化病人（standardized patient, SP）通常由经过训练的人按剧本稳定呈现病史、行为和反应；模拟病人（simulated patient）在文献中有时与之互用。这里强调可重复呈现和测量一致性。它不是 Markov 微观模拟，也不是让大模型生成“真实患者”。\n\n## 1. 先选研究用途\n\n| 用途 | 主要问题 | 能测量什么 | 不能直接声称什么 |\n|---|---|---|---|\n| 教学、沟通训练与OSCE | 学员在统一情境下如何表现 | 问诊、沟通、决策和程序表现；按站点评分 | 考场表现等于长期真实临床表现 |\n| 告知式服务评估 | 机构在知道评估存在时怎样服务 | 统一情境的质量与流程 | 不存在观察反应 |\n| 未预先告知标准化病人 USP | 常规服务中的实际行为如何 | 适当处置、询问检查、转诊、费用及沟通 | 覆盖所有疾病、重症体征和后续健康结局 |\n| 标准化病例/临床情境问卷 | 医务人员“知道该怎么做” | 知识或陈述决策 | 与USP测得的实际行为完全相同 |\n\nASPE 标准主要面向人类模拟教学与评估；USP 质量研究还需研究方案、抽样、识别与安全管理。先确定对象、场景、主要结局、比较与时间窗，再选是否适合 SP。[S1–S3]\n\n## 2. 剧本与评分表怎样制作\n\n选择可安全、可信地呈现且与研究问题相关的场景。明确开场陈述、只有被问到才提供的信息、允许的标准回答、情绪与行为、不可模拟的体征，以及何时停止访问。临床处置评分应由相应专业人员依据适用指南制定，注明指南日期与评分规则；本站不提供自行施行检查或治疗的脚本。\n\n评分表逐项定义“应询问/应检查/应处置/不适当操作”、信息来源、缺失、不适用和分母。主要结局应预先规定，例如符合预定处置标准的访问比例；不能看完结果才挑最显著条目。复杂综合分数需说明权重和测量依据，不能自动视为已验证量表。\n\n先做小规模可行性试演：对同一剧本重复训练，检查扮演者间与扮演者内一致性、评分者一致性及回忆遗漏。二分类可考虑一致率与κ，连续分数按设计考虑ICC；同时查看分布，因为高一致率与低κ可能并存。\n\n## 3. 抽样、分配与访视安排\n\n1. 写清机构抽样框、纳排、地区与服务类型。便利样本不代表全国质量。\n2. 若评价干预，明确随机化或政策分配单位；机构层分配不能把每次访问当作独立随机单位。\n3. 记录医生、机构、扮演者、剧本、访问时间与重复访问关系。角色和案例尽量在比较组平衡；同一医生重复访问及跨机构SP可形成多层或交叉结构。\n4. 事先做功效规划：主要结局、预期差异、机构数、每机构访问数、ICC、病例构成与不可完成比例。增加同一机构访问数不能无限替代增加机构数。\n5. 在许可范围内尽快填写结构化访问记录；若使用音频等材料，必须与批准方案、访问授权和保存期限一致。\n\n## 4. 伦理与安全是设计条件\n\n未预先告知不等于不需伦理审查。医务人员同意的取得方式或豁免、事后说明、隐私与声誉保护、对正常服务的干扰，须由相应伦理审查与机构安排明确。授权或豁免只能由适用程序认定，不能由本工具自动决定。[S1–S3]\n\n预设不得接受的侵入性检查、真实用药与危险情境及退出流程；出现超出模拟范围的情况应中止并按批准的安全流程处理。保护SP的身体与情绪安全，安排训练、支持和复盘。公开结果以适当聚合形式呈现，不发布可识别的个人访问记录。\n\n## 5. 数据字典与质量核查\n\n[下载SP字段模板](../data/standardized-patient-data-dictionary.csv)。模板是空白字段说明，不含患者或医务人员记录。\n\n核心字段：匿名机构/医生/访问/扮演者/案例ID，组别与实际分配，访问时间，各评分项目及分母，主要处置结局，费用，是否被识别，剧本偏离，不完整访问及缺失原因。\n\n保留访视失败和识别记录。识别可能与机构、剧本或干预有关；仅删除“被识别”样本可能引入选择偏倚。主分析与敏感性分析的对象、分母和排除标准均提前规定，并报告各阶段数量。[S2]\n\n## 6. 分析如何排布\n\n- 先描述抽样、完成率、案例分布、识别比例和评分缺失。\n- 二元结局报告比例与区间，并明确风险差、风险比或OR；不把OR直接解释成风险比。\n- 机构随机研究按分配单位处理相关性。脚本/演员固定或随机、平衡程度与目标总体决定采用机构汇总、多层或交叉模型。\n- 横断面USP比较主要支持描述与关联；只有满足随机或准实验识别条件才能作相应因果解释。\n- 观察真实患者资料与SP资料回答不同问题：病例标准化有利于比较，却不代表实际患者疾病谱与长期结局。[S2–S3]\n\n下方例子为36个随机分组机构，每机构3个案例×4位扮演者，共432次模拟访问。案例和扮演者在机构间完全平衡，采用等机构权重的组间比较；推断限于这组案例和扮演者。R脚本已在R 4.6.1运行；Stata为未执行教学模板。模拟结果不保证显著，也不是任何真实政策效果。\n\n## 7. 与实施科学怎样联用\n\n例如评估基层指南落实：同一临床服务对应两种实施支持策略，机构分组，USP评估处置过程质量，另用实施数据衡量采纳、忠实度、成本与维持。SP评分属于服务过程质量，不能替代全部实施结局或患者健康结局。明确盲法、识别风险、策略污染及机构聚类，再判断是否适合 hybrid 设计。[实施科学路线](implementation-science-skill.md)\n\n## 8. 最终交付清单\n\n研究问题与估计目标、剧本版本、评分依据、抽样与功效方案、伦理与安全文件、训练及一致性记录、字段字典、访问流程图、分析计划、可复现代码和限制说明。\n\n可复制提问：**“我想用标准化病人比较基层服务质量。请帮我定义主要结局、剧本、抽样、机构相关性、识别记录和R分析方案。”**\n\n## 核对来源\n\n- S1. Lewis KL 等，2017，ASPE Standards of Best Practice。[官方全文](https://www.aspeducators.org/assets/docs/ASPE%20SOBP%202.7.20.pdf) · [DOI](https://doi.org/10.1186/s41077-017-0043-4)。使用范围：训练、安全、情境与质量保证。\n- S2. King JJC 等，2019，How to do (or not to do)… using the standardized patient method to measure clinical quality of care in LMIC health facilities。[PubMed](https://pubmed.ncbi.nlm.nih.gov/31424494/) · [DOI](https://doi.org/10.1093/heapol/czz078)。使用范围：USP方法与测量边界。\n- S3. Assessing the quality of primary healthcare in seven Chinese provinces with unannounced standardised patients: protocol of a cross-sectional survey，2019。[BMJ Open](https://bmjopen.bmj.com/content/9/2/e023997)。这是研究方案，不能当作研究结果。\n\n题名、DOI、期刊与作者核对日期：2026-10-24。[来源登记](../data/sp-implementation-sources.json)。本站为原创方法导读，不复制论文全文。\n\n<!-- BEGIN TEACHING CODE -->\n\n## 教学代码：可以单独下载，也可展开复制\n\n### 机构层级分析 · R\n\n[下载脚本](scripts/standardized_patient_demo.R)\n\n[教学代码见手册]\n\n### 机构层级分析 · STATA\n\n[下载脚本](scripts/standardized_patient_demo.do)\n\n[教学代码见手册]\n","url":"guides/standardized-patient-skill.html","locator":"本站原创方法手册","license":"本站原创说明与代码；参考论文各自保留权利","verified":"2026-10-24；来源登记另列；R模拟已执行"}},{"id":"studio-library:skill-implementation-science-skill","collection":"studio-library","collection_title":"工作台知识与阅读材料","kind":"文献","title":"实施科学：从框架到研究设计","summary":"区分服务干预与实施策略，把障碍、策略、实施结局、健康结局和持续使用连接到具体设计。\n先明确服务证据、策略执行者、主要结局、层级、分母与时间窗；框架不能代替识别设计。\n---\nname: implementation-science\ndescription: 实施科学完整路线：临床/服务干预与实施策略分离，CFIR、RE-AIM、Proctor实施结局、ERIC、hybrid 1/2/3、混合方法、分母与层级、R/Stata教学分析和StaRI报告。\n---\n\n# 实施科学：让有证据的服务在真实场景中被采用和持续使用\n\n先区分**要落实的服务/干预**（innovation/intervention）与**帮助它落实的实施策略**（strategy）。例如，“规范的慢病随访服务”与“为实施该服务提供培训、反馈和促进支持”是两条不同的线。研究应同时明确谁采用、如何交付、为何受阻、产生何种结果。[I1–I4]\n\n## 1. 把研究问题写成可执行句子\n\n在【场景与机构】中，为【人群】落实【已有证据的服务】时，针对【已识别障碍】，由【执行者】实施【策略及剂量】，相对于【比较条件】，在【时间窗】内改善【主要实施结局】，并观察【服务或健康结局】。\n\n先评估服务本身的证据与可实施性，不把“有效性未知”隐藏成单纯推广问题。研究不是将多个框架名词拼接；框架服务于具体问题、构念和数据。\n\n## 2. 框架按任务分工\n\n| 任务 | 候选工具 | 用法与边界 |\n|---|---|---|\n| 解释障碍","url":"index.html#library","source":"data/studio-library.json","details":{"id":"skill-implementation-science-skill","title":"实施科学：从框架到研究设计","journal":"方法 Skill","year":"2026","kind":"方法 Skill","text":"区分服务干预与实施策略，把障碍、策略、实施结局、健康结局和持续使用连接到具体设计。\n先明确服务证据、策略执行者、主要结局、层级、分母与时间窗；框架不能代替识别设计。\n---\nname: implementation-science\ndescription: 实施科学完整路线：临床/服务干预与实施策略分离，CFIR、RE-AIM、Proctor实施结局、ERIC、hybrid 1/2/3、混合方法、分母与层级、R/Stata教学分析和StaRI报告。\n---\n\n# 实施科学：让有证据的服务在真实场景中被采用和持续使用\n\n先区分**要落实的服务/干预**（innovation/intervention）与**帮助它落实的实施策略**（strategy）。例如，“规范的慢病随访服务”与“为实施该服务提供培训、反馈和促进支持”是两条不同的线。研究应同时明确谁采用、如何交付、为何受阻、产生何种结果。[I1–I4]\n\n## 1. 把研究问题写成可执行句子\n\n在【场景与机构】中，为【人群】落实【已有证据的服务】时，针对【已识别障碍】，由【执行者】实施【策略及剂量】，相对于【比较条件】，在【时间窗】内改善【主要实施结局】，并观察【服务或健康结局】。\n\n先评估服务本身的证据与可实施性，不把“有效性未知”隐藏成单纯推广问题。研究不是将多个框架名词拼接；框架服务于具体问题、构念和数据。\n\n## 2. 框架按任务分工\n\n| 任务 | 候选工具 | 用法与边界 |\n|---|---|---|\n| 解释障碍与促进因素 | 更新版CFIR（2022）；2025使用指南 | 按问题选构念，建立访谈/观察与分析对应；不是全部条目强制打分 |\n| 规划和评价真实世界覆盖 | RE-AIM | 分开触达、效果、采纳、实施、维持；逐维指定层级、分母与时间 |\n| 定义实施成效 | Proctor实施结局 | 区分实施、服务与健康结局；不把患者满意度当所有实施成功 |\n| 具体说明实施支持措施 | ERIC策略术语 | 明确谁对谁做什么、何时、多频繁及预期机制；不是看到障碍就机械配策略 |\n| 组织结果报告 | StaRI | 服务干预与实施策略分两条线报告；另按具体研究设计核对报告规范 |\n\nCFIR常用于解释决定因素，RE-AIM常用于规划与评价；两者可互补，但不是同一套量表。构念名称本身不会产生因果识别。[I1–I3、I6–I8]\n\n## 3. 结局、分母与时间窗\n\nProctor等区分可接受性、采纳、适切性、可行性、忠实度、实施成本、渗透/整合和可持续性。分别定义测量对象与操作化指标；临床效果和服务系统结果单独记录。[I4]\n\n| 指标例子 | 分子 | 分母 | 注意 |\n|---|---|---|---|\n| 触达 | 实际接受服务的符合条件者 | 目标范围内全部符合条件者 | 说明招募、拒绝和排除；还要看代表性 |\n| 机构采纳 | 开始采用服务的机构 | 纳入目标的符合条件机构 | 机构层和工作人员层不要混算 |\n| 忠实度 | 按规定完成核心环节的服务次数 | 应执行这些环节的服务次数 | 区分适应性调整与偏离；不适用不是未完成 |\n| 维持 | 规定随访时点仍运行的机构 | 按方案定义的初始或已采纳机构 | 明确时点和分母，不能临时替换 |\n| 单位实施成本 | 明确视角下的实施资源成本 | 对应触达者/采纳机构数 | 分开服务成本与实施支持成本，注明币种和价格年 |\n\n分母为0时显示缺失并说明原因，不能直接记为0%。机构平均比例与“总分子/总分母”是不同估计目标；两者不能混报。可接受性等潜变量如使用量表，需核对授权、译本、测量性质及本场景适用性，不能把自编问题标成已验证量表。\n\n## 4. 选择设计与hybrid类型\n\n| 类型 | 主要关注 | 同时关注 |\n|---|---|---|\n| Hybrid 1 | 检验服务干预有效性 | 探索实施条件与障碍 |\n| Hybrid 2 | 服务干预与实施策略的双重研究目标 | 两类目标的样本量、结局和比较均须说明 |\n| Hybrid 3 | 检验实施策略对实施结局的作用 | 观察服务/健康效果 |\n\n类型描述研究目标的侧重，不能替代具体的随机、准实验或观察设计。根据已有服务证据、场景差异、污染和可行性选择个体/整群随机、分阶段推行或适当准实验；阶梯楔形设计仍要处理日历时间和机构相关性，不能等同简单前后比较。[I5]\n\n样本量以主要目标和分配层级计算，考虑ICC、机构大小差异、双重主要结局与缺失；不能把个体数当成独立机构数。\n\n## 5. 实施策略与机制表\n\n一行一个策略：执行者 → 具体行动 → 对象 → 频率/时长 → 对应障碍 → 预期机制 → 近端实施结局 → 远端服务/健康结局。区分计划剂量与实际执行，记录适应性修改的时间、理由和决定者。[I6]\n\n访谈可按选定CFIR构念设计，同时保留开放编码与反例。建立联合展示：构念、定性证据、量化指标、相符/不符、替代解释。不能用访谈提及次数直接估计人群比例，也不能仅凭显著路径判定机制成立。\n\n## 6. 数据与分析\n\n[下载实施研究字段模板](../data/implementation-science-data-dictionary.csv)。至少包括机构/工作人员/服务对象层级ID，策略分配，服务定义，实施阶段，时间，分母，采纳与执行记录，适应性修改，成本及相关群体特征。公开模板不含任何个体记录。\n\n下方教学例子把40个机构随机分配至两种实施策略，对同一种临床服务计算采纳、触达、执行忠实度和单位实施成本。主示例比较等机构权重的触达率；重复时间点需另建纵向模型。\n\n忠实度在实际接受服务者中计算，条件于一个分组后的变量；组间忠实度差不能自动解释为随机策略对所有符合条件者的因果效应。R脚本已运行并检查分母与缺失逻辑；Stata未执行。模拟数据只演示计算，不提供效果证据。\n\n## 7. 公平与标准化病人联用\n\n按预先定义的群体查看谁未被触达、谁承担更大负担、机构是否代表目标场景。平均改善不排除差距扩大。[公平性补充](implementation-equity-skill.md)\n\n[标准化病人](standardized-patient-skill.md)可提供统一情境的服务过程评价，帮助比较策略实施前后或不同组的实际处置；仍需单独收集实施策略执行、采纳和成本，不能把USP处置合格率直接改名为全部实施成功。\n\n## 8. 按StaRI交付\n\n分别报告服务干预、实施策略及其依据；交代情境、适应性修改、设计、比较、主要分母、过程、实施/健康结局和局限。交付研究问题表、框架—构念映射、逻辑模型、策略操作表、测量字典、混合方法联合展示、分析计划与报告核对表。[I7]\n\n可复制提问：**“我想提高基层指南采纳。请区分服务干预与实施策略，用CFIR选择障碍、RE-AIM定义分母，并比较hybrid 1/2/3的适用条件。”**\n\n## 核对来源\n\n- I1. Damschroder等，2022，更新版CFIR。[原文](https://link.springer.com/article/10.1186/s13012-022-01245-0)。\n- I2. CFIR User Guide，2025，五步使用指南。[原文](https://link.springer.com/article/10.1186/s13012-025-01450-7)。\n- I3. RE-AIM框架团队。[官方五维说明](https://re-aim.org/learn/what-is-re-aim/)。\n- I4. Proctor等，2011，实施结局概念区分。[原文](https://doi.org/10.1007/s10488-010-0319-7)。\n- I5. Curran等，2012，Effectiveness-implementation Hybrid Designs。[原文](https://pmc.ncbi.nlm.nih.gov/articles/PMC3731143/)。\n- I6. Powell等，2015，ERIC策略术语整理。[原文](https://link.springer.com/article/10.1186/s13012-015-0209-1)。\n- I7. Pinnock等，2017，StaRI。[EQUATOR登记与清单](https://www.equator-network.org/reporting-guidelines/stari-statement/)。\n- I8. [CFIR官方资源](https://cfirguide.org/)。\n\n核对日期：2026-10-24。[完整题名、DOI、作者及核验范围](../data/sp-implementation-sources.json)。本文为本站原创教学整理，未复制论文全文。\n\n<!-- BEGIN TEACHING CODE -->\n\n## 教学代码：可以单独下载，也可展开复制\n\n### 机构层级分析 · R\n\n[下载脚本](scripts/implementation_science_demo.R)\n\n[教学代码见手册]\n\n### 机构层级分析 · STATA\n\n[下载脚本](scripts/implementation_science_demo.do)\n\n[教学代码见手册]\n","url":"guides/implementation-science-skill.html","locator":"本站原创方法手册","license":"本站原创说明与代码；参考论文各自保留权利","verified":"2026-10-24；来源登记另列；R模拟已执行"}},{"id":"papers:GLSJ2026007007","collection":"papers","collection_title":"CSSCI当前专题筛选版","kind":"文献","title":"药品集采改革、医疗负担与医疗公平——来自医院处方的微观证据","summary":"国家组织药品集中带量采购是治理药价虚高、深化我国医药服务供给侧改革的重要切入口。本文构建了“价格冲击—行为反应—医疗费用与医疗公平结果”的统一分析框架，利用患者—医生层面的微观处方数据，基于逐步推开的17个批次的集采药品，系统考察了集采改革对药品价格和患者医疗负担的综合影响。研究发现：第一，改革的降价效应明显，药品平均降幅超过70%，且对非集采药产生了显著的溢出效应；第二，改革降低了门诊患者的医药负担，且低收入群体、农村居民等群体受益更大，起到了促进医疗公平的预期效果；第三，改革容易出现“跷跷板效应”，医院和医生的策略性反应是将药品收入的减少转嫁到其他收费项目，部分抵消了集采改革的减负效应。本文的结论证实了药品集采改革在缓解“看病贵”问题方面的积极效应，也揭示了改革对医疗服务供需方行为的潜在影响及挑战，为完善药品集采制度提供了经验证据和重要参考。","url":"papers.html","source":"data/papers.json","details":{"id":"GLSJ2026007007","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"7","authors":["诸宇灵","顾晓敏","周黎安","范子英"],"affils":["上海财经大学公共管理学院","上海财经大学中国式现代化研究院","上海对外经贸大学国际经贸学院","北京大学光华管理学院","上海财经大学财税投资学院"],"title":"药品集采改革、医疗负担与医疗公平——来自医院处方的微观证据","titleEn":"Volume-Based Drug Procurement Reform, Medical Burden and Health Equity: Evidence from Hospital Prescriptions","keywords":["药品集中带量采购","医疗负担","溢出效应","医疗公平"],"keywordsEn":[],"abstract":"国家组织药品集中带量采购是治理药价虚高、深化我国医药服务供给侧改革的重要切入口。本文构建了“价格冲击—行为反应—医疗费用与医疗公平结果”的统一分析框架，利用患者—医生层面的微观处方数据，基于逐步推开的17个批次的集采药品，系统考察了集采改革对药品价格和患者医疗负担的综合影响。研究发现：第一，改革的降价效应明显，药品平均降幅超过70%，且对非集采药产生了显著的溢出效应；第二，改革降低了门诊患者的医药负担，且低收入群体、农村居民等群体受益更大，起到了促进医疗公平的预期效果；第三，改革容易出现“跷跷板效应”，医院和医生的策略性反应是将药品收入的减少转嫁到其他收费项目，部分抵消了集采改革的减负效应。本文的结论证实了药品集采改革在缓解“看病贵”问题方面的积极效应，也揭示了改革对医疗服务供需方行为的潜在影响及挑战，为完善药品集采制度提供了经验证据和重要参考。","abstractEn":"","pages":"108-127","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=NmZ1SlZZdjhQWnBPRWF3MmNhbW5uQkRvemdYOGZnbnRGSlJHU01nTDkxMXZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"诸宇灵, 顾晓敏, 周黎安, 范子英. 药品集采改革、医疗负担与医疗公平——来自医院处方的微观证据[J]. 管理世界, 2026(7): 108-127.","lenZh":378,"lenEn":0}},{"id":"papers:GLSJ2026007008","collection":"papers","collection_title":"CSSCI当前专题筛选版","kind":"文献","title":"分级诊疗制度下的医疗控费机制——基于大样本病案数据的研究证据","summary":"近年来，我国持续推进分级诊疗制度建设，旨在通过优化医疗资源配置与规范医疗服务行为，遏制因资源错配和过度医疗导致的费用不合理增长。然而，由于医疗服务供需双方存在信息不对称，且医疗机构在资源扩张与医保支付约束下拥有自主调整患者结构的空间，分级诊疗制度的有效运行仍面临现实挑战。为此，本文在理论上构建了医疗卫生机构最优选择模型，并基于某副省级城市2020～2023年1687.7万条病案首页数据，提出了结合医疗大数据与机器学习的诊疗难度识别方法，用以验证基层医疗机构的控费功能，同时系统分析三级医院入院标准的差异及其动态调整机制。研究结果显示，三级医院入院标准显著受当日住院资源占用量影响：资源越宽松，入院标准越低。在此机制下，部分三级医院可能出于利益驱动主动降低入院门槛，长期收治低诊疗难度患者，从而在一定程度上削弱分级诊疗制度的实施效果。基于上述发现，本文提出，可依托病案大数据构建以基层病组为核心的差异化医保支付方式，对三级医院入院标准下调行为形成柔性约束。","url":"papers.html","source":"data/papers.json","details":{"id":"GLSJ2026007008","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"7","authors":["臧文斌","吴舒祥","王一如","陈晨"],"affils":["西南财经大学公共管理学院","西南财经大学金融学院","山西财经大学财政与公共经济学院"],"title":"分级诊疗制度下的医疗控费机制——基于大样本病案数据的研究证据","titleEn":"Cost Containment in China's Hierarchical Medical System: Evidence from Large-scale Inpatient Medical Records","keywords":["分级诊疗","医疗控费","病案数据"],"keywordsEn":[],"abstract":"近年来，我国持续推进分级诊疗制度建设，旨在通过优化医疗资源配置与规范医疗服务行为，遏制因资源错配和过度医疗导致的费用不合理增长。然而，由于医疗服务供需双方存在信息不对称，且医疗机构在资源扩张与医保支付约束下拥有自主调整患者结构的空间，分级诊疗制度的有效运行仍面临现实挑战。为此，本文在理论上构建了医疗卫生机构最优选择模型，并基于某副省级城市2020～2023年1687.7万条病案首页数据，提出了结合医疗大数据与机器学习的诊疗难度识别方法，用以验证基层医疗机构的控费功能，同时系统分析三级医院入院标准的差异及其动态调整机制。研究结果显示，三级医院入院标准显著受当日住院资源占用量影响：资源越宽松，入院标准越低。在此机制下，部分三级医院可能出于利益驱动主动降低入院门槛，长期收治低诊疗难度患者，从而在一定程度上削弱分级诊疗制度的实施效果。基于上述发现，本文提出，可依托病案大数据构建以基层病组为核心的差异化医保支付方式，对三级医院入院标准下调行为形成柔性约束。","abstractEn":"","pages":"128-152","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=NmZ1SlZZdjhQWnBPRWF3MmNhbW5uTEdrV3ltdHRVUWJnNEpnTWZiWFJTSnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"臧文斌, 吴舒祥, 王一如, 陈晨. 分级诊疗制度下的医疗控费机制——基于大样本病案数据的研究证据[J]. 管理世界, 2026(7): 128-152.","lenZh":429,"lenEn":0}},{"id":"papers:SHKX2026008005","collection":"papers","collection_title":"CSSCI当前专题筛选版","kind":"文献","title":"工作稳定性如何降低健康不平等？——基于“中国社会状况综合调查（2023）”的多重中介分析","summary":"促进高质量充分就业，是新时代新征程就业工作的新定位、新使命。本文将工作稳定性视为高质量充分就业的重要维度，依据健康不平等理论整合了工作稳定性影响健康的多重解释路径。研究采用“中国社会状况综合调查（2023）”中非农业就业劳动力样本的数据，从多个维度构建了工作稳定性指数，分析发现：工作越稳定者，其自评健康水平越高。主要路径表现为：稳定就业增加了劳动者的收入，同时降低了劳动者的日常生活压力和健康行为约束等，从而提升了他们的自评健康水平，其中日常生活压力的中介解释力最强。进一步分析发现，工作稳定性对不同性别、户口、教育程度和阶层地位劳动者自评健康的影响是广泛的。系统提升劳动者就业稳定性，推动实现更高质量、更充分的就业发展目标，有助于改善劳动者身心健康状况，并进一步增强广大劳动者的获得感、幸福感、安全感。","url":"papers.html","source":"data/papers.json","details":{"id":"SHKX2026008005","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"8","authors":["王甫勤"],"affils":["同济大学政治与国际关系学院","同济大学中国特色社会主义理论研究中心"],"title":"工作稳定性如何降低健康不平等？——基于“中国社会状况综合调查（2023）”的多重中介分析","titleEn":"How Does Job Stability Reduce Health Inequality? A Multiple Mediation Analysis Based on Data from the Chinese Social Survey (2023)","keywords":["高质量充分就业","工作稳定性","健康不平等","不稳定就业"],"keywordsEn":[],"abstract":"促进高质量充分就业，是新时代新征程就业工作的新定位、新使命。本文将工作稳定性视为高质量充分就业的重要维度，依据健康不平等理论整合了工作稳定性影响健康的多重解释路径。研究采用“中国社会状况综合调查（2023）”中非农业就业劳动力样本的数据，从多个维度构建了工作稳定性指数，分析发现：工作越稳定者，其自评健康水平越高。主要路径表现为：稳定就业增加了劳动者的收入，同时降低了劳动者的日常生活压力和健康行为约束等，从而提升了他们的自评健康水平，其中日常生活压力的中介解释力最强。进一步分析发现，工作稳定性对不同性别、户口、教育程度和阶层地位劳动者自评健康的影响是广泛的。系统提升劳动者就业稳定性，推动实现更高质量、更充分的就业发展目标，有助于改善劳动者身心健康状况，并进一步增强广大劳动者的获得感、幸福感、安全感。","abstractEn":"","pages":"51-64","doi":"10.13644/j.cnki.cn31-1112.2026.08.006","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=Z0kxREJGL3hIbUExOXRNQmxNTnB4Q2VHYzllelYwbVJQQitjTlpsY3dycHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"王甫勤. 工作稳定性如何降低健康不平等？——基于“中国社会状况综合调查（2023）”的多重中介分析[J]. 社会科学, 2026(8): 51-64.","lenZh":353,"lenEn":0}},{"id":"papers:SHKX2026007005","collection":"papers","collection_title":"CSSCI当前专题筛选版","kind":"文献","title":"生命的成全：面向“第四年龄”的养老服务及保障体系建设","summary":"当前我国养老服务存在结构性供需错配，即按法定年龄实施普惠供给，市场资源向健康老年群体集聚，中重度失能失智“第四年龄”群体刚性照护保障不足。这不仅使人民满足感、安全感未达到政策预期，也制约了这项民生保障工程的经济发展效能。如何破题，成为“十五五”时期扎实推进共同富裕的重要议题。为此，通过反思个体本位生命观及养老服务制度模式，立足中国家文化、生命文化中的“关系性”“群体性”生命观与“代际生命一体性”，本文构建“生命—制度”分析框架，提出以“生命的成全”为核心价值，以家为本位，聚焦“第四年龄”群体服务精准供给与综合赋能效应，破除生命历程、代际关系、经济社会、技术人文的二元对立，构建中国式养老服务体系，为推动中国式现代化与共同富裕目标实现提供理论支撑与实践方案。","url":"papers.html","source":"data/papers.json","details":{"id":"SHKX2026007005","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"7","authors":["贾玉娇"],"affils":["吉林大学哲学社会学院"],"title":"生命的成全：面向“第四年龄”的养老服务及保障体系建设","titleEn":"The Fulfi llment of Life: Construction and Development Empowerment of the Elderly Care Service System for the \"Fourth Age\"","keywords":["生命的成全","中国式积极老龄化","关系性生命观","养老服务体系","第四年龄"],"keywordsEn":[],"abstract":"当前我国养老服务存在结构性供需错配，即按法定年龄实施普惠供给，市场资源向健康老年群体集聚，中重度失能失智“第四年龄”群体刚性照护保障不足。这不仅使人民满足感、安全感未达到政策预期，也制约了这项民生保障工程的经济发展效能。如何破题，成为“十五五”时期扎实推进共同富裕的重要议题。为此，通过反思个体本位生命观及养老服务制度模式，立足中国家文化、生命文化中的“关系性”“群体性”生命观与“代际生命一体性”，本文构建“生命—制度”分析框架，提出以“生命的成全”为核心价值，以家为本位，聚焦“第四年龄”群体服务精准供给与综合赋能效应，破除生命历程、代际关系、经济社会、技术人文的二元对立，构建中国式养老服务体系，为推动中国式现代化与共同富裕目标实现提供理论支撑与实践方案。","abstractEn":"","pages":"54-64","doi":"10.13644/j.cnki.cn31-1112.2026.07.001","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=d29DdDJRejkrTjlLT3pnRFUvSDlCeWVHYzllelYwbVJQQitjTlpsY3dycHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"贾玉娇. 生命的成全：面向“第四年龄”的养老服务及保障体系建设[J]. 社会科学, 2026(7): 54-64.","lenZh":331,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026008006","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"数智时代历史学的新进展","summary":"编者按：当前，以人工智能为代表的新兴科技快速发展，提高了人类认知能力，拓展了认知范围。尤其值得注意的是，随着从“数字史学”到“数智史学”的演进，技术已不再是仅仅作为辅助工具，而是推动研究范式向“人机协同”方向发展，这一现象正深刻重塑着历史学的方法论根基与学科理念。有鉴于此，本刊约请三位学者对人工智能在史学研究领域的勃兴之势进行研讨，以期探寻历史学创新发展的可能路径。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026008006","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"8","authors":["高晞","梁晨","张萍"],"affils":[],"title":"数智时代历史学的新进展","titleEn":"New Advances in Historiography in the Digital-Intelligence Era","keywords":[],"keywordsEn":[],"abstract":"编者按：当前，以人工智能为代表的新兴科技快速发展，提高了人类认知能力，拓展了认知范围。尤其值得注意的是，随着从“数字史学”到“数智史学”的演进，技术已不再是仅仅作为辅助工具，而是推动研究范式向“人机协同”方向发展，这一现象正深刻重塑着历史学的方法论根基与学科理念。有鉴于此，本刊约请三位学者对人工智能在史学研究领域的勃兴之势进行研讨，以期探寻历史学创新发展的可能路径。","abstractEn":"Editor's Note: At present, emerging technologies represented by artificial intelligence are developing rapidly, enhancing human cognitive capacities and expanding the scope of cognition. Particularly noteworthy is that, with the evolution from \"digital historiography\" to \"digital-intelligent historiography,\" technology is no longer merely an auxiliary tool but is driving the research paradigm toward \"human-machine collaboration\"—a phenomenon that is profoundly reshaping the methodological foundations and disciplinary concepts of history. In view of this, the journal has invited three scholars to discuss the burgeoning trend of artificial intelligence in historical research, with a view to exploring possible paths for the innovative development of history.","pages":"104","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=VjJSRHlrQ2dSQitrVS9TN3NDUjY2eFdqVEpOWkFnYmFCMDVodk84U2FlSWQxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"高晞, 梁晨, 张萍. 数智时代历史学的新进展[J]. 中国社会科学, 2026(8): 104.","lenZh":184,"lenEn":765}},{"id":"archive/cssci-100:ZGSHKX2026008007","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"进入人工智能实验室的历史学者","summary":"学术界关于人工智能与史学关系的讨论普遍忽略了工程师作为第三方在知识生产中的结构性角色。将这一缺位纳入人工智能史学的认识论框架，是探讨“人工智能史学思维”的核心议题之一。历史学者参与史学专题智能体与HistBench基准评测框架建构的实验表明，史学知识须经过与工程师科学逻辑的持续碰撞与协商，在工程化转译中被拆解、重组与形式化，方能完成模型建构过程。由此可见，人工智能史学思维的技术话语权实际掌握在隐形的工程师手中。人工智能史学思维的形成有赖于历史学者深度介入模型设计、语料遴选与评测机制。作为一种尚在生成的新型认知形态，人工智能史学思维在与史学传统的持续对话中，有可能反作用于人类认知方式，为史学范式的深层变革注入难以预估的动力。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026008007","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"8","authors":["高晞"],"affils":["复旦大学历史学系"],"title":"进入人工智能实验室的历史学者","titleEn":"The Historians That Enter the AI Laboratory: Practice and Methodological Reflections","keywords":["人工智能史学","人工智能史学实验","人工智能史学思维","HistBench","HistAgent"],"keywordsEn":["Artificial Intelligence Historiography","Artificial Intelligence Historiography Experiment","Artificial Intelligence Historiographical Thinking","HistBench","HistAgent"],"abstract":"学术界关于人工智能与史学关系的讨论普遍忽略了工程师作为第三方在知识生产中的结构性角色。将这一缺位纳入人工智能史学的认识论框架，是探讨“人工智能史学思维”的核心议题之一。历史学者参与史学专题智能体与HistBench基准评测框架建构的实验表明，史学知识须经过与工程师科学逻辑的持续碰撞与协商，在工程化转译中被拆解、重组与形式化，方能完成模型建构过程。由此可见，人工智能史学思维的技术话语权实际掌握在隐形的工程师手中。人工智能史学思维的形成有赖于历史学者深度介入模型设计、语料遴选与评测机制。作为一种尚在生成的新型认知形态，人工智能史学思维在与史学传统的持续对话中，有可能反作用于人类认知方式，为史学范式的深层变革注入难以预估的动力。","abstractEn":"","pages":"104-120","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=VjJSRHlrQ2dSQitrVS9TN3NDUjY2NlA0OHZKQlF6ekI0QjZ3ZFRuZGtzQWQxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"高晞. 进入人工智能实验室的历史学者[J]. 中国社会科学, 2026(8): 104-120.","lenZh":315,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026008008","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"人工智能赋能史学研究的实践路径与边界反思","summary":"从数字工具到数智助手，技术的迅猛发展正在深刻重塑史学研究的方法体系，探寻适合的技术赋能路径日益紧迫。基于包含史料链接与重构、文献解读与分析、叙述建构与阐释的多维度、多层次数字史学研究实践表明：一方面，人工智能技术实现了从“看得见”史料到“读得懂”史料的关键跨越，其不仅能够作为高效的工具赋能史学研究各环节，也可以像学术助手一般形成自动化研究工作流，深度参与史学建构；另一方面，数智技术的引入并不意味着认识论层面上史学研究范式的根本转变，数智技术不应成为历史学者的替代者。如何善用数智技术、构建数智史学的研究规范，是历史学者的当务之急。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026008008","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"8","authors":["梁晨"],"affils":["南京大学历史学院"],"title":"人工智能赋能史学研究的实践路径与边界反思","titleEn":"Practical Pathways and Reflections on the Boundaries of AI-Empowered Historical Research","keywords":["数字史学","数智史学","人工智能","人机协同"],"keywordsEn":["Digital History","Digital-Intelligent Historiography","Artificial Intelligence","Human-Machine Collaboration"],"abstract":"从数字工具到数智助手，技术的迅猛发展正在深刻重塑史学研究的方法体系，探寻适合的技术赋能路径日益紧迫。基于包含史料链接与重构、文献解读与分析、叙述建构与阐释的多维度、多层次数字史学研究实践表明：一方面，人工智能技术实现了从“看得见”史料到“读得懂”史料的关键跨越，其不仅能够作为高效的工具赋能史学研究各环节，也可以像学术助手一般形成自动化研究工作流，深度参与史学建构；另一方面，数智技术的引入并不意味着认识论层面上史学研究范式的根本转变，数智技术不应成为历史学者的替代者。如何善用数智技术、构建数智史学的研究规范，是历史学者的当务之急。","abstractEn":"","pages":"121-137","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=VjJSRHlrQ2dSQitrVS9TN3NDUjY2LzlVM295NWNKQXREUVY3OXNyZ3g2NGQxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"梁晨. 人工智能赋能史学研究的实践路径与边界反思[J]. 中国社会科学, 2026(8): 121-137.","lenZh":267,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026008009","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"从数据库到智能体：人工智能参与史学研究新形态","summary":"大语言模型与生成式人工智能被引入史学研究领域后，引发了史学研究的重大变化。一方面，在数字史学研究中，解决了人工搭建数据库的难题，简化了软件操作流程，一定程度上缓解了数字史学发展的困境。另一方面，催生出历史智能体的设计理念，以知识库代替数据库，实现全文本解析；借助大语言模型与生成式人工智能的自主生成能力，最大限度地模拟历史学者的工作流程与思维方式，成为“数智时代”产出的新型知识系统。历史智能体设计在一定程度上实现了“人工智能+”“人机协作”，为史学研究开辟了新的认知路径。但是，该研究模式并不能取代传统史学与数字史学研究，史学研究范式的多样性才是推动史学研究繁荣发展的重要动力。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026008009","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"8","authors":["张萍"],"affils":["首都师范大学历史学院"],"title":"从数据库到智能体：人工智能参与史学研究新形态","titleEn":"From Database to AI Agent: A New Form of AI Participation in Historical Research","keywords":["人工智能","数据库","智能体","史学研究新形态"],"keywordsEn":["Artificial Intelligence","Database","Intelligent Agent","New Forms of Historical Research"],"abstract":"大语言模型与生成式人工智能被引入史学研究领域后，引发了史学研究的重大变化。一方面，在数字史学研究中，解决了人工搭建数据库的难题，简化了软件操作流程，一定程度上缓解了数字史学发展的困境。另一方面，催生出历史智能体的设计理念，以知识库代替数据库，实现全文本解析；借助大语言模型与生成式人工智能的自主生成能力，最大限度地模拟历史学者的工作流程与思维方式，成为“数智时代”产出的新型知识系统。历史智能体设计在一定程度上实现了“人工智能+”“人机协作”，为史学研究开辟了新的认知路径。但是，该研究模式并不能取代传统史学与数字史学研究，史学研究范式的多样性才是推动史学研究繁荣发展的重要动力。","abstractEn":"","pages":"138-151","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=VjJSRHlrQ2dSQitrVS9TN3NDUjY2eERtSnE2V0dqTE9pTGh6VVdkNzlkTWQxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"张萍. 从数据库到智能体：人工智能参与史学研究新形态[J]. 中国社会科学, 2026(8): 138-151.","lenZh":290,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026008010","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"18世纪中华法系的空间结构及影响","summary":"如何全面客观地展示中华法系，一直是法律史和比较法研究共同关注的重大理论问题。清末民初以来，既有关于中华法系的研究主要集中于纵向时间维度，横向空间维度的分析不足。18世纪中华法系受内/外服区分“三重天下”构造的影响，除中央行省外，其空间结构由内而外，分别对藩部、封贡国、互市国及海外诸国产生了不同层次、样态和性质的影响。中央政权在国家法体系中为理藩体制下隶属于中央的藩部制定了“地区特别法”，同时默认其适用自行创制的“地方性法律”。封贡体制下的封贡国，基于“政治—文化”上的双重认同，主动选择继受中国法，成为中华法系的有机组成部分。未加入封贡体制的互市国和海外诸国，基于“文化”上的需要，将中国法作为一种实现自身独立主体性的“知识资源”。中华法系不仅在横向空间维度上影响了外部世界法律秩序的建构与发展，而且通过具有“同心圆”特征的空间结构，维系了一种由政治统属、政治—文化认同、文化性知识利用等多重机制共同构成的世界性法系地位。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026008010","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"8","authors":["李栋"],"affils":["武汉大学法学院"],"title":"18世纪中华法系的空间结构及影响","titleEn":"The Spatial Structure and Influence of the Chinese Legal Family in the 18th Century","keywords":["中华法系","法律继受","比较法律史","涉外法治"],"keywordsEn":["Chinese legal family","legal reception","comparative legal history","foreign-related rule of law"],"abstract":"如何全面客观地展示中华法系，一直是法律史和比较法研究共同关注的重大理论问题。清末民初以来，既有关于中华法系的研究主要集中于纵向时间维度，横向空间维度的分析不足。18世纪中华法系受内/外服区分“三重天下”构造的影响，除中央行省外，其空间结构由内而外，分别对藩部、封贡国、互市国及海外诸国产生了不同层次、样态和性质的影响。中央政权在国家法体系中为理藩体制下隶属于中央的藩部制定了“地区特别法”，同时默认其适用自行创制的“地方性法律”。封贡体制下的封贡国，基于“政治—文化”上的双重认同，主动选择继受中国法，成为中华法系的有机组成部分。未加入封贡体制的互市国和海外诸国，基于“文化”上的需要，将中国法作为一种实现自身独立主体性的“知识资源”。中华法系不仅在横向空间维度上影响了外部世界法律秩序的建构与发展，而且通过具有“同心圆”特征的空间结构，维系了一种由政治统属、政治—文化认同、文化性知识利用等多重机制共同构成的世界性法系地位。","abstractEn":"","pages":"152-175","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=VjJSRHlrQ2dSQitrVS9TN3NDUjY2L05vZTJSbDFOdzdKN0pSQTI4WU5NRWQxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"李栋. 18世纪中华法系的空间结构及影响[J]. 中国社会科学, 2026(8): 152-175.","lenZh":413,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026008011","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"中古西欧的帝国观念与帝国政治","summary":"中古神圣罗马帝国的存在具有三重意义：作为一种象征或符号；作为法学家和教会思想家建构的理论产物；作为一个政治实体。作为一种象征或符号，帝国反映了西欧人的集体历史记忆，也体现了西欧人的理想和抱负。法学家和教会思想家的理论建构论证了皇帝在法律上的应有权利，为帝国提供合法性和想象空间。作为政治实体，帝国始终受制于政治局势和自身实力。皇帝在不同程度上心怀帝国理想，在实践中却奉行政治实用主义。中古时期，有关帝国的理论和法权与帝国的政治实践相去甚远，甚至与西欧国家发展趋向背道而驰。中古后期，帝国政治的演进驶入地方化轨道，渐趋德意志化。中古帝国呈现的是一部理论与实践相互矛盾、相互背离的历史。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026008011","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"8","authors":["侯树栋"],"affils":["暨南大学文学院历史学系"],"title":"中古西欧的帝国观念与帝国政治","titleEn":"Concepts of Empire and Imperial Politics in Medieval Western Europe","keywords":["中古西欧","神圣罗马帝国","帝国观念","帝国政治"],"keywordsEn":["Medieval Western Europe","Holy Roman Empire","Imperial Ideology","Imperial Politics"],"abstract":"中古神圣罗马帝国的存在具有三重意义：作为一种象征或符号；作为法学家和教会思想家建构的理论产物；作为一个政治实体。作为一种象征或符号，帝国反映了西欧人的集体历史记忆，也体现了西欧人的理想和抱负。法学家和教会思想家的理论建构论证了皇帝在法律上的应有权利，为帝国提供合法性和想象空间。作为政治实体，帝国始终受制于政治局势和自身实力。皇帝在不同程度上心怀帝国理想，在实践中却奉行政治实用主义。中古时期，有关帝国的理论和法权与帝国的政治实践相去甚远，甚至与西欧国家发展趋向背道而驰。中古后期，帝国政治的演进驶入地方化轨道，渐趋德意志化。中古帝国呈现的是一部理论与实践相互矛盾、相互背离的历史。","abstractEn":"","pages":"176-195","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=VjJSRHlrQ2dSQitrVS9TN3NDUjY2L2s0cG1LZThXNW1jemtrM0ZPT0VRQWQxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"侯树栋. 中古西欧的帝国观念与帝国政治[J]. 中国社会科学, 2026(8): 176-195.","lenZh":291,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026008012","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"因果与意识问题再反思——与吴小安商榷","summary":"吴小安在《机器意识与因果自我模型》中提出，没有因果自我模型就没有真正的机器意识。这是一个大胆的设想，但其具体观点与论证中有一些问题值得商榷。就因果自我模型对于机器意识的必要性论断而言，其主要论证的两个步骤都有进一步讨论的空间。第一，建构因果自我模型并非获得关于自身的因果推理能力的唯一途径。第二，因果推理与反事实思维能力亦非高水平意识状态的必要条件，可能只是某些特定意识内容的条件。另外，文章声称“统合”主流意识理论的主张，有混淆本体论层面的因果结构与认识论层面的因果表征之嫌。针对如何在结构因果模型中融入自我，也存在更有潜力的思路。没有理由认为人工智能的因果进路与机器意识之间有紧密联系，前者没有且不应该把后者作为目标。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026008012","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"8","authors":["张寄冀"],"affils":["香港中文大学哲学系"],"title":"因果与意识问题再反思——与吴小安商榷","titleEn":"Further Reflections on Causality and Consciousness——A Discussion with Wu Xiaoan","keywords":["机器意识","因果自我模型","因果人工智能"],"keywordsEn":["Machine consciousness","Causal self-model","Causal artificial intelligence"],"abstract":"吴小安在《机器意识与因果自我模型》中提出，没有因果自我模型就没有真正的机器意识。这是一个大胆的设想，但其具体观点与论证中有一些问题值得商榷。就因果自我模型对于机器意识的必要性论断而言，其主要论证的两个步骤都有进一步讨论的空间。第一，建构因果自我模型并非获得关于自身的因果推理能力的唯一途径。第二，因果推理与反事实思维能力亦非高水平意识状态的必要条件，可能只是某些特定意识内容的条件。另外，文章声称“统合”主流意识理论的主张，有混淆本体论层面的因果结构与认识论层面的因果表征之嫌。针对如何在结构因果模型中融入自我，也存在更有潜力的思路。没有理由认为人工智能的因果进路与机器意识之间有紧密联系，前者没有且不应该把后者作为目标。","abstractEn":"","pages":"196-203","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=VjJSRHlrQ2dSQitrVS9TN3NDUjY2OEw3SVFJTnc4UmpWVXBVcy9tWG1UUWQxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"张寄冀. 因果与意识问题再反思——与吴小安商榷[J]. 中国社会科学, 2026(8): 196-203.","lenZh":311,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026008002","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"新质生产力概念的历史逻辑与理论贡献","summary":"“生产力”在政治经济学的发展历程中从偶尔提及的术语，转变为理论构建的基本范畴，进而发展出系统的生产力理论。马克思通过批判资产阶级经济学的生产力理论形成了自己的生产力概念，明确提出生产力本质上是人的实践能力，是历史的产物和基础，还将革命阶级视为最强大的一种生产力。马克思的生产力概念超越了资产阶级政治经济学的狭隘视域，呈现了其思想的整体性和革命性特质。新质生产力概念超越了传统生产力定义的理论视域，凸显了创新的主导作用和丰富内容。它不仅以马克思的生产力概念为理论基础，而且发展了马克思生产力概念的基本内涵，从而实现了生产力概念史上的又一次飞跃，对历史唯物主义作出了原理性贡献。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026008002","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"8","authors":["周可"],"affils":["武汉大学哲学学院"],"title":"新质生产力概念的历史逻辑与理论贡献","titleEn":"The Historical Logic and Theoretical Contributions of the Concept of New Quality Productive Forces","keywords":["马克思","生产力","新质生产力","实践能力","创新"],"keywordsEn":["Marx","productive forces","new quality productive forces","practical ability","innovation"],"abstract":"“生产力”在政治经济学的发展历程中从偶尔提及的术语，转变为理论构建的基本范畴，进而发展出系统的生产力理论。马克思通过批判资产阶级经济学的生产力理论形成了自己的生产力概念，明确提出生产力本质上是人的实践能力，是历史的产物和基础，还将革命阶级视为最强大的一种生产力。马克思的生产力概念超越了资产阶级政治经济学的狭隘视域，呈现了其思想的整体性和革命性特质。新质生产力概念超越了传统生产力定义的理论视域，凸显了创新的主导作用和丰富内容。它不仅以马克思的生产力概念为理论基础，而且发展了马克思生产力概念的基本内涵，从而实现了生产力概念史上的又一次飞跃，对历史唯物主义作出了原理性贡献。","abstractEn":"","pages":"21-39","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=VjJSRHlrQ2dSQitrVS9TN3NDUjY2OHIzSWFpQjJ1YWdiWnIzbFlxZE02c2QxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"周可. 新质生产力概念的历史逻辑与理论贡献[J]. 中国社会科学, 2026(8): 21-39.","lenZh":287,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026008001","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"党的治国理政创新理论蕴含的新政治学原理","summary":"党的治国理政创新理论蕴含的新政治学原理亟待提炼和阐发。现代政治学原理的元国家理论是基于欧洲民族国家历史而建构的主权、政府、人口、领土“国家要素说”，但是它既无法解释中国式“党和国家领导体制”的事实，在理论上也没有回答主权是如何转化为国家权力的。中国共产党作为领导型政党的革命建国（创制主权）、组织政权（政府）以及“中国之治”的持久经验表明，领导型政党是内生性的国家要素，这个重大发现为“党的全面领导”“党的领导是中国特色社会主义最本质的特征”等重大政治命题提供了根本依据。领导权是人民主权转换为国家权力的关键一环，领导型政党第一次解决了公共性权力私有化的千年难题，使得公共性权力掌握在公共性政治力量手里。在将党的治国理政创新理论转换为“原理”的双向运动中，勾画出了建构中国政治学自主知识体系的路线图。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026008001","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"8","authors":["杨光斌"],"affils":["中国人民大学"],"title":"党的治国理政创新理论蕴含的新政治学原理","titleEn":"New Principles of Political Science Embedded in the Communist Party of China's Innovative Theories of State Governance","keywords":["领导型政党","全过程人民民主","国家治理现代化","新政治学原理","党的创新理论"],"keywordsEn":["Leadership party","whole-process people's democracy","modernization of national governance","New Principles of Political Science","the Party's innovative theory"],"abstract":"党的治国理政创新理论蕴含的新政治学原理亟待提炼和阐发。现代政治学原理的元国家理论是基于欧洲民族国家历史而建构的主权、政府、人口、领土“国家要素说”，但是它既无法解释中国式“党和国家领导体制”的事实，在理论上也没有回答主权是如何转化为国家权力的。中国共产党作为领导型政党的革命建国（创制主权）、组织政权（政府）以及“中国之治”的持久经验表明，领导型政党是内生性的国家要素，这个重大发现为“党的全面领导”“党的领导是中国特色社会主义最本质的特征”等重大政治命题提供了根本依据。领导权是人民主权转换为国家权力的关键一环，领导型政党第一次解决了公共性权力私有化的千年难题，使得公共性权力掌握在公共性政治力量手里。在将党的治国理政创新理论转换为“原理”的双向运动中，勾画出了建构中国政治学自主知识体系的路线图。","abstractEn":"","pages":"4-20","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=VjJSRHlrQ2dSQitrVS9TN3NDUjY2ejM0UXFzY1FHQXEvbHpwdVhoem14WWQxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"杨光斌. 党的治国理政创新理论蕴含的新政治学原理[J]. 中国社会科学, 2026(8): 4-20.","lenZh":350,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026008003","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"作为文艺事业的新大众文艺","summary":"新大众文艺深植于中国文艺的“人民性”传统，是对当代数智化大众文艺蕴含的文化创新性的理论提炼，其以“大众化先锋”的方式构成了“新的文艺”。从文艺事业的角度来理解，新大众文艺包含五个层面的含义：一是文艺事业表现了中国马克思主义者对大众文艺属性的新理解与本土化实践，对大众文艺的改造与新创造构成了一种中国经验；二是表明大众文艺与国家事业、社会发展的深层关联，注重其社会功能；三是强调大众的主体性，提倡创造新的大众文艺并拓展文艺的人民性内涵；四是新大众文艺存在一个从普及到提高的发展过程，追求文艺自身的变革与繁荣；五是需要关注大众文艺的组织生产问题，关注其生产、流通、接受与评论各个环节的实践与引导，促进其沿着正确的方向发展。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026008003","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"8","authors":["黎杨全"],"affils":["华中师范大学文学院"],"title":"作为文艺事业的新大众文艺","titleEn":"New Popular Literature and Art as the Cause of Literature and Art","keywords":["新大众文艺","文艺事业","媒介迭代","人民性","“大众化先锋”"],"keywordsEn":["New Mass Literature and Art","Literature and Art Undertakings","Media Iteration","People’s Nature","“Vanguard of Popularization”"],"abstract":"新大众文艺深植于中国文艺的“人民性”传统，是对当代数智化大众文艺蕴含的文化创新性的理论提炼，其以“大众化先锋”的方式构成了“新的文艺”。从文艺事业的角度来理解，新大众文艺包含五个层面的含义：一是文艺事业表现了中国马克思主义者对大众文艺属性的新理解与本土化实践，对大众文艺的改造与新创造构成了一种中国经验；二是表明大众文艺与国家事业、社会发展的深层关联，注重其社会功能；三是强调大众的主体性，提倡创造新的大众文艺并拓展文艺的人民性内涵；四是新大众文艺存在一个从普及到提高的发展过程，追求文艺自身的变革与繁荣；五是需要关注大众文艺的组织生产问题，关注其生产、流通、接受与评论各个环节的实践与引导，促进其沿着正确的方向发展。","abstractEn":"","pages":"40-59","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=VjJSRHlrQ2dSQitrVS9TN3NDUjY2N1g0ZWhUdFFDMEFjclpxWkVYTTBDY2QxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"黎杨全. 作为文艺事业的新大众文艺[J]. 中国社会科学, 2026(8): 40-59.","lenZh":309,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026008004","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"有组织的发现：中国特色的产业政策执行机制","summary":"在西方主流经济学对产业政策有效性普遍存疑的背景下，中国产业政策在特定领域与阶段取得了积极成效。既有争论长期聚焦政府能否“选对赢家”，而中国产业政策的关键优势更多体现在内在运行机制，即“有组织的发现”。结合新能源汽车产业的典型案例可知，这一机制通过中央政府的战略定向、地方政府的差异化执行与市场竞争的最终筛选，在产业发展不同阶段放大企业进入与试错空间，并依托退出约束与事后纠偏发挥调节功能。该机制的有效运转依赖于全国统一市场、中央层面的退出治理能力和制度性纠偏能力等基础条件。当前，纵深推进全国统一大市场建设，旨在推动中央政府、地方政府与市场形成协同互动、良性循环的制度合力，为“有组织的发现”在更高发展阶段实现制度化升级创造条件。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026008004","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"8","authors":["周黎安"],"affils":["北京大学光华管理学院"],"title":"有组织的发现：中国特色的产业政策执行机制","titleEn":"Organized Discovery: The Implementation Mechanism of Industrial Policy with Chinese Characteristics","keywords":["产业政策","有组织的发现","颠覆式创新","新能源汽车产业"],"keywordsEn":["Industrial policy","organized discovery","disruptive innovation","new energy vehicle industry"],"abstract":"在西方主流经济学对产业政策有效性普遍存疑的背景下，中国产业政策在特定领域与阶段取得了积极成效。既有争论长期聚焦政府能否“选对赢家”，而中国产业政策的关键优势更多体现在内在运行机制，即“有组织的发现”。结合新能源汽车产业的典型案例可知，这一机制通过中央政府的战略定向、地方政府的差异化执行与市场竞争的最终筛选，在产业发展不同阶段放大企业进入与试错空间，并依托退出约束与事后纠偏发挥调节功能。该机制的有效运转依赖于全国统一市场、中央层面的退出治理能力和制度性纠偏能力等基础条件。当前，纵深推进全国统一大市场建设，旨在推动中央政府、地方政府与市场形成协同互动、良性循环的制度合力，为“有组织的发现”在更高发展阶段实现制度化升级创造条件。","abstractEn":"","pages":"60-82","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=VjJSRHlrQ2dSQitrVS9TN3NDUjY2eENLVE9MVk5Fb3hHU04zZWlINE1yZ2QxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"周黎安. 有组织的发现：中国特色的产业政策执行机制[J]. 中国社会科学, 2026(8): 60-82.","lenZh":315,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026008005","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"世界现代化的开放逻辑及关系转向","summary":"从内在逻辑来看，现代化并不等于西方化。西方国家在殖民扩张基础上构筑的世界现代化图景导致国际社会对现代化的认知长期扭曲。西方社会基于线性、等级和竞争思维形成的个体主义现代化认知已难以解释全球南方群体性崛起与多元现代化路径并存的新现实。随着经济全球化的深入发展及其带来的世界现代化实践空间的拓展，开放已成为世界现代化的鲜明特征，各国现代化呈现日益明显的空间依存、联动发展和多维互动关系。有关世界现代化的认知随之经历深刻的“关系转向”。中国式现代化的成功实践亦反映了当代世界现代化的共生、共赢、共享逻辑。新时代以来，为顺应世界现代化的关系转向，中国式现代化的开放标识愈发凸显，并不断丰富和塑造着发展的和平性、合作的互利性、繁荣的共同性等世界现代化的新特质。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026008005","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"8","authors":["徐秀军","田旭"],"affils":["中国社会科学院大学全球与区域国别学院"],"title":"世界现代化的开放逻辑及关系转向","titleEn":"The Open Logic and Relational Turn of Global Modernization","keywords":["经济全球化","世界现代化","认知转向","中国式现代化"],"keywordsEn":["Economic globalization","world modernization","cognitive turn","Chinese modernization"],"abstract":"从内在逻辑来看，现代化并不等于西方化。西方国家在殖民扩张基础上构筑的世界现代化图景导致国际社会对现代化的认知长期扭曲。西方社会基于线性、等级和竞争思维形成的个体主义现代化认知已难以解释全球南方群体性崛起与多元现代化路径并存的新现实。随着经济全球化的深入发展及其带来的世界现代化实践空间的拓展，开放已成为世界现代化的鲜明特征，各国现代化呈现日益明显的空间依存、联动发展和多维互动关系。有关世界现代化的认知随之经历深刻的“关系转向”。中国式现代化的成功实践亦反映了当代世界现代化的共生、共赢、共享逻辑。新时代以来，为顺应世界现代化的关系转向，中国式现代化的开放标识愈发凸显，并不断丰富和塑造着发展的和平性、合作的互利性、繁荣的共同性等世界现代化的新特质。","abstractEn":"","pages":"83-103","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=VjJSRHlrQ2dSQitrVS9TN3NDUjY2NXdtN0VqVmRvTFI0Ukh6UHBIWWxVZ2QxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"徐秀军, 田旭. 世界现代化的开放逻辑及关系转向[J]. 中国社会科学, 2026(8): 83-103.","lenZh":325,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026007006","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"当代中国的城市更新与社会治理创新——基于人民性的协同改革及其机理","summary":"当代中国的城市更新与社会治理创新是现代化人民城市建设的两大关键进程。在人民性理念指引下，两者在实践维度紧密交融并深嵌于社区公共生活之中，这对于增进社会团结与激发社会活力具有重要作用。从机制研究视角出发，可以呈现这两大进程相互促进的内在机理，及其对塑造公共性领域、提升城市包容性、发展社会力量的重要意义。城市更新与社会治理创新的复杂互动，彰显了人民性引领下城市发展的制度逻辑，呈现了中国城市空间生产的共同体逻辑，相关理论与实践探索可为中国特色城市社会学研究提供新进路。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026007006","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"7","authors":["黄晓春"],"affils":["上海大学社会学院"],"title":"当代中国的城市更新与社会治理创新——基于人民性的协同改革及其机理","titleEn":"Urban Renewal and Social Governance Innovation in Contemporary China——A Coordinated Reform Based on People-Centeredness and Its Mechanism","keywords":["人民性","城市更新","社会治理创新","协同改革","现代化人民城市"],"keywordsEn":["People-centeredness","Urban Renewal","Innovation in Social Governance","Coordinated Reform","Modern People's City"],"abstract":"当代中国的城市更新与社会治理创新是现代化人民城市建设的两大关键进程。在人民性理念指引下，两者在实践维度紧密交融并深嵌于社区公共生活之中，这对于增进社会团结与激发社会活力具有重要作用。从机制研究视角出发，可以呈现这两大进程相互促进的内在机理，及其对塑造公共性领域、提升城市包容性、发展社会力量的重要意义。城市更新与社会治理创新的复杂互动，彰显了人民性引领下城市发展的制度逻辑，呈现了中国城市空间生产的共同体逻辑，相关理论与实践探索可为中国特色城市社会学研究提供新进路。","abstractEn":"","pages":"105-126","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=SkgvN0dCOXRMNXFwazVOWTQxS3o0QldqVEpOWkFnYmFCMDVodk84U2FlSWQxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"黄晓春. 当代中国的城市更新与社会治理创新——基于人民性的协同改革及其机理[J]. 中国社会科学, 2026(7): 105-126.","lenZh":233,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026007007","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"迈向文化组织：当代中国乡村宗族变迁","summary":"中国式现代化进程中，宗族组织形态渐趋松散，但其文化影响力仍延续于农民日常生活。基于农民日常生活视角，从生活实践、共有财产与宗族观念三个维度考察宗族变迁，可以看到宗族逐渐从传统社会中的绵续性事业组织转向文化组织，不再以制度规范成员生活，而是以文化符号承载对血脉传承与家世绵延的关怀。宗族变迁在延续文化活力的同时，亦带来治理挑战，需要通过加强引领，推动中华传统美德与社会主义核心价值观融合，共同塑造农民的精神世界。这既是乡村善治的内在要求，也是中国式现代化进程中乡土传统实现创造性转化的重要路径。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026007007","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"7","authors":["董磊明","谢梅婕"],"affils":["北京师范大学社会学院","江西财经大学财政与社会治理研究院"],"title":"迈向文化组织：当代中国乡村宗族变迁","titleEn":"Moving toward Cultural Organizations: The Transformation of Rural Lineages in Contemporary China","keywords":["宗族","生活实践","共有财产","宗族观念","文化组织"],"keywordsEn":["Clan","Life Practice","Common Property","Clan Ideology","Cultural Organization"],"abstract":"中国式现代化进程中，宗族组织形态渐趋松散，但其文化影响力仍延续于农民日常生活。基于农民日常生活视角，从生活实践、共有财产与宗族观念三个维度考察宗族变迁，可以看到宗族逐渐从传统社会中的绵续性事业组织转向文化组织，不再以制度规范成员生活，而是以文化符号承载对血脉传承与家世绵延的关怀。宗族变迁在延续文化活力的同时，亦带来治理挑战，需要通过加强引领，推动中华传统美德与社会主义核心价值观融合，共同塑造农民的精神世界。这既是乡村善治的内在要求，也是中国式现代化进程中乡土传统实现创造性转化的重要路径。","abstractEn":"","pages":"127-146","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=SkgvN0dCOXRMNXFwazVOWTQxS3o0S1A0OHZKQlF6ekI0QjZ3ZFRuZGtzQWQxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"董磊明, 谢梅婕. 迈向文化组织：当代中国乡村宗族变迁[J]. 中国社会科学, 2026(7): 127-146.","lenZh":246,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026007008","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"西周王畿多元人群互动交融的考古学观察","summary":"西周王畿既是王朝核心政治区域，也是多元人群交往交流交融的重要舞台。多元人群交融共生格局的形成，根源于周相较商具有更强的包容性特质。这一特质贯穿西周兴衰，自先周时期与戎狄的初步互动中萌芽，到商周之际灭商战争中的强力整合，再到西周早期分封制下的迁徙共处，直至西周晚期深度交融，其始终是周人崛起与兴盛的核心动力。纵观王畿多元人群，从都城看，呈现出“以都为聚，四方攸同”的特点；从各层级聚落和空间格局上看，呈现出“友邦冢君，保我有周”的态势；从多元人群的整体构成上看，呈现出“东南西北，无思不服”的气魄。周人“怀柔兼容”的特性，将文化差异在互动交融中转化为强大的政治凝聚力，为中华民族“多元一体”格局的形成奠定了历史基础。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026007008","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"7","authors":["王一凡"],"affils":["中国社会科学院考古研究所"],"title":"西周王畿多元人群互动交融的考古学观察","titleEn":"Archaeological Observations on the Interaction and Integration of Diverse Populations in the Western Zhou Royal Domain","keywords":["西周王畿","多元人群","包容性","交往交流交融"],"keywordsEn":["Western Zhou Royal Domain","Diverse Populations","Inclusiveness","Interaction, Exchange, and Integration"],"abstract":"西周王畿既是王朝核心政治区域，也是多元人群交往交流交融的重要舞台。多元人群交融共生格局的形成，根源于周相较商具有更强的包容性特质。这一特质贯穿西周兴衰，自先周时期与戎狄的初步互动中萌芽，到商周之际灭商战争中的强力整合，再到西周早期分封制下的迁徙共处，直至西周晚期深度交融，其始终是周人崛起与兴盛的核心动力。纵观王畿多元人群，从都城看，呈现出“以都为聚，四方攸同”的特点；从各层级聚落和空间格局上看，呈现出“友邦冢君，保我有周”的态势；从多元人群的整体构成上看，呈现出“东南西北，无思不服”的气魄。周人“怀柔兼容”的特性，将文化差异在互动交融中转化为强大的政治凝聚力，为中华民族“多元一体”格局的形成奠定了历史基础。","abstractEn":"","pages":"147-164","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=SkgvN0dCOXRMNXFwazVOWTQxS3o0UDlVM295NWNKQXREUVY3OXNyZ3g2NGQxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"王一凡. 西周王畿多元人群互动交融的考古学观察[J]. 中国社会科学, 2026(7): 147-164.","lenZh":307,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026007009","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"经史分合与中国知识体系的近代转型","summary":"清末民初，中国学术经历了从四部之学向分科之学的知识体系转型，经学被分解为各学科门类的研究对象。若从近代学术转型的整体进程而言，传统形态的经学逐渐退出了历史舞台，但仍有必要考察经学与近代知识体系转型的复杂关系。近代学术，经史嬗变，各派学人转化经史的方式迥异。20世纪三四十年代，超越经学派分、“由经入史”成为学术界关注的重要问题，分科与“复古”是研究、讲授经学乃至中国学术的两种趋向。前者以“中国学术”容纳经学，展现中国学术的开放性与包容性，后者致力于以“复古”求解放，凸显经学在中国学术中的独特性。辨析近代经史转承过程中“由经入史”的复杂性，是探索现代学术分科知识系统中会通经史的有效路径，为构建义理、制度与事实相贯通的中国自主知识体系提供思想资源。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026007009","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"7","authors":["张凯"],"affils":["浙江大学历史学院"],"title":"经史分合与中国知识体系的近代转型","titleEn":"The Separation and Integration of Jingxue and Historiography and the Modern Transformation of Chinese Knowledge System","keywords":["经学","经学史","近代知识体系","经史嬗变"],"keywordsEn":["Classics","History of Classical Studies","Modern Knowledge System","Evolution of Classics and Historiography"],"abstract":"清末民初，中国学术经历了从四部之学向分科之学的知识体系转型，经学被分解为各学科门类的研究对象。若从近代学术转型的整体进程而言，传统形态的经学逐渐退出了历史舞台，但仍有必要考察经学与近代知识体系转型的复杂关系。近代学术，经史嬗变，各派学人转化经史的方式迥异。20世纪三四十年代，超越经学派分、“由经入史”成为学术界关注的重要问题，分科与“复古”是研究、讲授经学乃至中国学术的两种趋向。前者以“中国学术”容纳经学，展现中国学术的开放性与包容性，后者致力于以“复古”求解放，凸显经学在中国学术中的独特性。辨析近代经史转承过程中“由经入史”的复杂性，是探索现代学术分科知识系统中会通经史的有效路径，为构建义理、制度与事实相贯通的中国自主知识体系提供思想资源。","abstractEn":"","pages":"165-183","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=SkgvN0dCOXRMNXFwazVOWTQxS3o0QkRtSnE2V0dqTE9pTGh6VVdkNzlkTWQxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"张凯. 经史分合与中国知识体系的近代转型[J]. 中国社会科学, 2026(7): 165-183.","lenZh":325,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026007010","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"法益修复的体系定位——兼与“功能违法论”商榷","summary":"法益修复在犯罪论中如何安置，理论界存在重大争议。功能违法论主张对法益修复以不具有需罚的违法性为由出罪，混淆了不法与有责的区分，理论建构上存在缺陷。新规范二元论主张不法是从行为规范角度的观察，本质是法律的禁止或允许；而有责是立足于裁判规范的判断，本质是法律的宽恕。法益修复的体系定位仍需以二者区分为前提。功能违法论不具有替代功能责任论的妥当性，而功能责任论并非完美理论。刑法理论应当结合我国最新立法与司法需求提倡修复责任论，对责任采取应受谴责性与需罚性的二元构造：在具备应受谴责性的前提下，尚需进行需罚性判断，并以此实现因法益修复而免责、减轻责任的类型区分。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026007010","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"7","authors":["姜涛"],"affils":["华东政法大学刑事法学院"],"title":"法益修复的体系定位——兼与“功能违法论”商榷","titleEn":"The Systematic Positioning of the Restoration of Legal Interests——With a Discussion on the \"Functional Theory of Illegality\"","keywords":["功能违法论","功能责任论","修复责任论","新规范二元论","法益修复"],"keywordsEn":["Functional Illegality Theory","Functional Liability Theory","Remedial Liability Theory","New Normative Dualism","Legal Interest Restoration"],"abstract":"法益修复在犯罪论中如何安置，理论界存在重大争议。功能违法论主张对法益修复以不具有需罚的违法性为由出罪，混淆了不法与有责的区分，理论建构上存在缺陷。新规范二元论主张不法是从行为规范角度的观察，本质是法律的禁止或允许；而有责是立足于裁判规范的判断，本质是法律的宽恕。法益修复的体系定位仍需以二者区分为前提。功能违法论不具有替代功能责任论的妥当性，而功能责任论并非完美理论。刑法理论应当结合我国最新立法与司法需求提倡修复责任论，对责任采取应受谴责性与需罚性的二元构造：在具备应受谴责性的前提下，尚需进行需罚性判断，并以此实现因法益修复而免责、减轻责任的类型区分。","abstractEn":"","pages":"184-202","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=SkgvN0dCOXRMNXFwazVOWTQxS3o0UE5vZTJSbDFOdzdKN0pSQTI4WU5NRWQxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"姜涛. 法益修复的体系定位——兼与“功能违法论”商榷[J]. 中国社会科学, 2026(7): 184-202.","lenZh":278,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026007002","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"马克思“从抽象上升到具体”中的认识论革命","summary":"马克思“哲学革命”在政治经济学批判中的深化发展具有一个关键质变点：“从抽象上升到具体”等科学方法论构建过程中蕴含着前提性的“认识论革命”。应聚焦于政治经济学方法的“两条道路”，阐明一直以来被忽视的思维行程对现实具体的“再生产”问题。所谓“两条道路”在认识论意义上就是：从具体到抽象的“表象—再现”过程、科学抽象过程与从抽象到具体的“思维中再生产”过程。以表象具体和抽象范畴为中介对现实具体进行“思维中再生产”是贯穿“两条道路”的主线。马克思重构了存在论与认识论的传统关系，构建了特定社会存在论与特定社会认识论的根本制约与能动再生产关系，改变了近代认识论的“意识”范式，将“社会定在”作为认识论的核心范式，开启了面向资本主义新变化和社会主义新发展的未来认识论向度。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026007002","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"7","authors":["郗戈"],"affils":["中国人民大学马克思主义学院"],"title":"马克思“从抽象上升到具体”中的认识论革命","titleEn":"The Epistemological Revolution in Marx's \"Ascent from the Abstract to the Concrete\"","keywords":["《资本论》","社会认识论","“两条道路”","思维中再生产"],"keywordsEn":["\"Capital\"","Social Epistemology","\"Two Paths\"","Reproduction in Thought"],"abstract":"马克思“哲学革命”在政治经济学批判中的深化发展具有一个关键质变点：“从抽象上升到具体”等科学方法论构建过程中蕴含着前提性的“认识论革命”。应聚焦于政治经济学方法的“两条道路”，阐明一直以来被忽视的思维行程对现实具体的“再生产”问题。所谓“两条道路”在认识论意义上就是：从具体到抽象的“表象—再现”过程、科学抽象过程与从抽象到具体的“思维中再生产”过程。以表象具体和抽象范畴为中介对现实具体进行“思维中再生产”是贯穿“两条道路”的主线。马克思重构了存在论与认识论的传统关系，构建了特定社会存在论与特定社会认识论的根本制约与能动再生产关系，改变了近代认识论的“意识”范式，将“社会定在”作为认识论的核心范式，开启了面向资本主义新变化和社会主义新发展的未来认识论向度。","abstractEn":"","pages":"19-38","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=SkgvN0dCOXRMNXFwazVOWTQxS3o0TXIzSWFpQjJ1YWdiWnIzbFlxZE02c2QxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"郗戈. 马克思“从抽象上升到具体”中的认识论革命[J]. 中国社会科学, 2026(7): 19-38.","lenZh":331,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026007003","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"“物”的构成性原则与西方形而上学的近代转型","summary":"关于“物”的构成性原则的“质形论”结构模式曾在西方形而上学中占统治地位，但这种理论模式在西方近代早期受到广泛质疑。与机械论只是表面上抛弃质形论的主要概念不同，康德直接深入质形论中最核心的概念预设，通过对“质料—形式”“实在性”“实体”“实存”等概念的批判性分析，对“物”概念的外延和内涵作了新的界定。这些思考带来了西方形而上学的重要转变，使得形而上学被划分为不同部门来分别研究自然物和自由行动者。其中，对“物”的“动力论”理解模式引出了一门新的自然哲学，而对自由行动者的形而上学反思则为一门“人”的形而上学奠定了基础。这一围绕“物”的构成性原则问题展开的形而上学近代转型，深刻影响了西方哲学的后续发展。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026007003","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"7","authors":["谢裕伟"],"affils":["中山大学哲学系"],"title":"“物”的构成性原则与西方形而上学的近代转型","titleEn":"The Constitutive Principles of \"Thing\" and the Modern Transformation of Western Metaphysics","keywords":["“物”","质形论","康德","动力论","“人”的形而上学"],"keywordsEn":["\"Thing\"","Hylomorphism","Kant","Dynamism","Metaphysics of \"Person\""],"abstract":"关于“物”的构成性原则的“质形论”结构模式曾在西方形而上学中占统治地位，但这种理论模式在西方近代早期受到广泛质疑。与机械论只是表面上抛弃质形论的主要概念不同，康德直接深入质形论中最核心的概念预设，通过对“质料—形式”“实在性”“实体”“实存”等概念的批判性分析，对“物”概念的外延和内涵作了新的界定。这些思考带来了西方形而上学的重要转变，使得形而上学被划分为不同部门来分别研究自然物和自由行动者。其中，对“物”的“动力论”理解模式引出了一门新的自然哲学，而对自由行动者的形而上学反思则为一门“人”的形而上学奠定了基础。这一围绕“物”的构成性原则问题展开的形而上学近代转型，深刻影响了西方哲学的后续发展。","abstractEn":"","pages":"39-58","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=SkgvN0dCOXRMNXFwazVOWTQxS3o0TFg0ZWhUdFFDMEFjclpxWkVYTTBDY2QxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"谢裕伟. “物”的构成性原则与西方形而上学的近代转型[J]. 中国社会科学, 2026(7): 39-58.","lenZh":301,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026007001","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"中国共产党推进党的建设伟大工程：历程、成就与经验","summary":"百余年来，党的建设紧扣不同历史时期的中心任务，历经新民主主义革命时期的奠基、社会主义革命和建设时期的探索、改革开放和社会主义现代化建设新时期的发展、新时代的全面加强，取得了伟大成就。这集中体现在理论探索、自身建设和事业发展上，有力推进了马克思主义中国化时代化，锻造了走在时代前列的中国共产党，并在以中国式现代化全面推进中华民族伟大复兴的新征程上持续创造新的历史伟业。党的建设积累了宝贵的历史经验，即坚持党的领导特别是党中央集中统一领导，坚持党要管党、全面从严治党，坚持以加强党的长期执政能力建设、先进性和纯洁性建设为主线，坚持以党的政治建设统领党的建设布局，坚持一体推进不敢腐不能腐不想腐，坚持马克思主义的科学方法论。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026007001","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"7","authors":["欧阳淞"],"affils":["中共中央党史和文献研究院"],"title":"中国共产党推进党的建设伟大工程：历程、成就与经验","titleEn":"The Communist Party of China Advances the Great Project of Party Building: Journey, Achievements, and Experience","keywords":["党的建设","党的领导","党的事业","历史经验"],"keywordsEn":["Party Building","Party Leadership","Party Cause","Historical Experience"],"abstract":"百余年来，党的建设紧扣不同历史时期的中心任务，历经新民主主义革命时期的奠基、社会主义革命和建设时期的探索、改革开放和社会主义现代化建设新时期的发展、新时代的全面加强，取得了伟大成就。这集中体现在理论探索、自身建设和事业发展上，有力推进了马克思主义中国化时代化，锻造了走在时代前列的中国共产党，并在以中国式现代化全面推进中华民族伟大复兴的新征程上持续创造新的历史伟业。党的建设积累了宝贵的历史经验，即坚持党的领导特别是党中央集中统一领导，坚持党要管党、全面从严治党，坚持以加强党的长期执政能力建设、先进性和纯洁性建设为主线，坚持以党的政治建设统领党的建设布局，坚持一体推进不敢腐不能腐不想腐，坚持马克思主义的科学方法论。","abstractEn":"","pages":"4-18","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=SkgvN0dCOXRMNXFwazVOWTQxS3o0RDM0UXFzY1FHQXEvbHpwdVhoem14WWQxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"欧阳淞. 中国共产党推进党的建设伟大工程：历程、成就与经验[J]. 中国社会科学, 2026(7): 4-18.","lenZh":309,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026007004","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"文质论视域中文明概念的反思和重构","summary":"从学理上深化人类文明新形态的研究，需要反思西方文明概念。西方文明内部存在两种难以协调的取向，即文野论意义上的文明和文明体意义上的文明，背后蕴含自然与文明、天与人的紧张。从文质论视域出发可照见西方文明话语的局限在于文明等级论和西方优越论的预设，以及对自然的过度抑制。文质论文明概念注重天人的共明、文质的两尽，内含多层级的复合构造，其局限在于对“文”的理解未能包含生产方式、技术革命、经济生活等维度。对古今文明概念的反思与重释，为阐释人类文明新形态、构筑中国自主的文明理论，提供了养料和资源。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026007004","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"7","authors":["陈赟"],"affils":["华东师范大学中国现代思想文化研究所"],"title":"文质论视域中文明概念的反思和重构","titleEn":"Reflection on and Reconstruction of the Concept of Civilization from the Perspective of the Theory of Wen and Zhi","keywords":["文明","文质","天文与人文","人类文明新形态"],"keywordsEn":["Civilization","Substance and Refinement","Celestial Patterns and Human Culture","New Form of Human Civilization"],"abstract":"从学理上深化人类文明新形态的研究，需要反思西方文明概念。西方文明内部存在两种难以协调的取向，即文野论意义上的文明和文明体意义上的文明，背后蕴含自然与文明、天与人的紧张。从文质论视域出发可照见西方文明话语的局限在于文明等级论和西方优越论的预设，以及对自然的过度抑制。文质论文明概念注重天人的共明、文质的两尽，内含多层级的复合构造，其局限在于对“文”的理解未能包含生产方式、技术革命、经济生活等维度。对古今文明概念的反思与重释，为阐释人类文明新形态、构筑中国自主的文明理论，提供了养料和资源。","abstractEn":"","pages":"59-79","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=SkgvN0dCOXRMNXFwazVOWTQxS3o0QkNLVE9MVk5Fb3hHU04zZWlINE1yZ2QxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"陈赟. 文质论视域中文明概念的反思和重构[J]. 中国社会科学, 2026(7): 59-79.","lenZh":244,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026007005","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"重塑中国城镇空间格局","summary":"受数据和测度方法制约，中国城镇空间格局变动特征长期未被准确呈现。基于新的数据和测度方法发现：中国城化与镇化结构持续分化，总体上镇化增速快于城化，沿海或沿江区域由城化主导，中西部和北方则多由镇化主导；城镇化水平的省际差距虽在缩小，但收敛速度远小于传统指标，且相当数量省份仍处于较低发展阶段；毗邻沿海或沿江地级市的内陆区域空间布局未呈现出与其毗邻地区趋同的集约紧凑特征，反而与其他内陆区域更为接近，从而挑战了贸易成本随距离平滑变化的经典假说。这些发现为理解区域经济差异提供了全新视角，也为因地制宜制定差异化空间政策奠定了事实基础。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026007005","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"7","authors":["李辉文","冯皓","梁渊博"],"affils":["上海对外经贸大学国际经贸学院","上海对外经贸大学区域与产业发展研究中心"],"title":"重塑中国城镇空间格局","titleEn":"Reshaping China's Urban Spatial Pattern","keywords":["城镇化","城镇空间格局","城镇规模结构","城化与镇化"],"keywordsEn":["Urbanization","Urban Spatial Pattern","Urban Scale Structure","City-ization and Town-ization"],"abstract":"受数据和测度方法制约，中国城镇空间格局变动特征长期未被准确呈现。基于新的数据和测度方法发现：中国城化与镇化结构持续分化，总体上镇化增速快于城化，沿海或沿江区域由城化主导，中西部和北方则多由镇化主导；城镇化水平的省际差距虽在缩小，但收敛速度远小于传统指标，且相当数量省份仍处于较低发展阶段；毗邻沿海或沿江地级市的内陆区域空间布局未呈现出与其毗邻地区趋同的集约紧凑特征，反而与其他内陆区域更为接近，从而挑战了贸易成本随距离平滑变化的经典假说。这些发现为理解区域经济差异提供了全新视角，也为因地制宜制定差异化空间政策奠定了事实基础。","abstractEn":"","pages":"80-104","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=SkgvN0dCOXRMNXFwazVOWTQxS3o0SndtN0VqVmRvTFI0Ukh6UHBIWWxVZ2QxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"李辉文, 冯皓, 梁渊博. 重塑中国城镇空间格局[J]. 中国社会科学, 2026(7): 80-104.","lenZh":263,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026006002","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"一种面向生活世界的生成式论证理论","summary":"将论证视为生活世界中发生的社会互动，从论证者的角度考察论证理论，可以发现，论证理论往往囿于方法论立场，难以刻画生活世界中论证具有的文化多样性、个体性、动态性和生成性等特性。对此，回归生活世界的实践语境，依据个性化、主位—客位和动态性这三项论证研究原则，提出广义论证概念容纳不同文化和领域及相互之间的多样论证方式，阐明论证行动及论证序列生成过程，构建面向生活世界的生成式论证理论——广义论证理论。运用该理论，不仅能够处理生活世界中的论证问题，还可以探讨当前大语言模型（LLM）瓶颈问题的解决途径，为构建跨文化跨领域论证平台奠定理论基础。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026006002","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"6","authors":["鞠实儿"],"affils":["广东外语外贸大学外国语言学及应用语言学研究中心"],"title":"一种面向生活世界的生成式论证理论","titleEn":"A Generative Theory of Argumentation Oriented toward the Lifeworld","keywords":["生活世界","广义论证","论证理论","社会行动","LLM推理"],"keywordsEn":["Life world","Generalized argumentation","Argumentation theory","Social action","LLM reasoning"],"abstract":"将论证视为生活世界中发生的社会互动，从论证者的角度考察论证理论，可以发现，论证理论往往囿于方法论立场，难以刻画生活世界中论证具有的文化多样性、个体性、动态性和生成性等特性。对此，回归生活世界的实践语境，依据个性化、主位—客位和动态性这三项论证研究原则，提出广义论证概念容纳不同文化和领域及相互之间的多样论证方式，阐明论证行动及论证序列生成过程，构建面向生活世界的生成式论证理论——广义论证理论。运用该理论，不仅能够处理生活世界中的论证问题，还可以探讨当前大语言模型（LLM）瓶颈问题的解决途径，为构建跨文化跨领域论证平台奠定理论基础。","abstractEn":"","pages":"24-43","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=U0h3TXJDdWJrY3NrbDVqcmViMHg0c3IzSWFpQjJ1YWdiWnIzbFlxZE02c2QxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"鞠实儿. 一种面向生活世界的生成式论证理论[J]. 中国社会科学, 2026(6): 24-43.","lenZh":267,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026006001","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"中国共产党推进理论创新的主题主线、历史经验和时代特点","summary":"百余年来中国共产党围绕实现中华民族伟大复兴这个主题，推进马克思主义中国化时代化，积累了宝贵的历史经验，即坚持解放思想、实事求是、与时俱进、求真务实，坚持把马克思主义基本原理同中国具体实际相结合、同中华优秀传统文化相结合，坚持在实践中检验真理和发展真理，坚持把握时代大势、解答时代课题，坚持代表最广大人民的意志和根本利益，坚持用理论创新成果武装全党、教育人民。新时代以来，以习近平同志为核心的党中央接续推进理论创新，呈现出党的领导与人民主体相统一、自信自强与开放包容相统一、固本培元与开拓创新相统一、使命引领与问题导向相统一、系统观念与精准思维相统一的时代特点。这些时代特点丰富了历史经验，深化了对党的理论创新的规律性认识。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026006001","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"6","authors":["艾四林","张传泉"],"affils":["清华大学习近平新时代中国特色社会主义思想研究院","北京邮电大学马克思主义学院"],"title":"中国共产党推进理论创新的主题主线、历史经验和时代特点","titleEn":"The Theme, Main Thread, Historical Experience, and Epochal Characteristics of the Communist Party of China in Advancing Theoretical Innovation","keywords":["中国共产党","理论创新","马克思主义中国化时代化","“两个结合”"],"keywordsEn":["Chinese Communist Party","theoretical innovation","sinicization and modernization of Marxism","\"Two Integrations\""],"abstract":"百余年来中国共产党围绕实现中华民族伟大复兴这个主题，推进马克思主义中国化时代化，积累了宝贵的历史经验，即坚持解放思想、实事求是、与时俱进、求真务实，坚持把马克思主义基本原理同中国具体实际相结合、同中华优秀传统文化相结合，坚持在实践中检验真理和发展真理，坚持把握时代大势、解答时代课题，坚持代表最广大人民的意志和根本利益，坚持用理论创新成果武装全党、教育人民。新时代以来，以习近平同志为核心的党中央接续推进理论创新，呈现出党的领导与人民主体相统一、自信自强与开放包容相统一、固本培元与开拓创新相统一、使命引领与问题导向相统一、系统观念与精准思维相统一的时代特点。这些时代特点丰富了历史经验，深化了对党的理论创新的规律性认识。","abstractEn":"","pages":"4-23","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=U0h3TXJDdWJrY3NrbDVqcmViMHg0ajM0UXFzY1FHQXEvbHpwdVhoem14WWQxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"艾四林, 张传泉. 中国共产党推进理论创新的主题主线、历史经验和时代特点[J]. 中国社会科学, 2026(6): 4-23.","lenZh":311,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026006003","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"政治经济学批判视域中现代人的存在方式及其变革","summary":"立足马克思的政治经济学批判视域，从现实的生活过程出发考察现代人的存在方式及其变革，对于把握人的自由全面发展具有重要意义。通过阐明现代人的内涵及其内在尺度，以揭示交换价值作为现代人存在的社会基础及其自我扬弃的必然性。通过考察现代物质生活的生产方式、社会生活的交往方式和精神生活的展开方式，阐述现代人存在的普遍境遇和时代状况。基于唯物史观的原则和人的发展进程，力图在具有原则高度的实践中把握现代人的存在方式转变。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026006003","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"6","authors":["黄志军"],"affils":["中国人民大学哲学院"],"title":"政治经济学批判视域中现代人的存在方式及其变革","titleEn":"The Mode of Existence of Modern Humans and Its Transformation from the Perspective of the Critique of Political Economy","keywords":["政治经济学批判","现代人","人的存在方式","人的现代化"],"keywordsEn":["Critique of Political Economy","Modern Man","Human Mode of Existence","Human Modernization"],"abstract":"立足马克思的政治经济学批判视域，从现实的生活过程出发考察现代人的存在方式及其变革，对于把握人的自由全面发展具有重要意义。通过阐明现代人的内涵及其内在尺度，以揭示交换价值作为现代人存在的社会基础及其自我扬弃的必然性。通过考察现代物质生活的生产方式、社会生活的交往方式和精神生活的展开方式，阐述现代人存在的普遍境遇和时代状况。基于唯物史观的原则和人的发展进程，力图在具有原则高度的实践中把握现代人的存在方式转变。","abstractEn":"","pages":"44-57","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=U0h3TXJDdWJrY3NrbDVqcmViMHg0clg0ZWhUdFFDMEFjclpxWkVYTTBDY2QxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"黄志军. 政治经济学批判视域中现代人的存在方式及其变革[J]. 中国社会科学, 2026(6): 44-57.","lenZh":204,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026006004","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"大模型推动生产方式变革的唯物史观透视","summary":"大模型不仅是人类一般智力和科学知识对象化的产物，也是实现人类一般智力和整体知识统合的重要方法，将给人类知识生产方式带来深刻变革，推动知识生产范式从主体生产到人机协同、从强调相关关系到强化因果关系、从小学科到跨学科融合创新范式的转变。此外，大模型的具身化不仅会推动生产资料的智能革命，也会实现社会生产力的历史跃变，不仅会重塑人与自然之间的内生关系，更有可能实现数据向现实物的智能生成，进而从根本上改变物质生产的运行机制、一般图式和组织形式。厘清这些问题，对于深刻把握大模型背景下唯物史观的出场逻辑及其具体化形态具有重要意义。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026006004","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"6","authors":["孙乐强"],"affils":["南京大学哲学学院"],"title":"大模型推动生产方式变革的唯物史观透视","titleEn":"A Perspective of the Materialist Conception of History on the Transformations of Modes of Production Driven by Large Models","keywords":["大模型","生产方式","一般智力","具身智能"],"keywordsEn":["Large Models","Production Methods","General Intelligence","Embodied Intelligence"],"abstract":"大模型不仅是人类一般智力和科学知识对象化的产物，也是实现人类一般智力和整体知识统合的重要方法，将给人类知识生产方式带来深刻变革，推动知识生产范式从主体生产到人机协同、从强调相关关系到强化因果关系、从小学科到跨学科融合创新范式的转变。此外，大模型的具身化不仅会推动生产资料的智能革命，也会实现社会生产力的历史跃变，不仅会重塑人与自然之间的内生关系，更有可能实现数据向现实物的智能生成，进而从根本上改变物质生产的运行机制、一般图式和组织形式。厘清这些问题，对于深刻把握大模型背景下唯物史观的出场逻辑及其具体化形态具有重要意义。","abstractEn":"","pages":"58-70","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=U0h3TXJDdWJrY3NrbDVqcmViMHg0aENLVE9MVk5Fb3hHU04zZWlINE1yZ2QxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"孙乐强. 大模型推动生产方式变革的唯物史观透视[J]. 中国社会科学, 2026(6): 58-70.","lenZh":261,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026006005","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"资金供给的国民收入分配效应","summary":"将资金供给因素纳入国民收入分配问题分析具有重要的理论价值和现实意义。将金融交易的《资金流量表》和实物交易的《资金流量表》对接，编制体现部门间和上下游资金交易关系的“部门—部门”《资金流量表》，测算部门间资金流向及部门内交易项目资金流向发现：2009年后，增量资金主要进入实体经济部门和实物交易环节；在资金约束缓解后，实体经济部门和实物交易环节优先弥补劳动要素投入缺口，显著推动了就业扩容，大幅提高了劳动报酬份额和居民收入份额。这表明，资金供给具有显著的国民收入分配效应。资金供给的国民收入分配效应表明，需要从源头上强化资金需求结构识别和供给管道筹划，依托结构性货币政策工具，持续引导“资金向实”，防止资本劳动互补关系弱化和“高生产率低工资陷阱”等问题。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026006005","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"6","authors":["赵文","刘英俊"],"affils":["中国社会科学院人口与劳动经济研究所","福建农林大学经济与管理学院"],"title":"资金供给的国民收入分配效应","titleEn":"The National Income Distribution Effect of Funds Supply","keywords":["劳动报酬份额","居民收入份额","资金流量表","国民收入分配"],"keywordsEn":["Labor compensation share","Household income share","Flow of funds accounts","National income distribution"],"abstract":"将资金供给因素纳入国民收入分配问题分析具有重要的理论价值和现实意义。将金融交易的《资金流量表》和实物交易的《资金流量表》对接，编制体现部门间和上下游资金交易关系的“部门—部门”《资金流量表》，测算部门间资金流向及部门内交易项目资金流向发现：2009年后，增量资金主要进入实体经济部门和实物交易环节；在资金约束缓解后，实体经济部门和实物交易环节优先弥补劳动要素投入缺口，显著推动了就业扩容，大幅提高了劳动报酬份额和居民收入份额。这表明，资金供给具有显著的国民收入分配效应。资金供给的国民收入分配效应表明，需要从源头上强化资金需求结构识别和供给管道筹划，依托结构性货币政策工具，持续引导“资金向实”，防止资本劳动互补关系弱化和“高生产率低工资陷阱”等问题。","abstractEn":"","pages":"71-93","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=U0h3TXJDdWJrY3NrbDVqcmViMHg0cHdtN0VqVmRvTFI0Ukh6UHBIWWxVZ2QxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"赵文, 刘英俊. 资金供给的国民收入分配效应[J]. 中国社会科学, 2026(6): 71-93.","lenZh":326,"lenEn":0}},{"id":"archive/cssci-100:ZGSHKX2026006006","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"风险类型与国际公共产品供给模式——全球人工智能安全治理的分析框架","summary":"以风险为对立物的安全治理是全球治理中不容忽视的重要议题。面对人工智能风险的加速累积，尽管各类主体竞相参与治理，风险治理需求与国际公共产品供给之间的失衡与错配仍构成当前全球人工智能安全治理的主要矛盾。因此，有必要立足“力求实效”“标本兼治”的全球治理理念，聚焦风险属性导致的治理需求差异，构建基于风险类型的国际公共产品最优供给模式的理论框架。在此基础上，可对全球人工智能风险进行分类分级，进而构建多层次的全球人工智能安全治理架构，通过动态、敏捷调整国际公共产品供给方式，使全球治理体系最大限度匹配人工智能风险的动态演进。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"ZGSHKX2026006006","journal":"中国社会科学","journalEn":"Social Sciences in China","year":"2026","issue":"6","authors":["王净宇","梁正"],"affils":["清华大学人工智能国际治理研究院","清华大学公共管理学院"],"title":"风险类型与国际公共产品供给模式——全球人工智能安全治理的分析框架","titleEn":"Risk Types and Modes of International Public Goods Provision——An Analytical Framework for Global AI Safety Governance","keywords":["全球风险","全球治理","国际公共产品","人工智能安全治理"],"keywordsEn":["Global risks","Global governance","International public goods","AI safety governance"],"abstract":"以风险为对立物的安全治理是全球治理中不容忽视的重要议题。面对人工智能风险的加速累积，尽管各类主体竞相参与治理，风险治理需求与国际公共产品供给之间的失衡与错配仍构成当前全球人工智能安全治理的主要矛盾。因此，有必要立足“力求实效”“标本兼治”的全球治理理念，聚焦风险属性导致的治理需求差异，构建基于风险类型的国际公共产品最优供给模式的理论框架。在此基础上，可对全球人工智能风险进行分类分级，进而构建多层次的全球人工智能安全治理架构，通过动态、敏捷调整国际公共产品供给方式，使全球治理体系最大限度匹配人工智能风险的动态演进。","abstractEn":"","pages":"94-113","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=U0h3TXJDdWJrY3NrbDVqcmViMHg0aFdqVEpOWkFnYmFCMDVodk84U2FlSWQxZG5xK1FlRklLNnJGNnNmcTgycjVIS1F4NUpQSUxZNTdWNWVlWVVWVlpNeHZ1M2Z4dmpFbUxtYjBFVGJaZTJrMWg2M3lBb1lIeUlmRWtSaURsL2M","cite":"王净宇, 梁正. 风险类型与国际公共产品供给模式——全球人工智能安全治理的分析框架[J]. 中国社会科学, 2026(6): 94-113.","lenZh":259,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026008001","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"价格战监管与平台创新：资金约束下的平台动态竞争","summary":"平台经济在成为我国创新发展新引擎的同时，也带来了赢者通吃、掠夺性定价等挑战。本文通过构建两期动态博弈模型，研究资金约束下潜在进入平台的创新水平决策与竞争策略选择（质量战或价格战）的权衡机制，并讨论了针对在位平台补贴性定价行为的管制政策。研究表明，在一定条件下，规制在位平台的补贴性定价行为会产生加速创新与抑制创新的双重效应：一方面，可以增加受到资金约束的创新型平台进入市场的可能性；另一方面，也可能促使能够进入市场的平台做出更低产品质量的创新决策。具体而言，当存在创新风险且创新效率适中时，放松对在位平台的补贴管制会通过“鲶鱼效应”激励潜在进入平台选择更高创新水平；反之在低风险或高创新效率市场，管制政策能通过缓解“挤出效应”促进更多高质量平台进入市场。本文研究的政策启示在于，规制平台价格战行为需结合市场结构、企业创新能力及行业特征，采取差异化审慎政策。同时，本文对市场结构与创新激励这一经典研究议题（即“熊彼特-阿罗之争”）进行了理论拓展，并为制定产业创新激励政策与反垄断政策提供了新的视角。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026008001","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"8","authors":["李三希","黄靖旻","马梦阳"],"affils":["中国人民大学经济学院","中国人民大学数字经济研究中心","美国耶鲁大学管理学院"],"title":"价格战监管与平台创新：资金约束下的平台动态竞争","titleEn":"Regulation of Platform Price Wars and Platform Innovation: Dynamic Platform Competition under Financial Constraints","keywords":["平台经济","资金约束","产业创新","补贴性定价","反垄断规制"],"keywordsEn":[],"abstract":"平台经济在成为我国创新发展新引擎的同时，也带来了赢者通吃、掠夺性定价等挑战。本文通过构建两期动态博弈模型，研究资金约束下潜在进入平台的创新水平决策与竞争策略选择（质量战或价格战）的权衡机制，并讨论了针对在位平台补贴性定价行为的管制政策。研究表明，在一定条件下，规制在位平台的补贴性定价行为会产生加速创新与抑制创新的双重效应：一方面，可以增加受到资金约束的创新型平台进入市场的可能性；另一方面，也可能促使能够进入市场的平台做出更低产品质量的创新决策。具体而言，当存在创新风险且创新效率适中时，放松对在位平台的补贴管制会通过“鲶鱼效应”激励潜在进入平台选择更高创新水平；反之在低风险或高创新效率市场，管制政策能通过缓解“挤出效应”促进更多高质量平台进入市场。本文研究的政策启示在于，规制平台价格战行为需结合市场结构、企业创新能力及行业特征，采取差异化审慎政策。同时，本文对市场结构与创新激励这一经典研究议题（即“熊彼特-阿罗之争”）进行了理论拓展，并为制定产业创新激励政策与反垄断政策提供了新的视角。","abstractEn":"","pages":"1-18","doi":"10.19744/j.cnki.11-1235/f.2026.0108","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=cUlVVHFaeHp5YUNJTHM3eUZXeFFSRVBNRkNROURIQ0pCWXRlRmVtZFl5RnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"李三希, 黄靖旻, 马梦阳. 价格战监管与平台创新：资金约束下的平台动态竞争[J]. 管理世界, 2026(8): 1-18.","lenZh":448,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026008006","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"“直过民族”旅游脱贫村的内生发展动力机制研究","summary":"“直过民族”旅游脱贫村的全面振兴亟待从“外部输血”走向“内生造血”。本研究依托行动者网络理论和人地关系地域系统理论，采用多案例研究方法，以云南省4个不同模式的“直过民族”旅游脱贫村为研究对象，构建“情境特质—行动逻辑—动力机制”分析框架，系统揭示内生发展动力的生成机制及类型差异。研究发现：（1）“直过民族”旅游脱贫村的内生发展动力生成具有显著的情境依赖性，其初始条件取决于“关键行动者—核心资源潜力—外部机遇窗口”的特定组合；（2）人类行动者的多重制度逻辑与非人类行动者的核心资源转化逻辑是村寨把外部机遇转化为内生发展动力的关键“中介过程”；（3）内生发展动力机制体现为激励机制、整合机制、转化机制、调节机制、规范机制的联动演进，并最终涌现出制度规训下的制度化保育型、契约博弈中的市场竞合型、情感认同凝聚的文化驱动型、网络化学习共生的科学共治型4类“人—地”可持续动能系统。据此，本文提出了重构治理理念、优化政策供给逻辑、强化过程治理、夯实“人—地”互馈等政策建议。本文拓展了以往乡村内生发展动力机制研究的理论视野，也为因地制宜推动中国民族地区的旅游高质量发展、乡村振兴和共同富裕提供决策参考。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026008006","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"8","authors":["李佳","和智璇","张敬香"],"affils":["云南大学工商管理与旅游管理学院"],"title":"“直过民族”旅游脱贫村的内生发展动力机制研究","titleEn":"Mechanisms of Endogenous Development Dynamics in Tourism-Led Poverty Alleviation Villages among China's Direct-Transition Ethnic Groups","keywords":["“直过民族”","旅游脱贫村","内生发展动力机制","行动者网络理论","人地关系地域系统"],"keywordsEn":[],"abstract":"“直过民族”旅游脱贫村的全面振兴亟待从“外部输血”走向“内生造血”。本研究依托行动者网络理论和人地关系地域系统理论，采用多案例研究方法，以云南省4个不同模式的“直过民族”旅游脱贫村为研究对象，构建“情境特质—行动逻辑—动力机制”分析框架，系统揭示内生发展动力的生成机制及类型差异。研究发现：（1）“直过民族”旅游脱贫村的内生发展动力生成具有显著的情境依赖性，其初始条件取决于“关键行动者—核心资源潜力—外部机遇窗口”的特定组合；（2）人类行动者的多重制度逻辑与非人类行动者的核心资源转化逻辑是村寨把外部机遇转化为内生发展动力的关键“中介过程”；（3）内生发展动力机制体现为激励机制、整合机制、转化机制、调节机制、规范机制的联动演进，并最终涌现出制度规训下的制度化保育型、契约博弈中的市场竞合型、情感认同凝聚的文化驱动型、网络化学习共生的科学共治型4类“人—地”可持续动能系统。据此，本文提出了重构治理理念、优化政策供给逻辑、强化过程治理、夯实“人—地”互馈等政策建议。本文拓展了以往乡村内生发展动力机制研究的理论视野，也为因地制宜推动中国民族地区的旅游高质量发展、乡村振兴和共同富裕提供决策参考。","abstractEn":"","pages":"100-122","doi":"10.19744/j.cnki.11-1235/f.2026.0107","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=cUlVVHFaeHp5YUNJTHM3eUZXeFFSTXZKd0FXd0RmeW84MzV2VlQrcTFqOXZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"李佳, 和智璇, 张敬香. “直过民族”旅游脱贫村的内生发展动力机制研究[J]. 管理世界, 2026(8): 100-122.","lenZh":498,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026008007","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"欠发达地区乡村治理现代化中的个体道德义务制度化研究——基于云南S村寨人居环境整治的案例分析","summary":"中央一号文件连续多年强调发挥农民主体作用。如何增强欠发达地区农民内生发展动力已成为乡村治理现代化的关键难题。现有文献多从组织化动员、项目制供给或制度激励等方面解释乡村治理机制，较少研究个体道德义务何以转化为制度化的集体行动，对中华优秀传统文化激发乡村治理新动能的关注不足。本文基于云南S村寨人居环境整治案例，结合跨区域“影子案例”的镜像检验，提出并初步检验了个体道德义务制度化的过程机制及其边界条件，获得以下三方面研究发现。第一，中华优秀传统文化蕴含丰富的治理智慧，将个体道德义务进行制度转化是欠发达地区实现乡村治理现代化的可行路径之一，甚至可以成为流动性社会下重构乡村治理秩序的重要机制。第二，个体道德义务的制度化历经“价值—行动—制度”的链式发展过程，由“共同信念的外化—道德义务的客观化—责任规范的内化”机制构成；制度化并非强制推动，而是依赖党建引领、政社协同和社会共生的载体得以生成。第三，道德义务的制度化依赖特定边界条件，当共同体信念不易激活、贡献难以可见、党组织引领缺位或政府代替农民时，个体道德义务难以转化为集体行动；即便短期动员成功，也将在重复博弈阶段因缺乏监督反馈而失效。本研究通过主案例深描与“影子案例”差别对照，提供了一种兼顾厚度与外推限度的研究路径，不仅为中华优秀传统文化创造性转化提供有力理论支撑，也是在构建中国管理学自主知识体系下彰显中国特色乡村治理智慧的生动实践。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026008007","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"8","authors":["杜焱强","张茜茜"],"affils":["南京农业大学公共管理学院"],"title":"欠发达地区乡村治理现代化中的个体道德义务制度化研究——基于云南S村寨人居环境整治的案例分析","titleEn":"The Institutionalization of Individual Moral Obligations in the Modernization of Rural Governance in Under-developed Areas: A Case Study of Rural Living Environment Improvement in Village S, Yunnan Province","keywords":["乡村全面振兴","中华优秀传统文化","社会治理共同体","内生动力","集体行动"],"keywordsEn":[],"abstract":"中央一号文件连续多年强调发挥农民主体作用。如何增强欠发达地区农民内生发展动力已成为乡村治理现代化的关键难题。现有文献多从组织化动员、项目制供给或制度激励等方面解释乡村治理机制，较少研究个体道德义务何以转化为制度化的集体行动，对中华优秀传统文化激发乡村治理新动能的关注不足。本文基于云南S村寨人居环境整治案例，结合跨区域“影子案例”的镜像检验，提出并初步检验了个体道德义务制度化的过程机制及其边界条件，获得以下三方面研究发现。第一，中华优秀传统文化蕴含丰富的治理智慧，将个体道德义务进行制度转化是欠发达地区实现乡村治理现代化的可行路径之一，甚至可以成为流动性社会下重构乡村治理秩序的重要机制。第二，个体道德义务的制度化历经“价值—行动—制度”的链式发展过程，由“共同信念的外化—道德义务的客观化—责任规范的内化”机制构成；制度化并非强制推动，而是依赖党建引领、政社协同和社会共生的载体得以生成。第三，道德义务的制度化依赖特定边界条件，当共同体信念不易激活、贡献难以可见、党组织引领缺位或政府代替农民时，个体道德义务难以转化为集体行动；即便短期动员成功，也将在重复博弈阶段因缺乏监督反馈而失效。本研究通过主案例深描与“影子案例”差别对照，提供了一种兼顾厚度与外推限度的研究路径，不仅为中华优秀传统文化创造性转化提供有力理论支撑，也是在构建中国管理学自主知识体系下彰显中国特色乡村治理智慧的生动实践。","abstractEn":"","pages":"123-144","doi":"10.19744/j.cnki.11-1235/f.2026.0106","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=cUlVVHFaeHp5YUNJTHM3eUZXeFFSQkRvemdYOGZnbnRGSlJHU01nTDkxMXZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"杜焱强, 张茜茜. 欠发达地区乡村治理现代化中的个体道德义务制度化研究——基于云南S村寨人居环境整治的案例分析[J]. 管理世界, 2026(8): 123-144.","lenZh":597,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026008008","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"社保缴费基数限额下调的稳就业与促投资效应研究——来自社平工资统计口径调整的证据","summary":"完善要素价格机制对于充分释放市场潜力具有重要价值。社保缴费基数限额大幅提高了企业雇佣低工资职工的成本，对劳动力价格造成扭曲。2019年中央政府要求采用全口径职工平均工资确定社保缴费基数限额，部分地区基数限额因此下调。本文利用这一外生冲击，研究发现该政策使人均劳动力成本下降2.38%，企业雇佣职工数量因此增加3.86%，对应劳动力需求弹性为1.62。降低劳动力成本和缓解融资约束是基数限额下调能起到稳就业效果的重要机制，且稳就业效应在工资低、社保缴费遵从度高和小企业组别中更大。基数限额下调后，企业投资概率和投资强度都会显著上升。无论是促投资还是稳就业，这都是由下调缴费基数下限驱动。本文还发现，基数限额下调不仅不会使社保基金的收入下降，还能提高政府财政补贴社保基金的能力。上述结论表明，对社保缴费基数限额进行改革，不仅事关完善社保制度，也有助于优化劳动力要素价格机制，从而释放市场潜力。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026008008","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"8","authors":["唐珏","翁晓玉","黄永颖","谢强"],"affils":["上海财经大学公共管理学院","上海财经大学中国式现代化研究院","武汉大学经济与管理学院","浙江工商大学经济学院"],"title":"社保缴费基数限额下调的稳就业与促投资效应研究——来自社平工资统计口径调整的证据","titleEn":"The Effects of Lowering the Social Insurance Contribution Base Limits on Employment Stabilization and Investment Promotion: Evidence from the Adjustment of Average Wage Statistical Standards","keywords":["社会保险","缴费基数","就业","投资"],"keywordsEn":[],"abstract":"完善要素价格机制对于充分释放市场潜力具有重要价值。社保缴费基数限额大幅提高了企业雇佣低工资职工的成本，对劳动力价格造成扭曲。2019年中央政府要求采用全口径职工平均工资确定社保缴费基数限额，部分地区基数限额因此下调。本文利用这一外生冲击，研究发现该政策使人均劳动力成本下降2.38%，企业雇佣职工数量因此增加3.86%，对应劳动力需求弹性为1.62。降低劳动力成本和缓解融资约束是基数限额下调能起到稳就业效果的重要机制，且稳就业效应在工资低、社保缴费遵从度高和小企业组别中更大。基数限额下调后，企业投资概率和投资强度都会显著上升。无论是促投资还是稳就业，这都是由下调缴费基数下限驱动。本文还发现，基数限额下调不仅不会使社保基金的收入下降，还能提高政府财政补贴社保基金的能力。上述结论表明，对社保缴费基数限额进行改革，不仅事关完善社保制度，也有助于优化劳动力要素价格机制，从而释放市场潜力。","abstractEn":"","pages":"145-161","doi":"10.19744/j.cnki.11-1235/f.2026.0111","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=cUlVVHFaeHp5YUNJTHM3eUZXeFFSTEdrV3ltdHRVUWJnNEpnTWZiWFJTSnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"唐珏, 翁晓玉, 黄永颖, 谢强. 社保缴费基数限额下调的稳就业与促投资效应研究——来自社平工资统计口径调整的证据[J]. 管理世界, 2026(8): 145-161.","lenZh":394,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026008009","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"养老保险缴费率下降的工资弹性之谜——兼论积极财政政策精准发力的路径","summary":"财政政策如何提质增效以更好地推动经济稳定增长是今后一段时期我国财政工作的重点。费尔德斯坦（1999）以来的经典研究指出收入弹性是政策评估的关键统计量。现有研究普遍发现我国社保降费的工资收入弹性显著大于1，不仅大于国际主流文献的发现，也不能被经典的社保缴费归宿理论所解释。本文通过理论分析和基于我国2019年大规模养老保险缴费率下调的实证研究对我国工资收入弹性之谜进行了回答，弥补了现有研究的不足。理论上，本文通过在经典模型中引入内生的融资约束，解释了工资收入弹性大于1的现象，并指出融资约束与收入弹性之间可能存在倒U形关系。本文利用手工收集的2016～2023年我国各城市法定缴费率和微观企业数据，并通过构建双重差分模型在实证层面对理论分析的假说进行了验证，发现养老保险缴费率每降低1个百分点，劳动力工资收入增加2.18%，融资约束的差异很好地解释了企业间的弹性差异。此外，本文发现融资约束还显著放大了缴费率下降对劳动力雇佣和投资的激励效应。养老保险缴费率每降低1个百分点，企业劳动力雇佣和投资规模分别扩大2.47%和7.04%。本文解释了我国社保降费的工资弹性之谜，拓展了社保缴费归宿理论与实证研究，并指出有针对性地降低企业融资约束是实现财政政策提质增效的重要途径。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026008009","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"8","authors":["何凡","李文健","张熠"],"affils":["江西财经大学财政税务学院","江西财经大学财经数据科学教育部哲学社会科学实验室(培育)","浙江大学经济学院","上海财经大学公共管理学院"],"title":"养老保险缴费率下降的工资弹性之谜——兼论积极财政政策精准发力的路径","titleEn":"The Wage Elasticity Puzzle of Pension Contribution Rate Cuts: Implications for the Targeted Implementation of Proactive Fiscal Policy","keywords":["养老保险缴费率","劳动力工资","融资约束","积极财政政策"],"keywordsEn":[],"abstract":"财政政策如何提质增效以更好地推动经济稳定增长是今后一段时期我国财政工作的重点。费尔德斯坦（1999）以来的经典研究指出收入弹性是政策评估的关键统计量。现有研究普遍发现我国社保降费的工资收入弹性显著大于1，不仅大于国际主流文献的发现，也不能被经典的社保缴费归宿理论所解释。本文通过理论分析和基于我国2019年大规模养老保险缴费率下调的实证研究对我国工资收入弹性之谜进行了回答，弥补了现有研究的不足。理论上，本文通过在经典模型中引入内生的融资约束，解释了工资收入弹性大于1的现象，并指出融资约束与收入弹性之间可能存在倒U形关系。本文利用手工收集的2016～2023年我国各城市法定缴费率和微观企业数据，并通过构建双重差分模型在实证层面对理论分析的假说进行了验证，发现养老保险缴费率每降低1个百分点，劳动力工资收入增加2.18%，融资约束的差异很好地解释了企业间的弹性差异。此外，本文发现融资约束还显著放大了缴费率下降对劳动力雇佣和投资的激励效应。养老保险缴费率每降低1个百分点，企业劳动力雇佣和投资规模分别扩大2.47%和7.04%。本文解释了我国社保降费的工资弹性之谜，拓展了社保缴费归宿理论与实证研究，并指出有针对性地降低企业融资约束是实现财政政策提质增效的重要途径。","abstractEn":"","pages":"162-181","doi":"10.19744/j.cnki.11-1235/f.2026.0114","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=cUlVVHFaeHp5YUNJTHM3eUZXeFFSQThaWkZWMFZOYlU4WVZxcEc3MUVZdHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"何凡, 李文健, 张熠. 养老保险缴费率下降的工资弹性之谜——兼论积极财政政策精准发力的路径[J]. 管理世界, 2026(8): 162-181.","lenZh":534,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026008010","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"专精特新企业如何破解双重“低端锁定”循环？","summary":"关键核心技术体系培育既是专精特新企业突破其微观成长“低端锁定”的必由之路，亦是撬动我国从全球价值链的“低端锁定”走向自主攀升的战略支点。然而，既有研究未能就专精特新企业双重“低端锁定”循环这一现实困境及其突围路径提供理论解答。基于纵向案例研究，构建了专精特新企业培育关键核心技术体系、破解双重“低端锁定”循环的理论模型。研究发现：（1）专精特新企业培育关键核心技术体系的机会窗口呈现出“技术缺口撬动需求研判—需求升级技术滞后—技术需求交融互耦”的演化逻辑；（2）受机会窗口的触发，企业运用“技术—市场”双轮螺旋驱动关键核心技术体系培育，技术端通过技术架构自主定义、技术模块协同联动和技术平台生态衍生，市场端遵循市场需求梯度渗透、市场场景弹性迁移和市场生态细分锚定的逻辑，突破双重“低端锁定”循环；（3）技术立身的响应式生存动力、网络嵌入的互动式成长动力和价值赋能的内生式创新动力提供阶段性动力支持。本研究提出的关键核心技术体系培育路径，不仅为我国突破在全球价值链“低端锁定”的困局提供实践路径，也为专精特新企业破解双重“低端锁定”循环，实现从“链式补点”的从属地位向“体系培育”的主动战略地位跃迁提供理论依据与实践指引。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026008010","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"8","authors":["邵云飞","赵林汐","吴晓波","邬爱其"],"affils":["电子科技大学经济与管理学院","浙江大学管理学院"],"title":"专精特新企业如何破解双重“低端锁定”循环？","titleEn":"How Can SRDI Enterprises Break the Cycle of Dual \"Low-end Lock-in\"?","keywords":["双重“低端锁定”循环","关键核心技术体系","专精特新企业","“技术—市场”双轮螺旋驱动"],"keywordsEn":[],"abstract":"关键核心技术体系培育既是专精特新企业突破其微观成长“低端锁定”的必由之路，亦是撬动我国从全球价值链的“低端锁定”走向自主攀升的战略支点。然而，既有研究未能就专精特新企业双重“低端锁定”循环这一现实困境及其突围路径提供理论解答。基于纵向案例研究，构建了专精特新企业培育关键核心技术体系、破解双重“低端锁定”循环的理论模型。研究发现：（1）专精特新企业培育关键核心技术体系的机会窗口呈现出“技术缺口撬动需求研判—需求升级技术滞后—技术需求交融互耦”的演化逻辑；（2）受机会窗口的触发，企业运用“技术—市场”双轮螺旋驱动关键核心技术体系培育，技术端通过技术架构自主定义、技术模块协同联动和技术平台生态衍生，市场端遵循市场需求梯度渗透、市场场景弹性迁移和市场生态细分锚定的逻辑，突破双重“低端锁定”循环；（3）技术立身的响应式生存动力、网络嵌入的互动式成长动力和价值赋能的内生式创新动力提供阶段性动力支持。本研究提出的关键核心技术体系培育路径，不仅为我国突破在全球价值链“低端锁定”的困局提供实践路径，也为专精特新企业破解双重“低端锁定”循环，实现从“链式补点”的从属地位向“体系培育”的主动战略地位跃迁提供理论依据与实践指引。","abstractEn":"","pages":"182-203","doi":"10.19744/j.cnki.11-1235/f.2026.0112","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=NStZdEtrN25aL3NaOWRBL1dac2x1TFNZN052dWJhTGZGYTVMUThPZ3FuNXZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"邵云飞, 赵林汐, 吴晓波, 邬爱其. 专精特新企业如何破解双重“低端锁定”循环？[J]. 管理世界, 2026(8): 182-203.","lenZh":510,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026008002","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"结构转型中居民消费的广延边际和集约边际研究——基于市场可达的需求规模视角","summary":"把握结构转型中居民消费增长的驱动机制，对中国超大规模市场持续扩容提质、全方位扩大国内需求具有重要意义。本文构建家庭消费需求的一般均衡分析框架，利用城镇住户、超市零售价格及企业税收等调查数据，深入研究市场可达的需求规模对居民消费增长的广延边际（消费类型的数量）和集约边际（消费类型的品质）的影响效应。结果显示：市场可达性每提升1%，总消费支出增长1.239%；其中，广延边际的贡献率为22.5%，细分来看，食品消费、耐用品消费、服务消费的贡献率分别为18.4%、21.8%和45.6%；集约边际中，食品、耐用品的贡献率分别达23.4%和13.2%。机制分析显示，市场可达性通过降低交易成本强化供应链网络，不仅推动了社会消费品零售总额的增长，还减少了家庭购物的时间交易成本；需求规模扩大促使企业增加投资，推动全要素生产率、营收利润及职工平均工资的提升。从收入阈值来看，低收入家庭主要体现在食品消费的广延边际增长，中高收入家庭主要体现以质量提升为核心的集约边际增长；从消费饱和点来看，当前我国家庭人均服务消费远低于33000元/人的饱和点，服务消费的广延边际和集约边际增长均具有巨大潜能。本研究结论可为“十五五”时期政府精准推动居民消费的扩容提质提供有益参考。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026008002","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"8","authors":["宋泽","喻曦","邹红","何阳"],"affils":["南开大学经济学院","西南财经大学经济学院","中国社会科学院人口与劳动经济研究所"],"title":"结构转型中居民消费的广延边际和集约边际研究——基于市场可达的需求规模视角","titleEn":"The Extensive and Intensive Margins of Household Consumption in Structural Transition: From the Perspective of Demand Scale Driven by Market Accessibility","keywords":["居民消费","广延边际","集约边际","市场可达性"],"keywordsEn":[],"abstract":"把握结构转型中居民消费增长的驱动机制，对中国超大规模市场持续扩容提质、全方位扩大国内需求具有重要意义。本文构建家庭消费需求的一般均衡分析框架，利用城镇住户、超市零售价格及企业税收等调查数据，深入研究市场可达的需求规模对居民消费增长的广延边际（消费类型的数量）和集约边际（消费类型的品质）的影响效应。结果显示：市场可达性每提升1%，总消费支出增长1.239%；其中，广延边际的贡献率为22.5%，细分来看，食品消费、耐用品消费、服务消费的贡献率分别为18.4%、21.8%和45.6%；集约边际中，食品、耐用品的贡献率分别达23.4%和13.2%。机制分析显示，市场可达性通过降低交易成本强化供应链网络，不仅推动了社会消费品零售总额的增长，还减少了家庭购物的时间交易成本；需求规模扩大促使企业增加投资，推动全要素生产率、营收利润及职工平均工资的提升。从收入阈值来看，低收入家庭主要体现在食品消费的广延边际增长，中高收入家庭主要体现以质量提升为核心的集约边际增长；从消费饱和点来看，当前我国家庭人均服务消费远低于33000元/人的饱和点，服务消费的广延边际和集约边际增长均具有巨大潜能。本研究结论可为“十五五”时期政府精准推动居民消费的扩容提质提供有益参考。","abstractEn":"","pages":"19-36","doi":"10.19744/j.cnki.11-1235/f.2026.0109","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=cUlVVHFaeHp5YUNJTHM3eUZXeFFSTnk2SFIzZG02UHA1V0kxa09OclJadHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"宋泽, 喻曦, 邹红, 何阳. 结构转型中居民消费的广延边际和集约边际研究——基于市场可达的需求规模视角[J]. 管理世界, 2026(8): 19-36.","lenZh":527,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026008011","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"后发企业技术追赶的竞合效应：不对称防御视角的案例研究","summary":"后发企业在嵌入跨国公司主导的技术体系时，同时面临自身的创新价值获取被破坏的风险。有效防御是弱势者应对这一竞合困境的关键战略，但已有研究较少探讨如何利用后发优势实施不对称防御。本文对纳思达公司开展探索性单案例研究，基于其从耗材代工生产商到打印机产业跨国公司的发展历程，探讨不对称防御行为的实施过程及其潜在的价值动机与驱动机制。研究发现：第一，后发企业可以通过逆势型线性防御、借势型阵地防御与造势型动态防御的防御行为，分别与二次创新、自主创新与突破式创新的创新模式组合，以应对准入压力、低端锁定压力与生态位压力的阶段性竞合失衡风险；第二，后发企业在二元与多方关系层面中均实施了独占性防御，其不对称特征显著体现为专利防御的“先守后攻”与时机防御的“借力”；第三，防御行为的价值动机从较为割裂的二元观逐渐向内在依赖的全局观过渡，而由权力地位与竞合张力组成的竞合感知兼具“形势判断”，与价值动机共同构成对防御行为的循环驱动。本文细化了中国情境中典型的技术追赶路径，并丰富了技术追赶的实现条件研究，打开了应对竞合困境的“行为—机制”黑箱，为后发企业提升技术能力的同时培养竞合思维和竞合能力提供理论指导与实践启示。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026008011","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"8","authors":["谭畅","刘涵秋","刘林青","谢洪明"],"affils":["西南大学经济管理学院","武汉大学经济与管理学院","四川大学商学院"],"title":"后发企业技术追赶的竞合效应：不对称防御视角的案例研究","titleEn":"Coopetition Effects in Latecomer Firms' Technological Catch-up: A Case Study from the Perspective of Asymmetric Defense","keywords":["技术追赶","竞合关系","防御行为","驱动机制","案例研究"],"keywordsEn":[],"abstract":"后发企业在嵌入跨国公司主导的技术体系时，同时面临自身的创新价值获取被破坏的风险。有效防御是弱势者应对这一竞合困境的关键战略，但已有研究较少探讨如何利用后发优势实施不对称防御。本文对纳思达公司开展探索性单案例研究，基于其从耗材代工生产商到打印机产业跨国公司的发展历程，探讨不对称防御行为的实施过程及其潜在的价值动机与驱动机制。研究发现：第一，后发企业可以通过逆势型线性防御、借势型阵地防御与造势型动态防御的防御行为，分别与二次创新、自主创新与突破式创新的创新模式组合，以应对准入压力、低端锁定压力与生态位压力的阶段性竞合失衡风险；第二，后发企业在二元与多方关系层面中均实施了独占性防御，其不对称特征显著体现为专利防御的“先守后攻”与时机防御的“借力”；第三，防御行为的价值动机从较为割裂的二元观逐渐向内在依赖的全局观过渡，而由权力地位与竞合张力组成的竞合感知兼具“形势判断”，与价值动机共同构成对防御行为的循环驱动。本文细化了中国情境中典型的技术追赶路径，并丰富了技术追赶的实现条件研究，打开了应对竞合困境的“行为—机制”黑箱，为后发企业提升技术能力的同时培养竞合思维和竞合能力提供理论指导与实践启示。","abstractEn":"","pages":"204-222","doi":"10.19744/j.cnki.11-1235/f.2026.0110","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=NStZdEtrN25aL3NaOWRBL1dac2x1RVBNRkNROURIQ0pCWXRlRmVtZFl5RnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"谭畅, 刘涵秋, 刘林青, 谢洪明. 后发企业技术追赶的竞合效应：不对称防御视角的案例研究[J]. 管理世界, 2026(8): 204-222.","lenZh":500,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026008012","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"从核准制到注册制：供给冲击、定价扭曲与渐进式改革策略","summary":"股票发行注册制改革作为一项资本市场的关键制度变迁，其改革方向虽清晰在前，但囿于市场承受能力与风险防控约束，该当如何破局、择路径以达目标，亟待研究。鉴于此，本文拓展了异质性投资者模型，通过引入股票供给端因素，构建了一个双异质性动态模型以刻画股票发行制度从核准制到注册制的制度变迁。研究发现，在核准制下，股票发行的供给干预会导致绩优股与绩差股的均衡价格发生错位；倘若直接全面放开股票供给限制至注册制，会造成系统性的价格下降。进一步地，分类放松供给管制使各类股票均衡价格呈现异质性的变化趋势，这为监管者兼顾定价效率的改善与系统性风险的规避提供了施政空间。进而，本文在定价效率与系统性风险双重约束下，探讨了从核准制向注册制过渡的最小阻力路径，并据此提出高门槛注册制这一可供参考的渐进式改革策略，以规避过渡期可能出现的系统性风险。本文的研究不仅拓展了异质性投资者理论模型，还为中国股票发行制度的市场化改革提供了一个理论解释框架和备选政策思路，并表明在本文模型设定及约束条件下，渐进式改革具有局部最优性。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026008012","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"8","authors":["汲铮","华生","傅捷","张晓奇"],"affils":["东南大学经济管理学院","东南大学国家发展与政策研究院","天津大学复杂管理系统实验室"],"title":"从核准制到注册制：供给冲击、定价扭曲与渐进式改革策略","titleEn":"From the Approval-Based System to the Registration-Based System: Supply Shocks, Pricing Distortions, and Gradual Reform Strategies","keywords":["核准制","注册制","渐进式改革","异质性投资者模型"],"keywordsEn":[],"abstract":"股票发行注册制改革作为一项资本市场的关键制度变迁，其改革方向虽清晰在前，但囿于市场承受能力与风险防控约束，该当如何破局、择路径以达目标，亟待研究。鉴于此，本文拓展了异质性投资者模型，通过引入股票供给端因素，构建了一个双异质性动态模型以刻画股票发行制度从核准制到注册制的制度变迁。研究发现，在核准制下，股票发行的供给干预会导致绩优股与绩差股的均衡价格发生错位；倘若直接全面放开股票供给限制至注册制，会造成系统性的价格下降。进一步地，分类放松供给管制使各类股票均衡价格呈现异质性的变化趋势，这为监管者兼顾定价效率的改善与系统性风险的规避提供了施政空间。进而，本文在定价效率与系统性风险双重约束下，探讨了从核准制向注册制过渡的最小阻力路径，并据此提出高门槛注册制这一可供参考的渐进式改革策略，以规避过渡期可能出现的系统性风险。本文的研究不仅拓展了异质性投资者理论模型，还为中国股票发行制度的市场化改革提供了一个理论解释框架和备选政策思路，并表明在本文模型设定及约束条件下，渐进式改革具有局部最优性。","abstractEn":"","pages":"223-244","doi":"10.19744/j.cnki.11-1235/f.2026.0115","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=NStZdEtrN25aL3NaOWRBL1dac2x1Tnk2SFIzZG02UHA1V0kxa09OclJadHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"汲铮, 华生, 傅捷, 张晓奇. 从核准制到注册制：供给冲击、定价扭曲与渐进式改革策略[J]. 管理世界, 2026(8): 223-244.","lenZh":446,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026008013","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"基于文旅融合的粤港澳大湾区滨海休闲旅游发展——评《粤港澳大湾区融合发展与深圳实践》","summary":"粤港澳大湾区作为我国开放程度高、经济活力强的区域之一，其融合发展不仅是带动区域经济提质增效的核心引擎，也是促进文旅产业跨界赋能、打造世界级滨海休闲旅游目的地的重要载体。由彭芳梅撰写的《粤港澳大湾区融合发展与深圳实践》一书立足于粤港澳大湾区发展的理论根基和实践脉络，以多维度的研究视角和扎实的实证分析，为粤港澳大湾区的文旅融合、滨海休闲旅游发展提供参考与借鉴，兼具学术深度和现实价值。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026008013","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"8","authors":["刘赫男"],"affils":["华南师范大学音乐学院"],"title":"基于文旅融合的粤港澳大湾区滨海休闲旅游发展——评《粤港澳大湾区融合发展与深圳实践》","titleEn":"Development of Coastal Leisure Tourism in the Guangdong-Hong Kong-Macao Greater Bay Area Based on Cultural and Tourism Integration—A Review of \"Integration Development of the Guangdong-Hong Kong-Macao Greater Bay Area and Shenzhen's Practices\"","keywords":[],"keywordsEn":[],"abstract":"粤港澳大湾区作为我国开放程度高、经济活力强的区域之一，其融合发展不仅是带动区域经济提质增效的核心引擎，也是促进文旅产业跨界赋能、打造世界级滨海休闲旅游目的地的重要载体。由彭芳梅撰写的《粤港澳大湾区融合发展与深圳实践》一书立足于粤港澳大湾区发展的理论根基和实践脉络，以多维度的研究视角和扎实的实证分析，为粤港澳大湾区的文旅融合、滨海休闲旅游发展提供参考与借鉴，兼具学术深度和现实价值。","abstractEn":"As one of China's most open and economically dynamic regions, the Guangdong-Hong Kong-Macao Greater Bay Area serves not only as a core engine driving regional economic upgrading and efficiency enhancement, but also as a crucial platform for cross-sector empowerment of cultural and tourism industries and the development of a world-class coastal leisure tourism destination. Authored by Peng Fangmei, the book \"Integration Development of the Guangdong-Hong Kong-Macao Greater Bay Area and Shenzhen's Practices\" is grounded in the theoretical foundations and practical evolution of the Greater Bay Area's development. Through multidimensional research perspectives and robust empirical analysis, it provides valuable references and insights for cultural-tourism integration and coastal leisure tourism development in the region, combining both academic depth and practical relevance.","pages":"245","doi":"10.19744/j.cnki.11-1235/f.2026.0102","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=NStZdEtrN25aL3NaOWRBL1dac2x1SHhmejlCU1dOZGtQNEdkcG5kdUUwbHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"刘赫男. 基于文旅融合的粤港澳大湾区滨海休闲旅游发展——评《粤港澳大湾区融合发展与深圳实践》[J]. 管理世界, 2026(8): 245.","lenZh":191,"lenEn":882}},{"id":"archive/cssci-100:GLSJ2026008014","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"可持续发展视域下矿产资源综合利用管理机制的构建——评《矿产资源开发与管理》","summary":"矿产资源是支撑现代工业体系运转、保障产业链供应链安全稳定的重要物质基础，其综合利用水平直接决定矿业领域可持续发展的落地成效。笔者在主持国家自然科学基金面上项目“关键矿产资源可供性评价研究”（编号：42572386）时，对刘登娟和黄寰合著的《矿产资源开发与管理》一书进行了系统研究。该书立足我国矿产资源开发管理的现实国情与全球矿业绿色转型的发展趋势，系统梳理了矿产资源开发全流程的管理逻辑、制度框架与实践路径，包含关键矿产资源管理、绿色矿业发展等核心议题，为矿产资源领域的理论研究与行业实践搭建了学理性与实操性兼具的内容体系，也为强化关键矿产供应链韧性提供了重要的理论与实践指引。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026008014","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"8","authors":["鄢雯","徐德义"],"affils":["中国地质大学(武汉)经济管理学院"],"title":"可持续发展视域下矿产资源综合利用管理机制的构建——评《矿产资源开发与管理》","titleEn":"Construction of a Comprehensive Management Mechanism for Mineral Resources under the Perspective of Sustainable Development—A Review of \"Mineral Resources Development and Management\"","keywords":[],"keywordsEn":[],"abstract":"矿产资源是支撑现代工业体系运转、保障产业链供应链安全稳定的重要物质基础，其综合利用水平直接决定矿业领域可持续发展的落地成效。笔者在主持国家自然科学基金面上项目“关键矿产资源可供性评价研究”（编号：42572386）时，对刘登娟和黄寰合著的《矿产资源开发与管理》一书进行了系统研究。该书立足我国矿产资源开发管理的现实国情与全球矿业绿色转型的发展趋势，系统梳理了矿产资源开发全流程的管理逻辑、制度框架与实践路径，包含关键矿产资源管理、绿色矿业发展等核心议题，为矿产资源领域的理论研究与行业实践搭建了学理性与实操性兼具的内容体系，也为强化关键矿产供应链韧性提供了重要的理论与实践指引。","abstractEn":"Mineral resources serve as a vital material foundation for sustaining the operation of modern industrial systems and ensuring the security and stability of industrial and supply chains. The level of their comprehensive utilization directly determines the effectiveness of sustainable development in the mining sector. While leading the National Natural Science Foundation of China General Program \"Study on the Availability Assessment of Critical Mineral Resources\" (Grant No. 42572386), I conducted a systematic study of the book *Mineral Resource Development and Management* co-authored by Liu Dengjuan and Huang Huan. Grounded in China's practical realities of mineral resource development and management and the global trend of green transformation in the mining industry, the book systematically outlines the management logic, institutional framework, and practical pathways throughout the entire process of mineral resource development. It covers core topics such as critical mineral resource management and the development of green mining, establishing a content system that integrates theoretical reasoning with practical applicability for both academic research and industrial practices in the field of mineral resources. Furthermore, it provides significant theoretical and practical guidance for enhancing the resilience of critical mineral supply chains.","pages":"246","doi":"10.19744/j.cnki.11-1235/f.2026.0104","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=NStZdEtrN25aL3NaOWRBL1dac2x1R3U5ZmdSRWRRbk9zTTE0YXl6QjdQWnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"鄢雯, 徐德义. 可持续发展视域下矿产资源综合利用管理机制的构建——评《矿产资源开发与管理》[J]. 管理世界, 2026(8): 246.","lenZh":289,"lenEn":1366}},{"id":"archive/cssci-100:GLSJ2026008015","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"数字经济时代下海南文化产业创新发展的路径——评《文化产业与管理（第4版）》","summary":"在数字经济驱动经济高质量发展的时代背景下，文化产业与数字技术的融合已成为我国文化强国建设与产业转型升级的趋势，海南自由贸易港建设作为国家重大战略部署，其“三区一中心”的战略定位，是打造国际旅游消费中心、彰显中国文化自信的核心载体。当前海南正处于自贸港封关运作的关键时期，如何依托数字经济破解文化产业发展瓶颈，如何探索与自贸港开放定位相适配的创新发展路径，已成为海南产业发展的核心议题。由赵晶媛编著的《文化产业与管理（第4版）》一书系统阐释了文化产业的底层逻辑与运行规律，并且深度分析了数字经济背景下文化产业各领域的实践问题与解决方案，为数字经济时代海南文化产业的创新发展提供了重要参考。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026008015","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"8","authors":["何璐"],"affils":["海南师范大学"],"title":"数字经济时代下海南文化产业创新发展的路径——评《文化产业与管理（第4版）》","titleEn":"Pathways for the Innovative Development of Hainan's Cultural Industry in the Digital Economy Era—A Review of \"Cultural Industry and Management (4th Edition)\"","keywords":[],"keywordsEn":[],"abstract":"在数字经济驱动经济高质量发展的时代背景下，文化产业与数字技术的融合已成为我国文化强国建设与产业转型升级的趋势，海南自由贸易港建设作为国家重大战略部署，其“三区一中心”的战略定位，是打造国际旅游消费中心、彰显中国文化自信的核心载体。当前海南正处于自贸港封关运作的关键时期，如何依托数字经济破解文化产业发展瓶颈，如何探索与自贸港开放定位相适配的创新发展路径，已成为海南产业发展的核心议题。由赵晶媛编著的《文化产业与管理（第4版）》一书系统阐释了文化产业的底层逻辑与运行规律，并且深度分析了数字经济背景下文化产业各领域的实践问题与解决方案，为数字经济时代海南文化产业的创新发展提供了重要参考。","abstractEn":"In the era where the digital economy drives high-quality economic development, the integration of cultural industries and digital technology has become a trend in China's efforts to build a strong cultural nation and upgrade its industrial structure. The construction of the Hainan Free Trade Port, as a major national strategic deployment, with its strategic positioning of \"three zones and one center,\" serves as a core vehicle for creating an international tourism and consumption hub and showcasing China's cultural confidence. Currently, Hainan is in a critical period of the free trade port's customs closure operation. How to leverage the digital economy to overcome bottlenecks in the development of cultural industries and explore innovative development paths aligned with the open positioning of the free trade port has become a central issue in Hainan's industrial development. The book \"Cultural Industries and Management (4th Edition)\" edited by Zhao Jingyuan systematically explains the underlying logic and operational principles of cultural industries and provides an in-depth analysis of practical issues and solutions across various sectors of the cultural industry in the context of the digital economy, offering important references for the innovative development of Hainan's cultural industries in the digital age.","pages":"247","doi":"10.19744/j.cnki.11-1235/f.2026.0105","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=NStZdEtrN25aL3NaOWRBL1dac2x1Q2VHYzllelYwbVJQQitjTlpsY3dycHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"何璐. 数字经济时代下海南文化产业创新发展的路径——评《文化产业与管理（第4版）》[J]. 管理世界, 2026(8): 247.","lenZh":293,"lenEn":1335}},{"id":"archive/cssci-100:GLSJ2026008003","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"生产网络视角下的数字技术扩散效应：经验证据、影响路径与经济后果","summary":"在国际技术竞争加剧与逆全球化浪潮冲击下，如何依托国内生产网络实现数字经济时代的技术进步，已成为中国推进高质量发展的重要命题。本文以2015～2023年中国上市企业为研究样本，基于企业主营业务文本与投入产出结构，创新性地构建了企业层面的生产网络，并系统考察了生产网络中数字技术扩散的经验证据、影响机制及经济后果。研究发现，数字技术在生产网络中存在显著的扩散效应，且该效应并不能被传统的供应链关系或一般性技术溢出所完全解释。机制分析表明，数字技术主要通过微观要素配置效应以及结构性竞争和技术演化效应实现扩散。异质性分析显示，当焦点企业技术吸收能力较强、行业竞争程度较低、地区金融发展水平较高、科技支持力度较强时，扩散效应更为明显；当焦点企业与关联企业地理距离更近、关联企业资金运转水平更高、网络集中度更高时，扩散效应也更为突出。进一步研究发现，生产网络上的数字技术扩散能够提升企业生产效率与经营绩效，并降低生产网络风险、增强系统韧性。本文揭示了数字经济时代技术扩散的规律，也为中国企业在复杂国际环境下突破技术封锁、重塑竞争优势提供了政策依据。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026008003","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"8","authors":["金星晔","赵晟坤","郅曼琳","李涛"],"affils":["中央财经大学经济学院","中国人民大学财政金融学院"],"title":"生产网络视角下的数字技术扩散效应：经验证据、影响路径与经济后果","titleEn":"Digital Technology Diffusion through Production Networks: Empirical Evidence, Channels, and Economic Consequences","keywords":["数字技术","生产网络","扩散效应","文本分析","大语言模型"],"keywordsEn":[],"abstract":"在国际技术竞争加剧与逆全球化浪潮冲击下，如何依托国内生产网络实现数字经济时代的技术进步，已成为中国推进高质量发展的重要命题。本文以2015～2023年中国上市企业为研究样本，基于企业主营业务文本与投入产出结构，创新性地构建了企业层面的生产网络，并系统考察了生产网络中数字技术扩散的经验证据、影响机制及经济后果。研究发现，数字技术在生产网络中存在显著的扩散效应，且该效应并不能被传统的供应链关系或一般性技术溢出所完全解释。机制分析表明，数字技术主要通过微观要素配置效应以及结构性竞争和技术演化效应实现扩散。异质性分析显示，当焦点企业技术吸收能力较强、行业竞争程度较低、地区金融发展水平较高、科技支持力度较强时，扩散效应更为明显；当焦点企业与关联企业地理距离更近、关联企业资金运转水平更高、网络集中度更高时，扩散效应也更为突出。进一步研究发现，生产网络上的数字技术扩散能够提升企业生产效率与经营绩效，并降低生产网络风险、增强系统韧性。本文揭示了数字经济时代技术扩散的规律，也为中国企业在复杂国际环境下突破技术封锁、重塑竞争优势提供了政策依据。","abstractEn":"","pages":"37-58","doi":"10.19744/j.cnki.11-1235/f.2026.0113","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=cUlVVHFaeHp5YUNJTHM3eUZXeFFSSHhmejlCU1dOZGtQNEdkcG5kdUUwbHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"金星晔, 赵晟坤, 郅曼琳, 李涛. 生产网络视角下的数字技术扩散效应：经验证据、影响路径与经济后果[J]. 管理世界, 2026(8): 37-58.","lenZh":469,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026008004","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"结构性税收政策、生产结构转型与人力资源配置","summary":"有效塑造结构优化、分布合理的现代化人力资源，以人力资源充分利用加快人才红利形成和减缓人口红利收缩，是以人口高质量发展支撑中国式现代化的重要政策着力点。本文构建了一个内生市场和家庭生产结构转型的一般均衡分析框架，提出结构性税收政策会深刻改变人力资源在市场和家庭之间、在市场内不同产业之间的配置结构，促使生产结构和就业结构发生趋势性变化。本文发现，对劳动和资本等要素收入或对投资品与耐用消费品等产品销售实施结构性税收政策，既会通过相对价格效应促使要素之间和产业之间相互替代，又会通过需求规模效应推动市场生产与家庭生产相互替代，推动市场内部与市场和家庭之间的生产结构和人力资源配置结构转型。特别地，对非技能密集型产业资本收入或对投资品与耐用消费品销售减税，可以有效推动劳动从家庭流入市场并提高技能密集型产业比重，对技能密集型产业高技能劳动收入减税，可以有效推动高技能劳动从家庭流入技能密集型产业。本文从生产和生活互动、资本和劳动替代的视角发展了关于税收政策和结构转型关系的研究，为更好发挥有为政府作用优化人力资源配置提供了政策建议。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026008004","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"8","authors":["王钰冰","郭凯明","龚六堂"],"affils":["中山大学港澳珠江三角洲研究中心","中山大学粤港澳发展研究院","中山大学岭南学院","武汉大学高级研究中心","北京大学数量经济与数理金融教育部重点实验室"],"title":"结构性税收政策、生产结构转型与人力资源配置","titleEn":"Structrual Tax Policies, Structural Change in Production and Human Resources Allocation","keywords":["人力资源","结构转型","结构性税收","人口高质量发展","时间配置"],"keywordsEn":[],"abstract":"有效塑造结构优化、分布合理的现代化人力资源，以人力资源充分利用加快人才红利形成和减缓人口红利收缩，是以人口高质量发展支撑中国式现代化的重要政策着力点。本文构建了一个内生市场和家庭生产结构转型的一般均衡分析框架，提出结构性税收政策会深刻改变人力资源在市场和家庭之间、在市场内不同产业之间的配置结构，促使生产结构和就业结构发生趋势性变化。本文发现，对劳动和资本等要素收入或对投资品与耐用消费品等产品销售实施结构性税收政策，既会通过相对价格效应促使要素之间和产业之间相互替代，又会通过需求规模效应推动市场生产与家庭生产相互替代，推动市场内部与市场和家庭之间的生产结构和人力资源配置结构转型。特别地，对非技能密集型产业资本收入或对投资品与耐用消费品销售减税，可以有效推动劳动从家庭流入市场并提高技能密集型产业比重，对技能密集型产业高技能劳动收入减税，可以有效推动高技能劳动从家庭流入技能密集型产业。本文从生产和生活互动、资本和劳动替代的视角发展了关于税收政策和结构转型关系的研究，为更好发挥有为政府作用优化人力资源配置提供了政策建议。","abstractEn":"","pages":"59-73","doi":"10.19744/j.cnki.11-1235/f.2026.0103","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=cUlVVHFaeHp5YUNJTHM3eUZXeFFSR3U5ZmdSRWRRbk9zTTE0YXl6QjdQWnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"王钰冰, 郭凯明, 龚六堂. 结构性税收政策、生产结构转型与人力资源配置[J]. 管理世界, 2026(8): 59-73.","lenZh":463,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026008005","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"条件依赖视角下金融开放的稳定效应再审视","summary":"统筹金融开放和安全是促进金融高质量发展、推动金融强国建设的核心要义。本文立足条件依赖视角，基于典型事实归纳提炼和DSGE模型机制推演，系统剖析了金融开放的稳定效应。本文研究发现：金融开放对金融稳定的影响并非单一线性，而是呈现出显著的对外生压力冲击的条件依赖特征；其内在机理在于，不同压力冲击与开放政策交互作用，动态重塑了跨境资本流动的流向与流量、汇率波动的趋向与幅度，进而驱动金融开放影响金融稳定的正向缓冲机制与负向放大机制在激活状态和主导地位上的竞合演化。因此，金融开放进程应具备动态适应性，需要伴随外生压力环境变迁而不断调适、渐进修正。针对中国开放实践，短期视域内应精准识别主导性冲击类型，实施因势利导的动态调控策略，特别是在趋近开放制度边界时，必须高度警惕资本规模性流转同汇率剧烈波动联动共振所引致的金融稳定效应逆转；长期视域内则应兼顾效率与安全，遵循汇率市场化改革先行、资本账户开放随后的序贯路径，以强化金融系统韧性、提升社会福利水平、推动金融高质量发展。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026008005","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"8","authors":["方先明","李舞岩"],"affils":["南京大学商学院"],"title":"条件依赖视角下金融开放的稳定效应再审视","titleEn":"A Reappraisal of the Stability Effects of Financial Liberalization from a Conditional-Dependence Perspective","keywords":["金融开放","资本账户开放","汇率市场化","金融稳定","条件依赖"],"keywordsEn":[],"abstract":"统筹金融开放和安全是促进金融高质量发展、推动金融强国建设的核心要义。本文立足条件依赖视角，基于典型事实归纳提炼和DSGE模型机制推演，系统剖析了金融开放的稳定效应。本文研究发现：金融开放对金融稳定的影响并非单一线性，而是呈现出显著的对外生压力冲击的条件依赖特征；其内在机理在于，不同压力冲击与开放政策交互作用，动态重塑了跨境资本流动的流向与流量、汇率波动的趋向与幅度，进而驱动金融开放影响金融稳定的正向缓冲机制与负向放大机制在激活状态和主导地位上的竞合演化。因此，金融开放进程应具备动态适应性，需要伴随外生压力环境变迁而不断调适、渐进修正。针对中国开放实践，短期视域内应精准识别主导性冲击类型，实施因势利导的动态调控策略，特别是在趋近开放制度边界时，必须高度警惕资本规模性流转同汇率剧烈波动联动共振所引致的金融稳定效应逆转；长期视域内则应兼顾效率与安全，遵循汇率市场化改革先行、资本账户开放随后的序贯路径，以强化金融系统韧性、提升社会福利水平、推动金融高质量发展。","abstractEn":"","pages":"74-99","doi":"10.19744/j.cnki.11-1235/f.2026.0116","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=cUlVVHFaeHp5YUNJTHM3eUZXeFFSQ2VHYzllelYwbVJQQitjTlpsY3dycHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"方先明, 李舞岩. 条件依赖视角下金融开放的稳定效应再审视[J]. 管理世界, 2026(8): 74-99.","lenZh":432,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026007001","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"数据财政的前景研究：企业需求视角","summary":"数字经济推动对数据财政的前沿性制度探索，公共数据的需求问题由此受到关注。鉴于数据价值的转化以人力资本适配为前提，本文从雇佣决策这一重要视角切入，对企业公共数据需求进行检验。理论研究表明，随着地方政府开放公共数据交易权限，企业将扩大数据分析岗的相对雇佣规模，且这一效应在不同模式下存在异质性。基于招聘平台大数据的实证研究发现，地方政府实施公共数据授权运营显著增加了企业对数据分析岗的招聘规模。授权条款剖析表明，统一授权模式最佳、国有代理模式更优。在企业招聘动态方面：高技能岗位的招聘增幅超过了低技能岗位，工资呈现类似特征；对工作经验的关注高于学历。改革带动了地方政府相关收入提升。从数据分析流程看，政策对上游岗位没有显著影响，对下游岗位有正向影响。数据相关岗位的扩招是其他研发岗位缩减的结果，体现为研发方向的结构性调整。本文将数据财政可行性研究从概念探索拓展至具体分析，对于推进数字经济时代的财政体制创新、健全地方财政的可持续发展机制具有重要意义。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026007001","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"7","authors":["杨健鹏","梅思雨","张晗雨","梁若冰"],"affils":["中山大学岭南学院","广东财经大学财政税务学院","厦门大学经济学院"],"title":"数据财政的前景研究：企业需求视角","titleEn":"The Prospects of Data Finance: An Enterprise Demand Perspective","keywords":["数据财政","公共数据","授权运营","招聘结构"],"keywordsEn":[],"abstract":"数字经济推动对数据财政的前沿性制度探索，公共数据的需求问题由此受到关注。鉴于数据价值的转化以人力资本适配为前提，本文从雇佣决策这一重要视角切入，对企业公共数据需求进行检验。理论研究表明，随着地方政府开放公共数据交易权限，企业将扩大数据分析岗的相对雇佣规模，且这一效应在不同模式下存在异质性。基于招聘平台大数据的实证研究发现，地方政府实施公共数据授权运营显著增加了企业对数据分析岗的招聘规模。授权条款剖析表明，统一授权模式最佳、国有代理模式更优。在企业招聘动态方面：高技能岗位的招聘增幅超过了低技能岗位，工资呈现类似特征；对工作经验的关注高于学历。改革带动了地方政府相关收入提升。从数据分析流程看，政策对上游岗位没有显著影响，对下游岗位有正向影响。数据相关岗位的扩招是其他研发岗位缩减的结果，体现为研发方向的结构性调整。本文将数据财政可行性研究从概念探索拓展至具体分析，对于推进数字经济时代的财政体制创新、健全地方财政的可持续发展机制具有重要意义。","abstractEn":"","pages":"1-19","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=NmZ1SlZZdjhQWnBPRWF3MmNhbW5uRVBNRkNROURIQ0pCWXRlRmVtZFl5RnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"杨健鹏, 梅思雨, 张晗雨, 梁若冰. 数据财政的前景研究：企业需求视角[J]. 管理世界, 2026(7): 1-19.","lenZh":423,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026007007","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"药品集采改革、医疗负担与医疗公平——来自医院处方的微观证据","summary":"国家组织药品集中带量采购是治理药价虚高、深化我国医药服务供给侧改革的重要切入口。本文构建了“价格冲击—行为反应—医疗费用与医疗公平结果”的统一分析框架，利用患者—医生层面的微观处方数据，基于逐步推开的17个批次的集采药品，系统考察了集采改革对药品价格和患者医疗负担的综合影响。研究发现：第一，改革的降价效应明显，药品平均降幅超过70%，且对非集采药产生了显著的溢出效应；第二，改革降低了门诊患者的医药负担，且低收入群体、农村居民等群体受益更大，起到了促进医疗公平的预期效果；第三，改革容易出现“跷跷板效应”，医院和医生的策略性反应是将药品收入的减少转嫁到其他收费项目，部分抵消了集采改革的减负效应。本文的结论证实了药品集采改革在缓解“看病贵”问题方面的积极效应，也揭示了改革对医疗服务供需方行为的潜在影响及挑战，为完善药品集采制度提供了经验证据和重要参考。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026007007","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"7","authors":["诸宇灵","顾晓敏","周黎安","范子英"],"affils":["上海财经大学公共管理学院","上海财经大学中国式现代化研究院","上海对外经贸大学国际经贸学院","北京大学光华管理学院","上海财经大学财税投资学院"],"title":"药品集采改革、医疗负担与医疗公平——来自医院处方的微观证据","titleEn":"Volume-Based Drug Procurement Reform, Medical Burden and Health Equity: Evidence from Hospital Prescriptions","keywords":["药品集中带量采购","医疗负担","溢出效应","医疗公平"],"keywordsEn":[],"abstract":"国家组织药品集中带量采购是治理药价虚高、深化我国医药服务供给侧改革的重要切入口。本文构建了“价格冲击—行为反应—医疗费用与医疗公平结果”的统一分析框架，利用患者—医生层面的微观处方数据，基于逐步推开的17个批次的集采药品，系统考察了集采改革对药品价格和患者医疗负担的综合影响。研究发现：第一，改革的降价效应明显，药品平均降幅超过70%，且对非集采药产生了显著的溢出效应；第二，改革降低了门诊患者的医药负担，且低收入群体、农村居民等群体受益更大，起到了促进医疗公平的预期效果；第三，改革容易出现“跷跷板效应”，医院和医生的策略性反应是将药品收入的减少转嫁到其他收费项目，部分抵消了集采改革的减负效应。本文的结论证实了药品集采改革在缓解“看病贵”问题方面的积极效应，也揭示了改革对医疗服务供需方行为的潜在影响及挑战，为完善药品集采制度提供了经验证据和重要参考。","abstractEn":"","pages":"108-127","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=NmZ1SlZZdjhQWnBPRWF3MmNhbW5uQkRvemdYOGZnbnRGSlJHU01nTDkxMXZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"诸宇灵, 顾晓敏, 周黎安, 范子英. 药品集采改革、医疗负担与医疗公平——来自医院处方的微观证据[J]. 管理世界, 2026(7): 108-127.","lenZh":378,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026007008","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"分级诊疗制度下的医疗控费机制——基于大样本病案数据的研究证据","summary":"近年来，我国持续推进分级诊疗制度建设，旨在通过优化医疗资源配置与规范医疗服务行为，遏制因资源错配和过度医疗导致的费用不合理增长。然而，由于医疗服务供需双方存在信息不对称，且医疗机构在资源扩张与医保支付约束下拥有自主调整患者结构的空间，分级诊疗制度的有效运行仍面临现实挑战。为此，本文在理论上构建了医疗卫生机构最优选择模型，并基于某副省级城市2020～2023年1687.7万条病案首页数据，提出了结合医疗大数据与机器学习的诊疗难度识别方法，用以验证基层医疗机构的控费功能，同时系统分析三级医院入院标准的差异及其动态调整机制。研究结果显示，三级医院入院标准显著受当日住院资源占用量影响：资源越宽松，入院标准越低。在此机制下，部分三级医院可能出于利益驱动主动降低入院门槛，长期收治低诊疗难度患者，从而在一定程度上削弱分级诊疗制度的实施效果。基于上述发现，本文提出，可依托病案大数据构建以基层病组为核心的差异化医保支付方式，对三级医院入院标准下调行为形成柔性约束。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026007008","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"7","authors":["臧文斌","吴舒祥","王一如","陈晨"],"affils":["西南财经大学公共管理学院","西南财经大学金融学院","山西财经大学财政与公共经济学院"],"title":"分级诊疗制度下的医疗控费机制——基于大样本病案数据的研究证据","titleEn":"Cost Containment in China's Hierarchical Medical System: Evidence from Large-scale Inpatient Medical Records","keywords":["分级诊疗","医疗控费","病案数据"],"keywordsEn":[],"abstract":"近年来，我国持续推进分级诊疗制度建设，旨在通过优化医疗资源配置与规范医疗服务行为，遏制因资源错配和过度医疗导致的费用不合理增长。然而，由于医疗服务供需双方存在信息不对称，且医疗机构在资源扩张与医保支付约束下拥有自主调整患者结构的空间，分级诊疗制度的有效运行仍面临现实挑战。为此，本文在理论上构建了医疗卫生机构最优选择模型，并基于某副省级城市2020～2023年1687.7万条病案首页数据，提出了结合医疗大数据与机器学习的诊疗难度识别方法，用以验证基层医疗机构的控费功能，同时系统分析三级医院入院标准的差异及其动态调整机制。研究结果显示，三级医院入院标准显著受当日住院资源占用量影响：资源越宽松，入院标准越低。在此机制下，部分三级医院可能出于利益驱动主动降低入院门槛，长期收治低诊疗难度患者，从而在一定程度上削弱分级诊疗制度的实施效果。基于上述发现，本文提出，可依托病案大数据构建以基层病组为核心的差异化医保支付方式，对三级医院入院标准下调行为形成柔性约束。","abstractEn":"","pages":"128-152","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=NmZ1SlZZdjhQWnBPRWF3MmNhbW5uTEdrV3ltdHRVUWJnNEpnTWZiWFJTSnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"臧文斌, 吴舒祥, 王一如, 陈晨. 分级诊疗制度下的医疗控费机制——基于大样本病案数据的研究证据[J]. 管理世界, 2026(7): 128-152.","lenZh":429,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026007009","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"组织架构体系如何支撑裂变企业突破性创新？——基于“结构·流程·边界”框架的混合方法研究","summary":"突破性创新离不开作为生产关系重要成分的组织架构体系的支撑。脱胎于母体的裂变企业具有实现突破性创新的多维度组织优势。然而，既有相关研究多聚焦裂变创业中开放性组织边界这个单一维度，忽视了组织架构的体系化构成及其联合影响。本文基于“结构·流程·边界”三维度框架，采用“混合方法”研究设计，从组织架构视角探究裂变企业多维度组织架构要素对突破性创新影响的联动效应。首先，通过模糊集定性比较分析（fsQCA），识别出自主试验型、开放共创型、集群合作型和母子协同型4类实现突破性创新的构型，以及保守迟滞型和脱嵌孤岛型两类导致非突破性创新的构型。其次，跨构型比较结果显示，裂变企业突破性创新存在内部主体驱动、内外部主体联合与外部主体牵引3种组织机制，母体嵌入与产业嵌入可呈现互补或互斥关系。最后，依循多个设计模式开展多案例分析，包括通过“等结果设计”阐释各条等效路径背后的因果机制，通过“变异设计”控制其他维度要素并创造边界维度的变异，以此辨明创新主体两类嵌入间的替代关系，以及通过“双尾设计”揭示多维度组织架构要素共存或缺失产生创新“敏捷性”与“锁定性”的极化效应，从而深化对QCA分析结果内蕴的因果复杂性的认识。本文从结构、流程和边界维度推进了裂变企业创新支撑体系的理论建构，在夯实构型研究理论根基的同时推动了管理组织学向企业间关系领域拓展，为在微观经济层面构建驱动创新的组织体系以加快发展新质生产力提供了政策与管理启示。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026007009","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"7","authors":["王凤彬","冯依帆","曾凯","郭长伟"],"affils":["中国人民大学商学院","中国人民大学世界一流企业研究院李占祥矛盾管理学研究所","中国科学技术大学科技商学院、管理学院"],"title":"组织架构体系如何支撑裂变企业突破性创新？——基于“结构·流程·边界”框架的混合方法研究","titleEn":"How Does the Organizational Architecture System Support Breakthrough Innovation in Corporate Spin-offs? An Integrative Mixed-Method Analysis Based on the \"Structure-Process-Boundary\" Framework","keywords":["裂变企业","突破性创新","组织架构","新型生产关系","混合方法"],"keywordsEn":[],"abstract":"突破性创新离不开作为生产关系重要成分的组织架构体系的支撑。脱胎于母体的裂变企业具有实现突破性创新的多维度组织优势。然而，既有相关研究多聚焦裂变创业中开放性组织边界这个单一维度，忽视了组织架构的体系化构成及其联合影响。本文基于“结构·流程·边界”三维度框架，采用“混合方法”研究设计，从组织架构视角探究裂变企业多维度组织架构要素对突破性创新影响的联动效应。首先，通过模糊集定性比较分析（fsQCA），识别出自主试验型、开放共创型、集群合作型和母子协同型4类实现突破性创新的构型，以及保守迟滞型和脱嵌孤岛型两类导致非突破性创新的构型。其次，跨构型比较结果显示，裂变企业突破性创新存在内部主体驱动、内外部主体联合与外部主体牵引3种组织机制，母体嵌入与产业嵌入可呈现互补或互斥关系。最后，依循多个设计模式开展多案例分析，包括通过“等结果设计”阐释各条等效路径背后的因果机制，通过“变异设计”控制其他维度要素并创造边界维度的变异，以此辨明创新主体两类嵌入间的替代关系，以及通过“双尾设计”揭示多维度组织架构要素共存或缺失产生创新“敏捷性”与“锁定性”的极化效应，从而深化对QCA分析结果内蕴的因果复杂性的认识。本文从结构、流程和边界维度推进了裂变企业创新支撑体系的理论建构，在夯实构型研究理论根基的同时推动了管理组织学向企业间关系领域拓展，为在微观经济层面构建驱动创新的组织体系以加快发展新质生产力提供了政策与管理启示。","abstractEn":"","pages":"153-175","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=NmZ1SlZZdjhQWnBPRWF3MmNhbW5uQThaWkZWMFZOYlU4WVZxcEc3MUVZdHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"王凤彬, 冯依帆, 曾凯, 郭长伟. 组织架构体系如何支撑裂变企业突破性创新？——基于“结构·流程·边界”框架的混合方法研究[J]. 管理世界, 2026(7): 153-175.","lenZh":605,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026007010","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"央企共链行动何以推动技术双向进化？——以“九天”基座大模型为例","summary":"央企共链行动旨在打破跨产业领域合作壁垒，推动链长企业间融通创新。然而，作为一种外源性制度安排，其如何在合作双方缺乏历史互动与领域重叠的条件下重构企业间依赖关系，进而催生技术双向进化，仍有待探索。针对此问题，以中国移动“九天”基座大模型跨产业链赋能的共链实践为案例，结合制度理论与组织共演理论展开剖析。本文研究发现，共链行动的核心作用在于通过注入制度信任，以破解市场机制下难以自发形成的跨链协作信任困境。其传导路径在于：一是在制度层面，共链行动通过塑造链长身份与共链过程的合法性形成制度信任，构成协作初始信任的关键前因；二是在组织层面，桥接性议题场域充当关键过程机制，将抽象的制度信任转化为具体的组织协同；三是在技术层面，技术要素的跨领域编排催生技术依赖关系，从而激活供需双方技术渗透与要素反哺的价值共创循环，构成技术双向进化的核心生成机制。研究结论涌现为制度型技术共生模型，阐明了外源性的制度安排如何通过破解信任困境，最终催化出一个内生、自持的技术共演机制。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026007010","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"7","authors":["赵晨","林晨","高中华"],"affils":["北京邮电大学经济管理学院","中国社会科学院数量经济与技术经济研究所","中国社会科学院工业经济研究所"],"title":"央企共链行动何以推动技术双向进化？——以“九天”基座大模型为例","titleEn":"How Does the Central SOE \"Collaborative Chain\" Initiative Drive Bidirectional Technological Evolution? A Case Study of the \"Jiutian\" Foundation Model","keywords":["共链行动","制度场域","技术共演","产业链链长"],"keywordsEn":[],"abstract":"央企共链行动旨在打破跨产业领域合作壁垒，推动链长企业间融通创新。然而，作为一种外源性制度安排，其如何在合作双方缺乏历史互动与领域重叠的条件下重构企业间依赖关系，进而催生技术双向进化，仍有待探索。针对此问题，以中国移动“九天”基座大模型跨产业链赋能的共链实践为案例，结合制度理论与组织共演理论展开剖析。本文研究发现，共链行动的核心作用在于通过注入制度信任，以破解市场机制下难以自发形成的跨链协作信任困境。其传导路径在于：一是在制度层面，共链行动通过塑造链长身份与共链过程的合法性形成制度信任，构成协作初始信任的关键前因；二是在组织层面，桥接性议题场域充当关键过程机制，将抽象的制度信任转化为具体的组织协同；三是在技术层面，技术要素的跨领域编排催生技术依赖关系，从而激活供需双方技术渗透与要素反哺的价值共创循环，构成技术双向进化的核心生成机制。研究结论涌现为制度型技术共生模型，阐明了外源性的制度安排如何通过破解信任困境，最终催化出一个内生、自持的技术共演机制。","abstractEn":"","pages":"176-192","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=d2NuYnliQTVUOGtoS3FyYTZINkFUclNZN052dWJhTGZGYTVMUThPZ3FuNXZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"赵晨, 林晨, 高中华. 央企共链行动何以推动技术双向进化？——以“九天”基座大模型为例[J]. 管理世界, 2026(7): 176-192.","lenZh":429,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026007011","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"专精特新企业可持续成长的实现机制——基于扩展适应创新视角的多案例研究","summary":"破除专业化困境、构筑可持续的技术与市场优势，成为专精特新企业可持续成长的核心问题。已有研究从技术突破或市场跃迁出发探讨专业化困境的破解路径，但难以有效回答专精特新企业在新兴业务拓展过程中，技术与市场两端不确定条件下的技术与市场联动跃迁机制。基于扩展适应创新视角，本文以“技术—市场”关系为切口，采用纵向多案例研究方法，系统揭示专精特新企业通过扩展适应创新牵引技术与场景协同跃迁、实现可持续成长的内在机制。研究发现：（1）技术精一发展与场景梯度攀升构成专精特新企业成长的两大支柱；（2）扩展适应创新通过“征用—集成—生成”三位一体的行动联动技术与场景，依托“模块升级、结构深化、原理更新”3种路径破解专业化困境；（3）“技术精一发展—扩展适应创新—场景梯度攀升”协同演化，推动专精特新企业的可持续成长。本研究基于扩展适应创新视角揭示了专精特新企业双螺旋成长机制，理清了专精特新企业扩展适应创新的行动与路径，推动了专精特新企业可持续成长研究的发展。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026007011","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"7","authors":["吴波","杨步韵","邬爱其","牛志伟","李子莹"],"affils":["浙江工商大学浙商研究院","浙江工商大学工商管理学院(MBA学院)","浙江树人学院经济与管理学院","浙江大学管理学院","浙江工商大学经济学院","浙江工商大学现代商贸中心"],"title":"专精特新企业可持续成长的实现机制——基于扩展适应创新视角的多案例研究","titleEn":"The Mechanism for Achieving Sustainable Growth of SRDI Enterprises: A Multi-Case Study from the Perspective of Exaptive Innovation","keywords":["专精特新","扩展适应创新","技术精一发展","场景梯度攀升","可持续成长"],"keywordsEn":[],"abstract":"破除专业化困境、构筑可持续的技术与市场优势，成为专精特新企业可持续成长的核心问题。已有研究从技术突破或市场跃迁出发探讨专业化困境的破解路径，但难以有效回答专精特新企业在新兴业务拓展过程中，技术与市场两端不确定条件下的技术与市场联动跃迁机制。基于扩展适应创新视角，本文以“技术—市场”关系为切口，采用纵向多案例研究方法，系统揭示专精特新企业通过扩展适应创新牵引技术与场景协同跃迁、实现可持续成长的内在机制。研究发现：（1）技术精一发展与场景梯度攀升构成专精特新企业成长的两大支柱；（2）扩展适应创新通过“征用—集成—生成”三位一体的行动联动技术与场景，依托“模块升级、结构深化、原理更新”3种路径破解专业化困境；（3）“技术精一发展—扩展适应创新—场景梯度攀升”协同演化，推动专精特新企业的可持续成长。本研究基于扩展适应创新视角揭示了专精特新企业双螺旋成长机制，理清了专精特新企业扩展适应创新的行动与路径，推动了专精特新企业可持续成长研究的发展。","abstractEn":"","pages":"193-213","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=d2NuYnliQTVUOGtoS3FyYTZINkFUa1BNRkNROURIQ0pCWXRlRmVtZFl5RnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"吴波, 杨步韵, 邬爱其, 牛志伟, 李子莹. 专精特新企业可持续成长的实现机制——基于扩展适应创新视角的多案例研究[J]. 管理世界, 2026(7): 193-213.","lenZh":422,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026007002","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"数据赋能的两面性：低碳与居民收入增长何以兼得？","summary":"推动数字化和绿色化协同发展是“十五五”期间实现我国经济高质量发展和“双碳”目标的关键抓手。本文基于数据部门寡头竞争的特征构建了一个多地区多部门一般均衡模型，从全国和区域层面考察了数据价值链中各生产环节的技术进步对碳排放与福利的影响，并对现有低碳政策的效果进行了评估。结果表明：（1）信息数据化和算力的提升显著推动了全行业产出和产业数字化水平增长，进而提高了居民实际收入，且二者协同赋能的福利效应更为显著；（2）这两类数字技术进步也因刺激能源需求而导致碳排放总量上升，侵蚀了居民的净福利。特别地，数据部门的碳排放增长幅度远高于商品部门，表明其正成为不可忽视的碳排放来源；（3）数字技术协同发展虽大幅推动西部地区产业结构跃迁，但也使得区域间收入差距与环境压力同步扩大，导致西部地区净福利受损最为严重；（4）在现行技术条件下，碳税是实现经济增长与低碳转型双赢的有效手段。相比之下，电力补贴虽能提升居民实际收入，却因刺激能源需求而加剧了碳排放；碳捕集与封存技术则同时损害了居民实际收入与净福利。本文通过揭示数字经济时代的“杰文斯悖论”，为数字技术在促进经济增长与可持续发展之间的平衡路径选择提供了政策启示。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026007002","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"7","authors":["王永进","周成英","谢芳"],"affils":["南开大学经济学院","南开大学经济行为与政策模拟实验室","南开大学前沿交叉学科研究院","中南财经政法大学经济贸易学院"],"title":"数据赋能的两面性：低碳与居民收入增长何以兼得？","titleEn":"Data Empowerment's Duality: How Can Low-Carbon and Household Income Growth Be Reconciled?","keywords":["数据价值链","数字技术","碳外部性","产业数字化"],"keywordsEn":[],"abstract":"推动数字化和绿色化协同发展是“十五五”期间实现我国经济高质量发展和“双碳”目标的关键抓手。本文基于数据部门寡头竞争的特征构建了一个多地区多部门一般均衡模型，从全国和区域层面考察了数据价值链中各生产环节的技术进步对碳排放与福利的影响，并对现有低碳政策的效果进行了评估。结果表明：（1）信息数据化和算力的提升显著推动了全行业产出和产业数字化水平增长，进而提高了居民实际收入，且二者协同赋能的福利效应更为显著；（2）这两类数字技术进步也因刺激能源需求而导致碳排放总量上升，侵蚀了居民的净福利。特别地，数据部门的碳排放增长幅度远高于商品部门，表明其正成为不可忽视的碳排放来源；（3）数字技术协同发展虽大幅推动西部地区产业结构跃迁，但也使得区域间收入差距与环境压力同步扩大，导致西部地区净福利受损最为严重；（4）在现行技术条件下，碳税是实现经济增长与低碳转型双赢的有效手段。相比之下，电力补贴虽能提升居民实际收入，却因刺激能源需求而加剧了碳排放；碳捕集与封存技术则同时损害了居民实际收入与净福利。本文通过揭示数字经济时代的“杰文斯悖论”，为数字技术在促进经济增长与可持续发展之间的平衡路径选择提供了政策启示。","abstractEn":"","pages":"20-37","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=NmZ1SlZZdjhQWnBPRWF3MmNhbW5uTnk2SFIzZG02UHA1V0kxa09OclJadHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"王永进, 周成英, 谢芳. 数据赋能的两面性：低碳与居民收入增长何以兼得？[J]. 管理世界, 2026(7): 20-37.","lenZh":499,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026007012","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"企业间竞合：理论演进与数智时代的研究展望","summary":"在数智时代，数智技术渗透与数据价值激活使企业间竞合日益普遍，且更具价值创造潜力。然而现有竞合理论囿于人类决策主体性、传统资源稀缺性、组织边界稳定性的隐含假设，难以解释以人机协同为决策主体、以数据为核心资源、以平台生态为主要场域的竞合新现实。本文通过系统性文献综述，梳理竞合理论从概念溯源、视角拓展到体系整合的演进脉络，揭示传统理论范式的解释边界。在系统总结数智时代企业间竞合实践呈现的悖论性加剧、动态性增强、价值性提升等新趋势，以及分析竞合研究向数智化转型、数字平台生态系统等情境拓展的基础上，本文提出“数智竞合”概念，构建了整合“微观—中观—宏观”层次与“前因—过程—结果”链条的矩阵式研究框架。具体而言，本文在微观层面聚焦人机协同决策中的数智认知与算法特征、信任互动与能力演化等；中观层面探讨数智化组织变革、组织间数智链接、数实融合与算法赋能的演化和治理过程等；宏观层面分析数智经济环境与平台生态情境、多主体复杂网络演化与平台元治理以及生态绩效等价值结果。本文不仅有助于研究者加深对竞合理论演进和发展的理解，还通过提出数智竞合概念及研究展望，为根植于中国数智化实践的竞合研究提供新视角。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026007012","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"7","authors":["马鹏程","刘锡禄","李东红"],"affils":["中国人民大学商学院","清华大学经济管理学院"],"title":"企业间竞合：理论演进与数智时代的研究展望","titleEn":"Inter-Firm Coopetition: Theoretical Evolution and Research Agenda for the Digital-Intelligent Era","keywords":["竞合","数智技术","数据资源","平台生态"],"keywordsEn":[],"abstract":"在数智时代，数智技术渗透与数据价值激活使企业间竞合日益普遍，且更具价值创造潜力。然而现有竞合理论囿于人类决策主体性、传统资源稀缺性、组织边界稳定性的隐含假设，难以解释以人机协同为决策主体、以数据为核心资源、以平台生态为主要场域的竞合新现实。本文通过系统性文献综述，梳理竞合理论从概念溯源、视角拓展到体系整合的演进脉络，揭示传统理论范式的解释边界。在系统总结数智时代企业间竞合实践呈现的悖论性加剧、动态性增强、价值性提升等新趋势，以及分析竞合研究向数智化转型、数字平台生态系统等情境拓展的基础上，本文提出“数智竞合”概念，构建了整合“微观—中观—宏观”层次与“前因—过程—结果”链条的矩阵式研究框架。具体而言，本文在微观层面聚焦人机协同决策中的数智认知与算法特征、信任互动与能力演化等；中观层面探讨数智化组织变革、组织间数智链接、数实融合与算法赋能的演化和治理过程等；宏观层面分析数智经济环境与平台生态情境、多主体复杂网络演化与平台元治理以及生态绩效等价值结果。本文不仅有助于研究者加深对竞合理论演进和发展的理解，还通过提出数智竞合概念及研究展望，为根植于中国数智化实践的竞合研究提供新视角。","abstractEn":"","pages":"214-239","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=d2NuYnliQTVUOGtoS3FyYTZINkFUdHk2SFIzZG02UHA1V0kxa09OclJadHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"马鹏程, 刘锡禄, 李东红. 企业间竞合：理论演进与数智时代的研究展望[J]. 管理世界, 2026(7): 214-239.","lenZh":495,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026007013","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"洞察绿色低碳消费真实世界的前沿之作——评《绿色低碳消费大数据报告（2025）》","summary":"在经济社会发展全面绿色转型的关键时期，消费端的变革力量正日益凸显。党的二十届四中全会明确提出“加快形成绿色生产生活方式”，这为绿色消费研究赋予了重大的现实使命。2025年，由王建明教授、吴龙昌高级经济师合著的《绿色低碳消费大数据报告（2025）》由经济科学出版社正式出版。作为一名长期关注幸福产业与消费行为研究的学者，我深感这部著作不仅是一部有数据、有框架、有洞察、有温度的扎实学术成果，更是一面映照中国绿色消费现实与未来的镜子，为洞察绿色消费提供了全新的数据支撑。本文谨从学术专著书评的角度，对该书的学术贡献与特点进行评析。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026007013","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"7","authors":["白长虹"],"affils":["南开大学商学院"],"title":"洞察绿色低碳消费真实世界的前沿之作——评《绿色低碳消费大数据报告（2025）》","titleEn":"","keywords":[],"keywordsEn":[],"abstract":"在经济社会发展全面绿色转型的关键时期，消费端的变革力量正日益凸显。党的二十届四中全会明确提出“加快形成绿色生产生活方式”，这为绿色消费研究赋予了重大的现实使命。2025年，由王建明教授、吴龙昌高级经济师合著的《绿色低碳消费大数据报告（2025）》由经济科学出版社正式出版。作为一名长期关注幸福产业与消费行为研究的学者，我深感这部著作不仅是一部有数据、有框架、有洞察、有温度的扎实学术成果，更是一面映照中国绿色消费现实与未来的镜子，为洞察绿色消费提供了全新的数据支撑。本文谨从学术专著书评的角度，对该书的学术贡献与特点进行评析。","abstractEn":"","pages":"252-253","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=d2NuYnliQTVUOGtoS3FyYTZINkFUbnhmejlCU1dOZGtQNEdkcG5kdUUwbHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"白长虹. 洞察绿色低碳消费真实世界的前沿之作——评《绿色低碳消费大数据报告（2025）》[J]. 管理世界, 2026(7): 252-253.","lenZh":263,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026007014","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"共同富裕视域下的乡村振兴研究——评《迈向共同富裕：理论内涵和实现路径》","summary":"共同富裕与乡村振兴，既是马克思主义中国化时代化的重要理论议题，更是新时代中国特色社会主义的重大实践课题。在全面推进中国式现代化进程中，城乡发展不平衡、农村发展不充分仍是实现全体人民共同富裕的制约因素，而乡村振兴则是扎实推进共同富裕的基础性工程。笔者在主持新疆维吾尔自治区社会科学基金项目“乡村振兴战略下南疆农民农村共同富裕研究”（项目编号：2023BYJ037）时，对李实、何文炯所著《迈向共同富裕：理论内涵和实现路径》一书进行了系统研究。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026007014","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"7","authors":["骆育芳"],"affils":["吉林大学马克思主义学院","新疆医科大学马克思主义学院"],"title":"共同富裕视域下的乡村振兴研究——评《迈向共同富裕：理论内涵和实现路径》","titleEn":"Research on Rural Revitalization from the Perspective of Common Prosperity—A Review of \"Towards Common Prosperity: Theoretical Connotations and Implementation Paths\"","keywords":[],"keywordsEn":[],"abstract":"共同富裕与乡村振兴，既是马克思主义中国化时代化的重要理论议题，更是新时代中国特色社会主义的重大实践课题。在全面推进中国式现代化进程中，城乡发展不平衡、农村发展不充分仍是实现全体人民共同富裕的制约因素，而乡村振兴则是扎实推进共同富裕的基础性工程。笔者在主持新疆维吾尔自治区社会科学基金项目“乡村振兴战略下南疆农民农村共同富裕研究”（项目编号：2023BYJ037）时，对李实、何文炯所著《迈向共同富裕：理论内涵和实现路径》一书进行了系统研究。","abstractEn":"Common Prosperity and Rural Revitalization are not only significant theoretical issues in the Sinicization and modernization of Marxism but also major practical tasks in the new era of socialism with Chinese characteristics. In the comprehensive advancement of Chinese-style modernization, the imbalance in urban-rural development and the insufficiency of rural development remain constraining factors in achieving common prosperity for all people, while Rural Revitalization serves as a foundational project for steadily advancing common prosperity. While presiding over the Xinjiang Uygur Autonomous Region Social Sciences Fund project \"Research on Common Prosperity for Farmers and Rural Areas in Southern Xinjiang under the Rural Revitalization Strategy\" (Project No.: 2023BYJ037), the author conducted a systematic study of the book *Towards Common Prosperity: Theoretical Connotations and Implementation Pathways* by Li Shi and He Wenjiong.","pages":"254","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=d2NuYnliQTVUOGtoS3FyYTZINkFUbXU5ZmdSRWRRbk9zTTE0YXl6QjdQWnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"骆育芳. 共同富裕视域下的乡村振兴研究——评《迈向共同富裕：理论内涵和实现路径》[J]. 管理世界, 2026(7): 254.","lenZh":220,"lenEn":946}},{"id":"archive/cssci-100:GLSJ2026007015","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"粤港澳大湾区一流教师教育体系的建设现状与路径——评《粤港澳大湾区教师教育协同发展研究》","summary":"在粤港澳大湾区建设国际科技创新中心的国家战略背景下，人才作为创新发展的核心要素，其培育质量与供给能力直接决定着粤港澳大湾区科技创新的纵深发展，而教师教育体系则是人才培育的根基所在，是粤港澳大湾区打造高水平人才高地、实现科技创新能级跃升的重要支撑。由施雨丹撰写的《粤港澳大湾区教师教育协同发展研究》一书立足“一国两制”的制度优势，系统梳理了粤港澳三地教师教育的现实格局与协同困境，提出了兼具理论高度与实践操作的一体化发展路径，为粤港澳大湾区一流教师教育体系的建设提供了重要的理论指引。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026007015","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"7","authors":["宋雪洁"],"affils":["齐鲁师范学院"],"title":"粤港澳大湾区一流教师教育体系的建设现状与路径——评《粤港澳大湾区教师教育协同发展研究》","titleEn":"Current Status and Pathways for Building a First-Class Teacher Education System in the Guangdong-Hong Kong-Macao Greater Bay Area—A Review of \"Research on the Collaborative Development of Teacher Education in the Guangdong-Hong Kong-Macao Greater Bay Area\"","keywords":[],"keywordsEn":[],"abstract":"在粤港澳大湾区建设国际科技创新中心的国家战略背景下，人才作为创新发展的核心要素，其培育质量与供给能力直接决定着粤港澳大湾区科技创新的纵深发展，而教师教育体系则是人才培育的根基所在，是粤港澳大湾区打造高水平人才高地、实现科技创新能级跃升的重要支撑。由施雨丹撰写的《粤港澳大湾区教师教育协同发展研究》一书立足“一国两制”的制度优势，系统梳理了粤港澳三地教师教育的现实格局与协同困境，提出了兼具理论高度与实践操作的一体化发展路径，为粤港澳大湾区一流教师教育体系的建设提供了重要的理论指引。","abstractEn":"Under the national strategy of building an international science and technology innovation center in the Guangdong-Hong Kong-Macao Greater Bay Area, talent, as the core element of innovation-driven development, directly determines the depth and breadth of scientific and technological innovation in the region through the quality of cultivation and supply capacity. The teacher education system serves as the foundation for talent cultivation and is a crucial support for the Greater Bay Area in establishing a high-level talent hub and achieving a leap in scientific and technological innovation capabilities. The book \"Research on the Coordinated Development of Teacher Education in the Guangdong-Hong Kong-Macao Greater Bay Area,\" authored by Shi Yudan, leverages the institutional advantages of \"one country, two systems\" to systematically analyze the current landscape and collaborative challenges of teacher education in Guangdong, Hong Kong, and Macao. It proposes an integrated development path that combines theoretical depth with practical implementation, offering significant theoretical guidance for building a first-class teacher education system in the Greater Bay Area.","pages":"255","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=d2NuYnliQTVUOGtoS3FyYTZINkFUaWVHYzllelYwbVJQQitjTlpsY3dycHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"宋雪洁. 粤港澳大湾区一流教师教育体系的建设现状与路径——评《粤港澳大湾区教师教育协同发展研究》[J]. 管理世界, 2026(7): 255.","lenZh":241,"lenEn":1184}},{"id":"archive/cssci-100:GLSJ2026007016","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"从旅游开发走向乡村治理——评《中国传统村落乡村旅游化的动力与治理》","summary":"当大众旅游的时代洪流与传统村落的现代化进程相向而行时，发展旅游往往成为诸多乡村社会的一致选择，乡村社会也因此在经济、社会、文化等多个层面发生变化。客观认识这些现象，并施以科学的治理举措，从而使乡村社会在这一历史进程中实现多目标协同共进和可持续发展，既是当前乡村旅游研究的理论命题，也是乡村社会发展的现实问题。史玉丁博士所著《中国传统村落乡村旅游化的动力与治理》一书，正是围绕这些问题展开的系统性研究。作者基于对西南民族村寨和传统村落持续十余年的调研与思考，围绕“乡村旅游化”这一核心命题，展开对其动力机制、演化过程、过度旅游风险与治理逻辑的系统分析。从结构安排看，形成了较为完整的研究脉络，呈现出由概念建构、机制分析到治理回应的递进式结构。值得注意的是，作者没有停留在一般性的经验描述层面，而是努力将案例观察上升为理论概括，经由现象识别推进机制解释，通过旅游管理和公共治理视角提出理论框架，这使全书兼具理论系统性和现实解释力。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026007016","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"7","authors":["唐晓云"],"affils":["中国旅游研究院"],"title":"从旅游开发走向乡村治理——评《中国传统村落乡村旅游化的动力与治理》","titleEn":"From Tourism Development to Rural Governance—A Review of \"The Driving Forces and Governance of Traditional Chinese Villages' Tourism Transformation\"","keywords":[],"keywordsEn":[],"abstract":"当大众旅游的时代洪流与传统村落的现代化进程相向而行时，发展旅游往往成为诸多乡村社会的一致选择，乡村社会也因此在经济、社会、文化等多个层面发生变化。客观认识这些现象，并施以科学的治理举措，从而使乡村社会在这一历史进程中实现多目标协同共进和可持续发展，既是当前乡村旅游研究的理论命题，也是乡村社会发展的现实问题。史玉丁博士所著《中国传统村落乡村旅游化的动力与治理》一书，正是围绕这些问题展开的系统性研究。作者基于对西南民族村寨和传统村落持续十余年的调研与思考，围绕“乡村旅游化”这一核心命题，展开对其动力机制、演化过程、过度旅游风险与治理逻辑的系统分析。从结构安排看，形成了较为完整的研究脉络，呈现出由概念建构、机制分析到治理回应的递进式结构。值得注意的是，作者没有停留在一般性的经验描述层面，而是努力将案例观察上升为理论概括，经由现象识别推进机制解释，通过旅游管理和公共治理视角提出理论框架，这使全书兼具理论系统性和现实解释力。","abstractEn":"When the torrent of the mass tourism era converges with the modernization process of traditional villages, tourism development often becomes a unanimous choice for many rural societies, leading to transformations across economic, social, and cultural dimensions. Objectively understanding these phenomena and implementing scientific governance measures to achieve multi-objective synergy and sustainable development in rural societies during this historical process is both a theoretical proposition in current rural tourism research and a practical issue in rural societal development. Dr. Shi Yuding’s book, *The Dynamics and Governance of Rural Tourism in Traditional Chinese Villages*, offers a systematic study centered on these issues. Based on over a decade of research and reflection on ethnic villages and traditional settlements in Southwest China, the author explores the core theme of \"rural tourism development,\" systematically analyzing its driving mechanisms, evolutionary processes, risks of overtourism, and governance logic. Structurally, the book presents a coherent research framework, progressing from conceptual construction and mechanism analysis to governance responses. Notably, the author goes beyond general empirical descriptions, striving to elevate case observations into theoretical generalizations, advancing from phenomenon identification to mechanism explanation, and proposing theoretical frameworks from the perspectives of tourism management and public governance. This approach endows the book with both theoretical systematicity and practical explanatory power.","pages":"256","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=d2NuYnliQTVUOGtoS3FyYTZINkFUc3ZKd0FXd0RmeW84MzV2VlQrcTFqOXZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"唐晓云. 从旅游开发走向乡村治理——评《中国传统村落乡村旅游化的动力与治理》[J]. 管理世界, 2026(7): 256.","lenZh":413,"lenEn":1600}},{"id":"archive/cssci-100:GLSJ2026007003","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"监管沙盒何以缓释银行系统性风险？——基于银行金融科技创新与监管的演化博弈分析","summary":"金融监管如何促进金融科技创新去伪存真、平衡创新激励与风险防范，是我国推进金融高质量发展进程中亟待解决的难题之一，监管沙盒试验为破解这一难题提供了实践参考。本文通过构建银行与监管部门的演化博弈模型并进行仿真分析，进而以监管沙盒实施作为准自然实验，检验其对银行系统性风险的缓释效果。研究发现：监管沙盒对银行系统性风险具有缓释作用，其能够通过提升银行金融科技创新质量，实现对银行系统性风险的缓释。银行金融科技创新质量提升可通过提高银行效率与抑制机构关联来降低潜在系统性风险，监管沙盒则进一步强化了上述机制。具体表现为：一方面，通过增强创新质量对银行效率的驱动来提升风险抵御能力；另一方面，通过加强其对关联程度的抑制来弱化风险传染。本文研究为识别监管沙盒效能、揭示其风险缓释作用路径提供了理论支撑和实证证据，对完善金融科技创新监管框架、引导高质量金融科技创新具有参考价值。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026007003","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"7","authors":["马亚明","徐佳翔","温博慧","袁铭","孟弋然"],"affils":["天津财经大学金融学院","天津财经大学统计学院"],"title":"监管沙盒何以缓释银行系统性风险？——基于银行金融科技创新与监管的演化博弈分析","titleEn":"How Does the Regulatory Sandbox Mitigate Bank Systemic Risk? An Evolutionary Game Analysis of Bank FinTech Innovation and Regulation","keywords":["监管沙盒","银行金融科技创新","系统性风险","演化博弈","BERT模型"],"keywordsEn":[],"abstract":"金融监管如何促进金融科技创新去伪存真、平衡创新激励与风险防范，是我国推进金融高质量发展进程中亟待解决的难题之一，监管沙盒试验为破解这一难题提供了实践参考。本文通过构建银行与监管部门的演化博弈模型并进行仿真分析，进而以监管沙盒实施作为准自然实验，检验其对银行系统性风险的缓释效果。研究发现：监管沙盒对银行系统性风险具有缓释作用，其能够通过提升银行金融科技创新质量，实现对银行系统性风险的缓释。银行金融科技创新质量提升可通过提高银行效率与抑制机构关联来降低潜在系统性风险，监管沙盒则进一步强化了上述机制。具体表现为：一方面，通过增强创新质量对银行效率的驱动来提升风险抵御能力；另一方面，通过加强其对关联程度的抑制来弱化风险传染。本文研究为识别监管沙盒效能、揭示其风险缓释作用路径提供了理论支撑和实证证据，对完善金融科技创新监管框架、引导高质量金融科技创新具有参考价值。","abstractEn":"","pages":"38-53","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=NmZ1SlZZdjhQWnBPRWF3MmNhbW5uSHhmejlCU1dOZGtQNEdkcG5kdUUwbHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"马亚明, 徐佳翔, 温博慧, 袁铭, 孟弋然. 监管沙盒何以缓释银行系统性风险？——基于银行金融科技创新与监管的演化博弈分析[J]. 管理世界, 2026(7): 38-53.","lenZh":382,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026007004","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"交易机制创新助力提升资本市场流动性——基于债券匿名点击交易平台的研究","summary":"交易机制创新是提升资本市场流动性与服务实体经济效能的关键路径。本文以2016年银行间市场推出的现券匿名点击业务（以下简称X-Bond）为一项准自然实验，采用理论建模和双重差分方法，系统研究了匿名交易对债券市场流动性的影响及其机制。理论模型揭示，在场外交易市场引入集中匿名竞价机制后，匿名竞价交易对债券流动性的改善效果取决于逆向选择加剧与搜寻成本节约之间的相对权衡。基于跨市场国债的实证分析显示，相较于交易所市场，X-Bond的推出显著降低了银行间市场国债的买卖价差，有效提升了其流动性。进一步的作用机制检验表明，对于逆向选择问题较为突出的信用债，其流动性改善程度弱于国债；而对于前期搜寻成本较高的信用债，匿名点击交易的流动性提升效果更为显著。本文的研究结论不仅为数字金融时代资本市场交易机制的优化提供了理论依据与实证经验，也为监管部门通过创新交易机制防范债务风险、降低政府融资成本以及完善多层次资本市场体系提供了政策参考。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026007004","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"7","authors":["叶彦艺","施展","俞文奇"],"affils":["北京化工大学经济管理学院","清华大学五道口金融学院"],"title":"交易机制创新助力提升资本市场流动性——基于债券匿名点击交易平台的研究","titleEn":"Trading Mechanism Innovation Enhances Capital Market Liquidity: A Study Based on the Anonymous Bond Click Trading Platform","keywords":["交易机制创新","债券流动性","匿名交易","逆向选择","搜索成本"],"keywordsEn":[],"abstract":"交易机制创新是提升资本市场流动性与服务实体经济效能的关键路径。本文以2016年银行间市场推出的现券匿名点击业务（以下简称X-Bond）为一项准自然实验，采用理论建模和双重差分方法，系统研究了匿名交易对债券市场流动性的影响及其机制。理论模型揭示，在场外交易市场引入集中匿名竞价机制后，匿名竞价交易对债券流动性的改善效果取决于逆向选择加剧与搜寻成本节约之间的相对权衡。基于跨市场国债的实证分析显示，相较于交易所市场，X-Bond的推出显著降低了银行间市场国债的买卖价差，有效提升了其流动性。进一步的作用机制检验表明，对于逆向选择问题较为突出的信用债，其流动性改善程度弱于国债；而对于前期搜寻成本较高的信用债，匿名点击交易的流动性提升效果更为显著。本文的研究结论不仅为数字金融时代资本市场交易机制的优化提供了理论依据与实证经验，也为监管部门通过创新交易机制防范债务风险、降低政府融资成本以及完善多层次资本市场体系提供了政策参考。","abstractEn":"","pages":"54-73","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=NmZ1SlZZdjhQWnBPRWF3MmNhbW5uR3U5ZmdSRWRRbk9zTTE0YXl6QjdQWnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"叶彦艺, 施展, 俞文奇. 交易机制创新助力提升资本市场流动性——基于债券匿名点击交易平台的研究[J]. 管理世界, 2026(7): 54-73.","lenZh":411,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026007005","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"减税激励与外资稳定：预提税改革的证据","summary":"外商投资是推进高水平对外开放的重要内容，为发展新质生产力和实现中国式现代化发挥着重要作用。国际招商引资已进入存量博弈时代，为了鼓励境外投资者持续扩大在华投资，中国于2017年对非居民企业施行以分配利润再投资缓征预提所得税的优惠政策。本文发现，再投资税收优惠对面临资金约束的外资企业的投资规模具有显著的改善效果。机制分析表明，该政策能够扩大股权融资、降低权益资本成本，有助于缓解融资约束，进而促进投资。进一步地，本文发现该政策的作用效果在境外投资者预提税率较高、持股比例较高、外资企业的避税程度较低或所在地营商环境较好的情况下更加明显。同时，再投资税收优惠提升了外资企业经营绩效和市场价值，并对集团中其他子公司的投资产生正向溢出效应。本文的研究不仅为理解稳外资税收激励政策的成效和作用路径提供了经验支持，而且为优化吸引和利用外资的税收政策环境提供了有益参考。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026007005","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"7","authors":["周小昶","冯晨","曾艺"],"affils":["中南财经政法大学财政税务学院","中南财经政法大学税收治理研究中心","西安交通大学经济与金融学院","华中师范大学经济与工商管理学院"],"title":"减税激励与外资稳定：预提税改革的证据","titleEn":"Tax Reduction Incentives and Foreign Investment Stability: Evidence from the Withholding Tax Reform","keywords":["预提税优惠","利润再投资","稳外资"],"keywordsEn":[],"abstract":"外商投资是推进高水平对外开放的重要内容，为发展新质生产力和实现中国式现代化发挥着重要作用。国际招商引资已进入存量博弈时代，为了鼓励境外投资者持续扩大在华投资，中国于2017年对非居民企业施行以分配利润再投资缓征预提所得税的优惠政策。本文发现，再投资税收优惠对面临资金约束的外资企业的投资规模具有显著的改善效果。机制分析表明，该政策能够扩大股权融资、降低权益资本成本，有助于缓解融资约束，进而促进投资。进一步地，本文发现该政策的作用效果在境外投资者预提税率较高、持股比例较高、外资企业的避税程度较低或所在地营商环境较好的情况下更加明显。同时，再投资税收优惠提升了外资企业经营绩效和市场价值，并对集团中其他子公司的投资产生正向溢出效应。本文的研究不仅为理解稳外资税收激励政策的成效和作用路径提供了经验支持，而且为优化吸引和利用外资的税收政策环境提供了有益参考。","abstractEn":"","pages":"74-89","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=NmZ1SlZZdjhQWnBPRWF3MmNhbW5uQ2VHYzllelYwbVJQQitjTlpsY3dycHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"周小昶, 冯晨, 曾艺. 减税激励与外资稳定：预提税改革的证据[J]. 管理世界, 2026(7): 74-89.","lenZh":378,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026007006","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"地方财政压力、信贷干预与银行风险承担","summary":"厘清财政金融互动逻辑是构建财政金融协同联动机制的重要基础，更是经济高质量发展的坚实保障。本文通过构建包含地方政府、商业银行与代表性企业的三期动态博弈模型，并结合基于房地产限购政策的多时点双重差分估计，系统考察了地方财政压力如何影响区域性商业银行风险承担水平。理论分析表明，为应对财政压力冲击，地方政府的信贷规模干预弱化了商业银行对借款企业的事前筛选强度，导致更多信贷资源流向了高风险企业，由此推升了银行风险承担水平。随后的计量检验结果支持了上述理论推断，证实了地方财政压力所导致的信贷规模干预是核心作用渠道，并排除了银行事后监督强度下降、房地产贷款减少等多种替代性解释。异质性分析显示，地方财政压力的银行风险承担效应在政策冲击强度大和政府干预能力强的地区以及资本充足率高的商业银行更为凸显。本文强调，地方政府对区域性商业银行的信贷干预在助力实体经济发展的同时，还会推升银行风险承担水平，这对于“稳增长”和“防风险”双重目标的实现具有重要政策启示。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026007006","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"7","authors":["刘贯春","柳杭娟","陈肖雄","何枫"],"affils":["中山大学岭南学院","中山大学中观经济学与区域产业协同发展重点实验室","西南大学经济管理学院","首都经济贸易大学金融学院"],"title":"地方财政压力、信贷干预与银行风险承担","titleEn":"Local Fiscal Pressure, Credit Intervention and Bank Risk-Taking","keywords":["地方财政压力","银行风险承担","信贷规模干预","房地产限购政策"],"keywordsEn":[],"abstract":"厘清财政金融互动逻辑是构建财政金融协同联动机制的重要基础，更是经济高质量发展的坚实保障。本文通过构建包含地方政府、商业银行与代表性企业的三期动态博弈模型，并结合基于房地产限购政策的多时点双重差分估计，系统考察了地方财政压力如何影响区域性商业银行风险承担水平。理论分析表明，为应对财政压力冲击，地方政府的信贷规模干预弱化了商业银行对借款企业的事前筛选强度，导致更多信贷资源流向了高风险企业，由此推升了银行风险承担水平。随后的计量检验结果支持了上述理论推断，证实了地方财政压力所导致的信贷规模干预是核心作用渠道，并排除了银行事后监督强度下降、房地产贷款减少等多种替代性解释。异质性分析显示，地方财政压力的银行风险承担效应在政策冲击强度大和政府干预能力强的地区以及资本充足率高的商业银行更为凸显。本文强调，地方政府对区域性商业银行的信贷干预在助力实体经济发展的同时，还会推升银行风险承担水平，这对于“稳增长”和“防风险”双重目标的实现具有重要政策启示。","abstractEn":"","pages":"90-107","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=NmZ1SlZZdjhQWnBPRWF3MmNhbW5uTXZKd0FXd0RmeW84MzV2VlQrcTFqOXZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"刘贯春, 柳杭娟, 陈肖雄, 何枫. 地方财政压力、信贷干预与银行风险承担[J]. 管理世界, 2026(7): 90-107.","lenZh":423,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026006001","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"税基型减税政策、投资激励与消费提振——基于工资薪金据实扣除政策的证据","summary":"面对各级政府的财政紧平衡约束，扩大内需的积极财政政策需要调整思路，利用投资和消费的关联机制优化政策设计，实现从“一策一目标”向“一策多目标”转变。本文以中国企业所得税改革中有关内资企业工资薪金税前扣除政策调整为一次政策实验，探究扩大内需的积极财政政策从“一策一目标”向“一策多目标”转变的可能性。通过对中国2008年针对内资企业实施的工资薪金据实扣除政策的研究证实，中国2008年实施的内资企业工资薪金据实扣除政策能够同时产生激励投资提振消费的政策效果，在扩大内需上是一个具有“一箭双雕”作用的减税政策。基于受益归宿机制的识别发现，工资薪金据实扣除政策利用了企业最大化利润的行为动机，激励企业在扩大投资约6.15%的同时，增加雇佣、提高工资和降低产品价格，使职工和消费者也分享了政策受益，带动了居民消费增长约2.57%。本文的研究发现为当下解决财政紧平衡约束与积极财政政策要更加有力之间的矛盾提供了新思路。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026006001","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"6","authors":["李永友","吴云成"],"affils":["浙江财经大学财政安全监测与智慧治理实验室","山东大学经济学院","武汉大学经济与管理学院"],"title":"税基型减税政策、投资激励与消费提振——基于工资薪金据实扣除政策的证据","titleEn":"Tax Base Reduction, Investment Incentive and Consumption Boosting: Evidence from the Payroll Tax Deduction","keywords":["扩大内需","消费提振","受益分配","税基型减税"],"keywordsEn":[],"abstract":"面对各级政府的财政紧平衡约束，扩大内需的积极财政政策需要调整思路，利用投资和消费的关联机制优化政策设计，实现从“一策一目标”向“一策多目标”转变。本文以中国企业所得税改革中有关内资企业工资薪金税前扣除政策调整为一次政策实验，探究扩大内需的积极财政政策从“一策一目标”向“一策多目标”转变的可能性。通过对中国2008年针对内资企业实施的工资薪金据实扣除政策的研究证实，中国2008年实施的内资企业工资薪金据实扣除政策能够同时产生激励投资提振消费的政策效果，在扩大内需上是一个具有“一箭双雕”作用的减税政策。基于受益归宿机制的识别发现，工资薪金据实扣除政策利用了企业最大化利润的行为动机，激励企业在扩大投资约6.15%的同时，增加雇佣、提高工资和降低产品价格，使职工和消费者也分享了政策受益，带动了居民消费增长约2.57%。本文的研究发现为当下解决财政紧平衡约束与积极财政政策要更加有力之间的矛盾提供了新思路。","abstractEn":"","pages":"1-18","doi":"10.19744/j.cnki.11-1235/f.2026.0080","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=Y2ZXZUoycEtHd1pDcktYWjhVb0xuRVBNRkNROURIQ0pCWXRlRmVtZFl5RnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"李永友, 吴云成. 税基型减税政策、投资激励与消费提振——基于工资薪金据实扣除政策的证据[J]. 管理世界, 2026(6): 1-18.","lenZh":403,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026006002","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"计价货币与汇率传递：人民币国际化的作用","summary":"基于中国对外贸易计价货币以美元计价占主导、本币计价相对有限的事实，本文采用2012～2019年境内省份—境外国别层面的进口贸易及本币计价数据，研究了美元作为中国对外贸易的载体货币与进口价格汇率传递的关系，并进一步探讨了人民币承担跨境贸易计价货币职能以克服货币错配导致的汇率风险暴露问题。研究表明，我国进口价格变动对人民币兑美元汇率的敏感程度超过了人民币兑出口国货币。考虑美元和欧元等计价货币后，人民币双边汇率对进口价格的传递程度显著提高。有限的跨境贸易本币计价缓解了美元汇率的传递效应，从而降低了中国企业的汇率风险暴露。拓展研究发现，美元汇率通过贸易计价渠道对国内物价水平具有传导效应。通过提高本币的计价份额，“一带一路”倡议有助于提升中国企业在进口市场的国际定价权。区分计价货币粘性和计价货币弹性的不同情境，以及构建本币计价与汇率变动的Bartik工具变量估计等一系列检验均表明基准结果稳健。本文从计价货币的视角阐释了“人民币汇率调整之谜”，揭示了基于计价货币视角观察人民币汇率传递问题的重要性。本文的研究有助于更好地理解跨境贸易计价对人民币国际化进程的重要性。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026006002","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"6","authors":["王雅琦","杨措","谭小芬"],"affils":["中央财经大学金融学院","中央财经大学国际金融研究中心","青海民族大学经济与管理学院","北京航空航天大学经济管理学院","北京航空航天大学低碳治理与政策智能实验室"],"title":"计价货币与汇率传递：人民币国际化的作用","titleEn":"Invoice Currency and Exchange Rate Pass-Through: The Role of RMB Internationalization","keywords":["汇率传递","本币计价","人民币国际化","“一带一路”倡议"],"keywordsEn":[],"abstract":"基于中国对外贸易计价货币以美元计价占主导、本币计价相对有限的事实，本文采用2012～2019年境内省份—境外国别层面的进口贸易及本币计价数据，研究了美元作为中国对外贸易的载体货币与进口价格汇率传递的关系，并进一步探讨了人民币承担跨境贸易计价货币职能以克服货币错配导致的汇率风险暴露问题。研究表明，我国进口价格变动对人民币兑美元汇率的敏感程度超过了人民币兑出口国货币。考虑美元和欧元等计价货币后，人民币双边汇率对进口价格的传递程度显著提高。有限的跨境贸易本币计价缓解了美元汇率的传递效应，从而降低了中国企业的汇率风险暴露。拓展研究发现，美元汇率通过贸易计价渠道对国内物价水平具有传导效应。通过提高本币的计价份额，“一带一路”倡议有助于提升中国企业在进口市场的国际定价权。区分计价货币粘性和计价货币弹性的不同情境，以及构建本币计价与汇率变动的Bartik工具变量估计等一系列检验均表明基准结果稳健。本文从计价货币的视角阐释了“人民币汇率调整之谜”，揭示了基于计价货币视角观察人民币汇率传递问题的重要性。本文的研究有助于更好地理解跨境贸易计价对人民币国际化进程的重要性。","abstractEn":"","pages":"19-35","doi":"10.19744/j.cnki.11-1235/f.2026.0078","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=Y2ZXZUoycEtHd1pDcktYWjhVb0xuTnk2SFIzZG02UHA1V0kxa09OclJadHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"王雅琦, 杨措, 谭小芬. 计价货币与汇率传递：人民币国际化的作用[J]. 管理世界, 2026(6): 19-35.","lenZh":482,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026006003","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"中国贸易开放与产业结构转型——基于贸易壁垒演变的一般均衡分析","summary":"改革开放以来的实践表明，对外开放是推动我国经济增长与产业结构转型的重要力量。本文基于一个结合了非位似偏好的多国、多部门李嘉图贸易模型，定量分析了贸易开放对中国结构转型的影响。本文基于1995~2018年的贸易与生产数据，分别评估了我国从封闭到开放的贸易开放总效应，以及由积极准备入世所引致的贸易壁垒削减带来的贸易开放阶段效应。结果发现：我国的贸易开放政策效应在不同时期呈现非对称特征，入世期间的贸易开放阶段效应是推动我国产业结构转型的主要力量。若忽略了贸易开放的阶段性特征，则贸易开放的政策效应会被系统性低估。进一步地，对贸易模式的异质性分析表明一般贸易对结构转型的影响程度要高于加工贸易。由于目前我国的关税等贸易壁垒已处于较低水平，本文的长期预测发现仅以降低关税等贸易壁垒的传统方式来推动贸易开放对我国未来产业结构的影响相对有限。因此，要寻找推进我国未来产业转型的新动力，加快构建以国内大循环为主导的“双循环”新发展格局，稳步扩大制度型高水平对外开放，大力发展和培育新质生产力。最后，本文还讨论了全球贸易保护抬升对我国产业结构的影响，发现在工业出口受阻的同时，服务业也因国内需求收缩而受到更大程度的冲击。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026006003","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"6","authors":["陈卓","田巍"],"affils":["北京大学经济学院"],"title":"中国贸易开放与产业结构转型——基于贸易壁垒演变的一般均衡分析","titleEn":"Trade Openness and Industrial Structural Transformation in China: A General Equilibrium Analysis Based on the Evolution of Trade Barriers","keywords":["结构转型","国际贸易","量化分析"],"keywordsEn":[],"abstract":"改革开放以来的实践表明，对外开放是推动我国经济增长与产业结构转型的重要力量。本文基于一个结合了非位似偏好的多国、多部门李嘉图贸易模型，定量分析了贸易开放对中国结构转型的影响。本文基于1995~2018年的贸易与生产数据，分别评估了我国从封闭到开放的贸易开放总效应，以及由积极准备入世所引致的贸易壁垒削减带来的贸易开放阶段效应。结果发现：我国的贸易开放政策效应在不同时期呈现非对称特征，入世期间的贸易开放阶段效应是推动我国产业结构转型的主要力量。若忽略了贸易开放的阶段性特征，则贸易开放的政策效应会被系统性低估。进一步地，对贸易模式的异质性分析表明一般贸易对结构转型的影响程度要高于加工贸易。由于目前我国的关税等贸易壁垒已处于较低水平，本文的长期预测发现仅以降低关税等贸易壁垒的传统方式来推动贸易开放对我国未来产业结构的影响相对有限。因此，要寻找推进我国未来产业转型的新动力，加快构建以国内大循环为主导的“双循环”新发展格局，稳步扩大制度型高水平对外开放，大力发展和培育新质生产力。最后，本文还讨论了全球贸易保护抬升对我国产业结构的影响，发现在工业出口受阻的同时，服务业也因国内需求收缩而受到更大程度的冲击。","abstractEn":"","pages":"36-61","doi":"10.19744/j.cnki.11-1235/f.2026.0081","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=Y2ZXZUoycEtHd1pDcktYWjhVb0xuSHhmejlCU1dOZGtQNEdkcG5kdUUwbHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"陈卓, 田巍. 中国贸易开放与产业结构转型——基于贸易壁垒演变的一般均衡分析[J]. 管理世界, 2026(6): 36-61.","lenZh":503,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026006004","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"FTA深化对价值链结构性权力的不对称影响及“全球南方”的跃升路径","summary":"在全球化遭遇逆流、大国战略竞争加剧的背景下，大量缔结和持续深化的自由贸易协定（FTA）逐渐成为各国参与国际经贸规则制定与治理的关键战略手段。全球价值链网络中各国相互依赖的不对称性决定了其价值链结构性权力的非均衡性，FTA深化在推动全球价值链重构的同时，是否加剧了国家间结构性权力的不对称性？而全球南方又该如何突破“低端锁定”和“高端封锁”困境，实现价值链结构性权力的跃升与赶超？本文基于深度贸易协定数据库和经济合作与发展组织（OECD）的投入产出数据库，探究了FTA深化对价值链结构性权力的不对称影响及其形成机理。研究发现，FTA深化显著加剧了不同经济实力与治理效能的国家间的价值链结构性权力不对称性。机制检验表明，FTA深化通过影响国家间资源与产业基础的控制能力，扩大技术与创新水平的差距，推动中间品市场势力的重构，从而加剧了国家间结构性权力的不对称性。进一步研究发现，经济实力与治理效能相对较弱的国家可以利用后发优势提高自身经济适应度和扩大面向全球的高标准FTA网络来实现权力跃升甚至赶超。鉴于此，本文揭示了FTA深化对价值链结构性权力的不对称影响，并为促进全球南方国家实现权力跃升、推动构建更加开放包容、公平互惠、合作共赢的新型全球经贸治理体系，提供了理论支撑和定量依据。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026006004","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"6","authors":["彭水军","徐珍珍","李之旭"],"affils":["厦门大学经济学院","上海对外经贸大学国际经贸学院"],"title":"FTA深化对价值链结构性权力的不对称影响及“全球南方”的跃升路径","titleEn":"The Asymmetric Impact of the Deepening of FTA on Structural Power in Value Chains and the Ascent Path of the \"Global South\"","keywords":["FTA深化","价值链结构性权力","不对称影响","全球南方"],"keywordsEn":[],"abstract":"在全球化遭遇逆流、大国战略竞争加剧的背景下，大量缔结和持续深化的自由贸易协定（FTA）逐渐成为各国参与国际经贸规则制定与治理的关键战略手段。全球价值链网络中各国相互依赖的不对称性决定了其价值链结构性权力的非均衡性，FTA深化在推动全球价值链重构的同时，是否加剧了国家间结构性权力的不对称性？而全球南方又该如何突破“低端锁定”和“高端封锁”困境，实现价值链结构性权力的跃升与赶超？本文基于深度贸易协定数据库和经济合作与发展组织（OECD）的投入产出数据库，探究了FTA深化对价值链结构性权力的不对称影响及其形成机理。研究发现，FTA深化显著加剧了不同经济实力与治理效能的国家间的价值链结构性权力不对称性。机制检验表明，FTA深化通过影响国家间资源与产业基础的控制能力，扩大技术与创新水平的差距，推动中间品市场势力的重构，从而加剧了国家间结构性权力的不对称性。进一步研究发现，经济实力与治理效能相对较弱的国家可以利用后发优势提高自身经济适应度和扩大面向全球的高标准FTA网络来实现权力跃升甚至赶超。鉴于此，本文揭示了FTA深化对价值链结构性权力的不对称影响，并为促进全球南方国家实现权力跃升、推动构建更加开放包容、公平互惠、合作共赢的新型全球经贸治理体系，提供了理论支撑和定量依据。","abstractEn":"","pages":"62-78","doi":"10.19744/j.cnki.11-1235/f.2026.0077","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=Y2ZXZUoycEtHd1pDcktYWjhVb0xuR3U5ZmdSRWRRbk9zTTE0YXl6QjdQWnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"彭水军, 徐珍珍, 李之旭. FTA深化对价值链结构性权力的不对称影响及“全球南方”的跃升路径[J]. 管理世界, 2026(6): 62-78.","lenZh":539,"lenEn":0}},{"id":"archive/cssci-100:GLSJ2026006005","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"互利共赢还是零和博弈：跨境电商平台出海如何影响东道国既有平台？","summary":"近年来，以特姆和希音为代表的中国跨境电商平台在全球范围内高速发展。有观点认为，中国跨境电商的迅速崛起为东道国既有电商平台带来“不公平”竞争。本文则发现：中国跨境电商平台与东道国既有平台之间并非零和博弈关系。由于所依托的供应商不同，中外平台各自提供具有不同比较优势的商品，双方有望基于比较优势差异，实现“互利共赢”。本文构建理论模型证明，中国跨境电商平台不仅为东道国送去其不具有比较优势的商品，亦通过“收入溢出”与“信息溢出”双重效应促进东道国既有电商平台发展，实现市场总规模扩张。实证方面，基于覆盖68个国家和地区、434个城市的千万级电商平台监测数据，本文发现中国平台进入后，东道国既有电商平台的访问量、支付人数与用户评分等均显著提升，验证了理论模型的推断。本研究揭示了平台经济跨境贸易的独特属性，强调了发挥比较优势在出海过程中的重要作用，并为“经贸关系的本质是互利共赢”这一重要论断提供了新的理论注解与经验证据。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"GLSJ2026006005","journal":"管理世界","journalEn":"Management World","year":"2026","issue":"6","authors":["钟洲","陈思明","王勇","张梓桐"],"affils":["中国社会科学院数量经济与技术经济研究所","北京大学新结构经济学研究院"],"title":"互利共赢还是零和博弈：跨境电商平台出海如何影响东道国既有平台？","titleEn":"Win-Win Cooperation or Zero-Sum Game: How Does Cross-Border E-Commerce Platforms' Global Expansion Affect Incumbent Platforms in Host Countries?","keywords":["跨境电商","数字平台","出海","比较优势","关税"],"keywordsEn":[],"abstract":"近年来，以特姆和希音为代表的中国跨境电商平台在全球范围内高速发展。有观点认为，中国跨境电商的迅速崛起为东道国既有电商平台带来“不公平”竞争。本文则发现：中国跨境电商平台与东道国既有平台之间并非零和博弈关系。由于所依托的供应商不同，中外平台各自提供具有不同比较优势的商品，双方有望基于比较优势差异，实现“互利共赢”。本文构建理论模型证明，中国跨境电商平台不仅为东道国送去其不具有比较优势的商品，亦通过“收入溢出”与“信息溢出”双重效应促进东道国既有电商平台发展，实现市场总规模扩张。实证方面，基于覆盖68个国家和地区、434个城市的千万级电商平台监测数据，本文发现中国平台进入后，东道国既有电商平台的访问量、支付人数与用户评分等均显著提升，验证了理论模型的推断。本研究揭示了平台经济跨境贸易的独特属性，强调了发挥比较优势在出海过程中的重要作用，并为“经贸关系的本质是互利共赢”这一重要论断提供了新的理论注解与经验证据。","abstractEn":"","pages":"79-95","doi":"10.19744/j.cnki.11-1235/f.2026.0079","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=Y2ZXZUoycEtHd1pDcktYWjhVb0xuQ2VHYzllelYwbVJQQitjTlpsY3dycHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"钟洲, 陈思明, 王勇, 张梓桐. 互利共赢还是零和博弈：跨境电商平台出海如何影响东道国既有平台？[J]. 管理世界, 2026(6): 79-95.","lenZh":408,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026008009","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"斯宾诺莎与17世纪的国际法问题：一个力量形而上学的视角","summary":"长期以来，斯宾诺莎在国际法研究中一直处于边缘地位。学界认为，斯宾诺莎以“权利即力量”的自然法理论否定了格劳秀斯式的道德自然法，将战争、盟约与国际关系置于自然状态的力量逻辑之中，从而在事实上否定了国际法。然而，本文通过考察斯宾诺莎的自然法理论、主权理论与力量形而上学，力图指出，斯宾诺莎所否定的只是以道德自然法为基础的国际法，而非国际法本身。在斯宾诺莎那里，主权是“无辜”的，不受格劳秀斯式道德法庭的审判，但国家作为有限力量的集合，仍然受制于外部力量关系、自我保存与理性判断，因此必须在维护国家利益的前提下，服从理性的国际法。在这个意义上，斯宾诺莎以一种崭新的方式为现代国际法奠定了基础。这有助于我们超越主流的国际法范式，以一种完全自主的方式构建既符合主权又符合道德的国际新秩序。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026008009","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"8","authors":["吴功青","徐陆圆"],"affils":["中国人民大学哲学院"],"title":"斯宾诺莎与17世纪的国际法问题：一个力量形而上学的视角","titleEn":"Spinoza and the Problem of International Law in the 17th Century: A Perspective from the Metaphysics of Power","keywords":["斯宾诺莎","格劳秀斯","国际法","自然法","力量形而上学"],"keywordsEn":[],"abstract":"长期以来，斯宾诺莎在国际法研究中一直处于边缘地位。学界认为，斯宾诺莎以“权利即力量”的自然法理论否定了格劳秀斯式的道德自然法，将战争、盟约与国际关系置于自然状态的力量逻辑之中，从而在事实上否定了国际法。然而，本文通过考察斯宾诺莎的自然法理论、主权理论与力量形而上学，力图指出，斯宾诺莎所否定的只是以道德自然法为基础的国际法，而非国际法本身。在斯宾诺莎那里，主权是“无辜”的，不受格劳秀斯式道德法庭的审判，但国家作为有限力量的集合，仍然受制于外部力量关系、自我保存与理性判断，因此必须在维护国家利益的前提下，服从理性的国际法。在这个意义上，斯宾诺莎以一种崭新的方式为现代国际法奠定了基础。这有助于我们超越主流的国际法范式，以一种完全自主的方式构建既符合主权又符合道德的国际新秩序。","abstractEn":"","pages":"100-112","doi":"10.13644/j.cnki.cn31-1112.2026.08.014","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=Z0kxREJGL3hIbUExOXRNQmxNTnB4QThaWkZWMFZOYlU4WVZxcEc3MUVZdHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"吴功青, 徐陆圆. 斯宾诺莎与17世纪的国际法问题：一个力量形而上学的视角[J]. 社会科学, 2026(8): 100-112.","lenZh":339,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026008010","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"斯宾诺莎的双重形象与知识学的建构难题——以费希特1801/1802年《知识学阐述》为中心","summary":"斯宾诺莎哲学是费希特知识学始终要面对的“磨刀石”：一方面，斯宾诺莎哲学在体系建构方面为费希特提供了绝佳的样板；另一方面，其决定论色彩又是费希特亟须化解的难题。在耶拿时期，费希特试图以实践的方式解释斯宾诺莎超出自我、寻求知识最高本原的动机。到了1801年之后的柏林时期，费希特则将自我之上的绝对者引入体系建构，从而与斯宾诺莎哲学之间具有更为明显的亲缘性。为了化解决定论与宿命论的困境，费希特1801/1802年的知识学以形式自由作为实体与偶性的中介，以此确保精神世界的自发性和独立性。费希特的这一方案不仅批判了斯宾诺莎，还把矛头直指谢林的同一哲学。理解费希特此时对斯宾诺莎的暧昧态度，有助于我们更清晰地梳理德国古典哲学发展进程中日益白热化的思想争辩。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026008010","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"8","authors":["周小龙"],"affils":["中山大学哲学系"],"title":"斯宾诺莎的双重形象与知识学的建构难题——以费希特1801/1802年《知识学阐述》为中心","titleEn":"Spinoza's Double Image and the Constructive Problem of the Wissenschaftslehre: Focusing on Fichte's 1801/1802 Presentation of the Wissenschaftslehre","keywords":["费希特","斯宾诺莎","1801/1802知识学","决定论","形式自由"],"keywordsEn":[],"abstract":"斯宾诺莎哲学是费希特知识学始终要面对的“磨刀石”：一方面，斯宾诺莎哲学在体系建构方面为费希特提供了绝佳的样板；另一方面，其决定论色彩又是费希特亟须化解的难题。在耶拿时期，费希特试图以实践的方式解释斯宾诺莎超出自我、寻求知识最高本原的动机。到了1801年之后的柏林时期，费希特则将自我之上的绝对者引入体系建构，从而与斯宾诺莎哲学之间具有更为明显的亲缘性。为了化解决定论与宿命论的困境，费希特1801/1802年的知识学以形式自由作为实体与偶性的中介，以此确保精神世界的自发性和独立性。费希特的这一方案不仅批判了斯宾诺莎，还把矛头直指谢林的同一哲学。理解费希特此时对斯宾诺莎的暧昧态度，有助于我们更清晰地梳理德国古典哲学发展进程中日益白热化的思想争辩。","abstractEn":"","pages":"113-123","doi":"10.13644/j.cnki.cn31-1112.2026.08.016","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=U0dZZDBaMkZyWlNvN3RMSEtjRGtzTFNZN052dWJhTGZGYTVMUThPZ3FuNXZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"周小龙. 斯宾诺莎的双重形象与知识学的建构难题——以费希特1801/1802年《知识学阐述》为中心[J]. 社会科学, 2026(8): 113-123.","lenZh":324,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026008011","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"秦汉“更卒”新释","summary":"秦汉时期的“更卒”指轮番执役的卒，既包括一年一更的戍卒、屯卒，也包括一年数更、在县中执役的县卒。更卒之役包括兵役、职役，并非以劳役为主，其役期固定，到期轮替。《南郡卒更簿》所记各县“月用卒”，是各县常年应当役使的卒数，包括各种县卒（求盗、亭卒）、邮人、船人以及在县廷诸官曹轮流执役的役卒。各县“定更卒”由“甲卒”充任，轮番执役，一年中的役期在西汉前中期由一月余至四五个月不等，西汉末年至东汉时期确定为一个月。“徭”是官府因事临时征发的各种使役，诸如运输、工程兴修等劳役，没有明确的役期。举凡“事口”（包括大男、小男、大女、小女）均需应征“行徭”。秦汉时期的“役”，即由“更”“徭”两部分构成，二者的根本区别在于：“更”是轮流执役，是恒役（常役）；“徭”是因事征发，是临时役。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026008011","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"8","authors":["鲁西奇"],"affils":["复旦大学历史学系"],"title":"秦汉“更卒”新释","titleEn":"A New Interpretation of the \"Geng-zu\" in the Qin and Han Dynasties","keywords":["更卒","徭役","恒役","临时役","秦汉"],"keywordsEn":[],"abstract":"秦汉时期的“更卒”指轮番执役的卒，既包括一年一更的戍卒、屯卒，也包括一年数更、在县中执役的县卒。更卒之役包括兵役、职役，并非以劳役为主，其役期固定，到期轮替。《南郡卒更簿》所记各县“月用卒”，是各县常年应当役使的卒数，包括各种县卒（求盗、亭卒）、邮人、船人以及在县廷诸官曹轮流执役的役卒。各县“定更卒”由“甲卒”充任，轮番执役，一年中的役期在西汉前中期由一月余至四五个月不等，西汉末年至东汉时期确定为一个月。“徭”是官府因事临时征发的各种使役，诸如运输、工程兴修等劳役，没有明确的役期。举凡“事口”（包括大男、小男、大女、小女）均需应征“行徭”。秦汉时期的“役”，即由“更”“徭”两部分构成，二者的根本区别在于：“更”是轮流执役，是恒役（常役）；“徭”是因事征发，是临时役。","abstractEn":"","pages":"124-140","doi":"10.13644/j.cnki.cn31-1112.2026.08.007","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=U0dZZDBaMkZyWlNvN3RMSEtjRGtzRVBNRkNROURIQ0pCWXRlRmVtZFl5RnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"鲁西奇. 秦汉“更卒”新释[J]. 社会科学, 2026(8): 124-140.","lenZh":337,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026008012","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"里制变迁与秦汉基层管控模式演变","summary":"在周秦间的社会变革中，各国普遍尝试建立一套以户籍为基础，国家对民户进行层层编排与直接支配的新体制，里在其中表现为一级户口编组单位。而秦在借鉴各国制度的基础上，自上而下地设计了一套严密的国家管控体制，该体制在基层以户籍、授田宅和里制为中心，里在其中表现为行政单位与聚落形态的紧密结合。然而，受制于政府的行政能力，这套体制实际并没有得到完全的贯彻，里也因此呈现出了多样化的特征。汉承秦制，汉朝在最初制度建构上承袭了秦的管控体制，但授田宅制度在汉初就难以为继，里制也逐渐由封闭的聚落形态转变为单纯的户籍编组单位，政府无法再对民众生产生活空间进行严格的限制，因而不得不依靠地方势力构建新的统治秩序，国家对基层的管控模式遂发生转变，由政府单一主导的、严密的管控模式逐渐过渡到了政府和地方势力多重主导的、相对松散的管控模式。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026008012","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"8","authors":["卞贺"],"affils":["厦门大学历史与文化遗产学院"],"title":"里制变迁与秦汉基层管控模式演变","titleEn":"The Evolution of the Li System and Grassroots Governance Modes in the Qin and Han Dynasties","keywords":["里制","聚落形态","户籍编组","基层管控"],"keywordsEn":[],"abstract":"在周秦间的社会变革中，各国普遍尝试建立一套以户籍为基础，国家对民户进行层层编排与直接支配的新体制，里在其中表现为一级户口编组单位。而秦在借鉴各国制度的基础上，自上而下地设计了一套严密的国家管控体制，该体制在基层以户籍、授田宅和里制为中心，里在其中表现为行政单位与聚落形态的紧密结合。然而，受制于政府的行政能力，这套体制实际并没有得到完全的贯彻，里也因此呈现出了多样化的特征。汉承秦制，汉朝在最初制度建构上承袭了秦的管控体制，但授田宅制度在汉初就难以为继，里制也逐渐由封闭的聚落形态转变为单纯的户籍编组单位，政府无法再对民众生产生活空间进行严格的限制，因而不得不依靠地方势力构建新的统治秩序，国家对基层的管控模式遂发生转变，由政府单一主导的、严密的管控模式逐渐过渡到了政府和地方势力多重主导的、相对松散的管控模式。","abstractEn":"","pages":"141-151","doi":"10.13644/j.cnki.cn31-1112.2026.08.005","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=U0dZZDBaMkZyWlNvN3RMSEtjRGtzTnk2SFIzZG02UHA1V0kxa09OclJadHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"卞贺. 里制变迁与秦汉基层管控模式演变[J]. 社会科学, 2026(8): 141-151.","lenZh":356,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026008013","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"从历史化叙事到人间感叙事——论新大众文艺的现实性","summary":"新大众文艺的现实性是人间感叙事创造出来的现实性。它延续了现代大众文艺，尤其是延安文艺的人民性内涵，又延展和拓宽了这个内涵。从历史化叙事到人间感叙事，新大众文艺实现了从“服务于大众的文艺”到“大众自己创造的文艺”的转变，从塑造符合特定文化规范的现实世界到洋溢有血有肉的鲜活生活的转变。由此，新大众文艺呈现出一种“人间感”叙事的特色，一方面表达现实生活中人们的琐碎情绪和瞬间感受，充满“活人气”；另一方面则立足微末小人物的生活经验，表达卑微、渺小、脆弱背后挣命的坚强和奋进的勇力。新时期以来确立的“人的文学”让渡于“尘中人”为核心的“人间文学”。“活人气”“尘中人”和“此在性”交织构成新大众文艺“人间感”的三个层面。新大众文艺借此将现实感建立在人间感的基础之上，将宏大的故事叠加到每个人肩头的重量之上，构建一种新的通达鲜活生活的现实性。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026008013","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"8","authors":["周志强","何青红"],"affils":["南开大学文学院"],"title":"从历史化叙事到人间感叙事——论新大众文艺的现实性","titleEn":"From Historicized Narrative to Narrative of the \"Sense of Lived Humanity\"——On the Reality of New Popular Arts","keywords":["新大众文艺","人间感","现实性"],"keywordsEn":[],"abstract":"新大众文艺的现实性是人间感叙事创造出来的现实性。它延续了现代大众文艺，尤其是延安文艺的人民性内涵，又延展和拓宽了这个内涵。从历史化叙事到人间感叙事，新大众文艺实现了从“服务于大众的文艺”到“大众自己创造的文艺”的转变，从塑造符合特定文化规范的现实世界到洋溢有血有肉的鲜活生活的转变。由此，新大众文艺呈现出一种“人间感”叙事的特色，一方面表达现实生活中人们的琐碎情绪和瞬间感受，充满“活人气”；另一方面则立足微末小人物的生活经验，表达卑微、渺小、脆弱背后挣命的坚强和奋进的勇力。新时期以来确立的“人的文学”让渡于“尘中人”为核心的“人间文学”。“活人气”“尘中人”和“此在性”交织构成新大众文艺“人间感”的三个层面。新大众文艺借此将现实感建立在人间感的基础之上，将宏大的故事叠加到每个人肩头的重量之上，构建一种新的通达鲜活生活的现实性。","abstractEn":"","pages":"152-159","doi":"10.13644/j.cnki.cn31-1112.2026.08.013","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=U0dZZDBaMkZyWlNvN3RMSEtjRGtzSHhmejlCU1dOZGtQNEdkcG5kdUUwbHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"周志强, 何青红. 从历史化叙事到人间感叙事——论新大众文艺的现实性[J]. 社会科学, 2026(8): 152-159.","lenZh":367,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026008002","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"以多维嵌入提升文商旅联动机制：国际潮流与前瞻对策","summary":"党中央、国务院作出了扩大服务消费和促进文化旅游业发展的重要部署。文化、旅游、商业属于国民经济行业分类的不同板块，它们之间的联动不会自然而然地产生，必须通过多维的嵌入机制实现：文化嵌入，聚焦于人文引领和价值提升，丰富人们的游历体验和消费内涵；社会嵌入，聚焦互动网络和管理合作，以优质IP为核心建立跨界流通机制；空间嵌入，聚焦各类业态的空间耦合和布局动线，破解文化街区“过度商业化”或商业圈“文化味不足”的难题；集群嵌入，聚焦不同企业的混业集聚和创新扩散，带动文商旅产业的集约化发展。从国际潮流看，上海建设国际消费中心城市，要在逻辑理路和实践进路的结合上作出前瞻应对，在高阶人文引领、开发顶流IP、加强协调提升等方面持续跃升。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026008002","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"8","authors":["花建"],"affils":["上海社会科学院文化产业研究中心、文学研究所"],"title":"以多维嵌入提升文商旅联动机制：国际潮流与前瞻对策","titleEn":"Boosting the Synergy of Culture, Commerce, and Tourism Through Multidimensional Embedding: International Trends and Forward-Looking Strategies","keywords":["多维嵌入","文商旅","联动机制","国际潮流","上海贡献"],"keywordsEn":[],"abstract":"党中央、国务院作出了扩大服务消费和促进文化旅游业发展的重要部署。文化、旅游、商业属于国民经济行业分类的不同板块，它们之间的联动不会自然而然地产生，必须通过多维的嵌入机制实现：文化嵌入，聚焦于人文引领和价值提升，丰富人们的游历体验和消费内涵；社会嵌入，聚焦互动网络和管理合作，以优质IP为核心建立跨界流通机制；空间嵌入，聚焦各类业态的空间耦合和布局动线，破解文化街区“过度商业化”或商业圈“文化味不足”的难题；集群嵌入，聚焦不同企业的混业集聚和创新扩散，带动文商旅产业的集约化发展。从国际潮流看，上海建设国际消费中心城市，要在逻辑理路和实践进路的结合上作出前瞻应对，在高阶人文引领、开发顶流IP、加强协调提升等方面持续跃升。","abstractEn":"","pages":"16-27","doi":"10.13644/j.cnki.cn31-1112.2026.08.015","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=Z0kxREJGL3hIbUExOXRNQmxNTnB4Tnk2SFIzZG02UHA1V0kxa09OclJadHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"花建. 以多维嵌入提升文商旅联动机制：国际潮流与前瞻对策[J]. 社会科学, 2026(8): 16-27.","lenZh":311,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026008014","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"境启情生：新大众文艺的媒介逻辑与经验模式","summary":"新大众文艺的形成是人们日常生活与技术变革高度融合的结果。程序规则、平台算法、界面操作等媒介革新共同构建了新大众文艺的经验场域。数字媒介通过“仿境”机制重构现实经验，推动文艺活动从具身经验向拟身参与延展，文艺叙事从现实再现向氛围生成转移。在多重感知要素协同作用的机制中，大众情感表达转化为可以重复生产与广泛调动的结构性资源，推动新大众文艺形成开放、多元且高度互动的特质。受众依托数字媒介，在多维交互中实现个体经验的共感。生成式人工智能广泛介入内容生产后，情感表达进一步趋于模板化、均质化与浅表化，需重新审视数字媒介时代人与技术、世界的三者关系，反思当代大众文化情感治理的未来走向等。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026008014","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"8","authors":["邱慧婷"],"affils":["广西师范大学文学院"],"title":"境启情生：新大众文艺的媒介逻辑与经验模式","titleEn":"Where Milieu Evokes Emotion: The Media Logic and Experiential Modes of New Popular Arts","keywords":["新大众文艺","仿境","拟身","氛围","共感"],"keywordsEn":[],"abstract":"新大众文艺的形成是人们日常生活与技术变革高度融合的结果。程序规则、平台算法、界面操作等媒介革新共同构建了新大众文艺的经验场域。数字媒介通过“仿境”机制重构现实经验，推动文艺活动从具身经验向拟身参与延展，文艺叙事从现实再现向氛围生成转移。在多重感知要素协同作用的机制中，大众情感表达转化为可以重复生产与广泛调动的结构性资源，推动新大众文艺形成开放、多元且高度互动的特质。受众依托数字媒介，在多维交互中实现个体经验的共感。生成式人工智能广泛介入内容生产后，情感表达进一步趋于模板化、均质化与浅表化，需重新审视数字媒介时代人与技术、世界的三者关系，反思当代大众文化情感治理的未来走向等。","abstractEn":"","pages":"160-170","doi":"10.13644/j.cnki.cn31-1112.2026.08.010","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=U0dZZDBaMkZyWlNvN3RMSEtjRGtzR3U5ZmdSRWRRbk9zTTE0YXl6QjdQWnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"邱慧婷. 境启情生：新大众文艺的媒介逻辑与经验模式[J]. 社会科学, 2026(8): 160-170.","lenZh":290,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026008015","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"数字经济竞争商业道德标准的重塑：以用户为中心","summary":"商业道德作为反不正当竞争法一般条款的关键标尺，其适用效果直接决定着数字经济时代市场竞争秩序的规范与创新活力的激发。数字经济催生数据竞争、算法竞争等新型竞争形态，但传统商业道德标准以经营者为主视角，以同业诚信和行业惯例等为主要依据，忽视用户的主体地位与合法权益，导致商业道德标准在新型不正当竞争纠纷中陷入适用困境，难以完全适配数字经济价值共生的需求。从工业经济时代到数字经济时代，用户已从被动消费主体转变为价值共创主体，商业道德向用户中心转型符合理论机理与历史趋势。不应将用户作为竞争资源，而宜使其复归市场的形成主体地位，以此塑造以用户为中心的商业道德标准。以用户为中心不是片面强调用户利益优先，而是以用户与经营者的共同长期利益为核心，塑造双向尊重、责任共担、动态迭代的共生伦理，在此基础上可进一步优化立法、司法、执法三维协同机制。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026008015","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"8","authors":["黄尹旭"],"affils":["中国人民大学法学院","中国人民大学人工智能治理研究院"],"title":"数字经济竞争商业道德标准的重塑：以用户为中心","titleEn":"Reshaping Business Ethics Standards for Competition in the Digital Economy: A User-Centric Perspective","keywords":["反不正当竞争","商业道德","用户中心","数字经济","共生伦理"],"keywordsEn":[],"abstract":"商业道德作为反不正当竞争法一般条款的关键标尺，其适用效果直接决定着数字经济时代市场竞争秩序的规范与创新活力的激发。数字经济催生数据竞争、算法竞争等新型竞争形态，但传统商业道德标准以经营者为主视角，以同业诚信和行业惯例等为主要依据，忽视用户的主体地位与合法权益，导致商业道德标准在新型不正当竞争纠纷中陷入适用困境，难以完全适配数字经济价值共生的需求。从工业经济时代到数字经济时代，用户已从被动消费主体转变为价值共创主体，商业道德向用户中心转型符合理论机理与历史趋势。不应将用户作为竞争资源，而宜使其复归市场的形成主体地位，以此塑造以用户为中心的商业道德标准。以用户为中心不是片面强调用户利益优先，而是以用户与经营者的共同长期利益为核心，塑造双向尊重、责任共担、动态迭代的共生伦理，在此基础上可进一步优化立法、司法、执法三维协同机制。","abstractEn":"","pages":"171-180","doi":"10.13644/j.cnki.cn31-1112.2026.08.012","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=U0dZZDBaMkZyWlNvN3RMSEtjRGtzQ2VHYzllelYwbVJQQitjTlpsY3dycHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"黄尹旭. 数字经济竞争商业道德标准的重塑：以用户为中心[J]. 社会科学, 2026(8): 171-180.","lenZh":365,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026008016","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"数字社会信用法治的理论重构与制度展开","summary":"数字化转型作为当前关键的社会变革力量，正以前所未有的广度和深度塑造着经济社会的运行逻辑。在此背景下，数字社会的信用法治形成了以信息化为外在表征、以跨场域实践为核心特征、以公共信用与市场化信用的协同运行为底层框架的基本形态。其以“信用共享共治”为基本价值导向，旨在保障个体在数字社会中持续参与、获得机会并实现充分发展的可能性。内生信任是数字社会信用法治机理的深层维度，也是数字社会中支撑合作秩序的核心社会关系。为此，应构建基础公共服务和市场化增值服务互补协同的数字信用生态，发挥制度的规范调适功能，塑造能够持续孕育内生信任的协同信用秩序。在具体机制设计层面，应以比例原则为中心，重新界定政府信用管理职能，在此基础上建立信息归集评价环节的双系统运行机制，信用应用环节的后果分级和联结审查机制，以及信用救济环节的跨领域修复传导机制。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026008016","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"8","authors":["杨帆"],"affils":["上海师范大学哲学与法政学院"],"title":"数字社会信用法治的理论重构与制度展开","titleEn":"Theoretical Reconstruction and Institutional Development of the Legal Framework for Digital Social Credit Systems","keywords":["信用法治","公共信用","市场化信用","数字信用","法治政府"],"keywordsEn":[],"abstract":"数字化转型作为当前关键的社会变革力量，正以前所未有的广度和深度塑造着经济社会的运行逻辑。在此背景下，数字社会的信用法治形成了以信息化为外在表征、以跨场域实践为核心特征、以公共信用与市场化信用的协同运行为底层框架的基本形态。其以“信用共享共治”为基本价值导向，旨在保障个体在数字社会中持续参与、获得机会并实现充分发展的可能性。内生信任是数字社会信用法治机理的深层维度，也是数字社会中支撑合作秩序的核心社会关系。为此，应构建基础公共服务和市场化增值服务互补协同的数字信用生态，发挥制度的规范调适功能，塑造能够持续孕育内生信任的协同信用秩序。在具体机制设计层面，应以比例原则为中心，重新界定政府信用管理职能，在此基础上建立信息归集评价环节的双系统运行机制，信用应用环节的后果分级和联结审查机制，以及信用救济环节的跨领域修复传导机制。","abstractEn":"","pages":"181-192","doi":"10.13644/j.cnki.cn31-1112.2026.08.008","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=U0dZZDBaMkZyWlNvN3RMSEtjRGtzTXZKd0FXd0RmeW84MzV2VlQrcTFqOXZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"杨帆. 数字社会信用法治的理论重构与制度展开[J]. 社会科学, 2026(8): 181-192.","lenZh":363,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026008003","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"“电竞+文旅”何以可能与何以可为：消费与文化社会学视角的分析","summary":"作为新兴体育的重要组成部分，电竞继承了传统体育文化的社交属性与情感链接的特质。近年来，电竞与文旅的融合在实践中逐渐突破“赛事+景点”的简单叠加模式，发展至生态深度互嵌的新阶段。“电竞+文旅”消费呈现“电竞赛事衍生型”“圈层会展嵌套型”“跨界场景融合型”与“城市体验嵌入型”四种类型。这种融合型消费之所以可能，可从三个维度加以理解：在文化的结构性视角下，现代化城市建设与扩大消费等战略为其提供了制度机遇，青年休闲生活方式的变迁则提供了社会文化基础；在文化的社会性视角下，意义构建与情感满足难以仅凭个体实现，而必须依赖于个体与他人、群体乃至社会大众的连接；在文化的物质性视角下，该消费的兴起离不开城市物质供给体系的支撑。就发展机制而言，城市政策布局、社会文化环境与数字新媒体发展构成宏观外部条件，青年尤其是大学生较高的经济自主权构成微观结构性因素，业态融合创新与城市空间精准规划则为融合性消费提供了物质载体与场景依托，在此基础上，青年主动参与消费实践、社交互动与内容生产，成为文化意义与消费业态的产消共创者。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026008003","journal":"社会科学","journalEn":"Journal of Social 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28-37.","lenZh":452,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026008004","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"从循证实践到条件性推演：AI时代社会工作干预研究的方法论重构","summary":"社会工作干预研究长期受制于小样本困境与解释—设计断裂，难以在干预实施前比较方案、识别机制并检验风险。生成式人工智能与多主体仿真的结合，为田野材料转化为可计算行动规则、进而开展前瞻性方案推演提供了新的方法论可能。人工智能时代干预研究的关键变化，不是工具升级，而是证据生产的认识论重构：从数据压缩转向情境保留，从样本外推转向机制仿真，从事后评估转向前瞻设计。基于此，条件性推演证据作为一种以田野材料为基准、在明确假设下通过反事实推演形成的新型证据，其核心特征包括可追溯性、条件显明性与机制透明性。以方法论透明性作为质量评估原则，有助于将实践中的隐性知识转化为可审计、可检验的知识形态。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026008004","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"8","authors":["郭茂灿","姚红"],"affils":["云南大学民族学与社会学学院","中央民族大学民族学与社会学学院"],"title":"从循证实践到条件性推演：AI时代社会工作干预研究的方法论重构","titleEn":"From Evidence-Based Practice to Conditional Inference: Methodological Reconstruction of Social Work Intervention Research in the Age of AI","keywords":["社会工作干预研究","循证实践","条件性推演证据","生成式人工智能","方法论透明性"],"keywordsEn":[],"abstract":"社会工作干预研究长期受制于小样本困境与解释—设计断裂，难以在干预实施前比较方案、识别机制并检验风险。生成式人工智能与多主体仿真的结合，为田野材料转化为可计算行动规则、进而开展前瞻性方案推演提供了新的方法论可能。人工智能时代干预研究的关键变化，不是工具升级，而是证据生产的认识论重构：从数据压缩转向情境保留，从样本外推转向机制仿真，从事后评估转向前瞻设计。基于此，条件性推演证据作为一种以田野材料为基准、在明确假设下通过反事实推演形成的新型证据，其核心特征包括可追溯性、条件显明性与机制透明性。以方法论透明性作为质量评估原则，有助于将实践中的隐性知识转化为可审计、可检验的知识形态。","abstractEn":"","pages":"38-50","doi":"10.13644/j.cnki.cn31-1112.2026.08.004","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=Z0kxREJGL3hIbUExOXRNQmxNTnB4R3U5ZmdSRWRRbk9zTTE0YXl6QjdQWnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"郭茂灿, 姚红. 从循证实践到条件性推演：AI时代社会工作干预研究的方法论重构[J]. 社会科学, 2026(8): 38-50.","lenZh":290,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026008001","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"文商旅体展融合发展的理论意涵与创新路径","summary":"文商旅体展融合发展是助推服务业扩能提质、激活城市消费新动能的重要路径。明晰融合发展的核心特征、底层逻辑与动力机制，对推动产业高质量发展、完善现代服务业体系具有重要意义。融合发展的核心要义在于打破业态条块分割的发展桎梏，实现要素跨界流动、场景深度创新与价值协同共创。当前，文商旅体展融合发展的进程仍面临机制协同不畅、收益核算不清、要素流通阻滞、流量变现乏力及场景创新不足等多重结构性梗阻。厘清融合发展的核心堵点与关键难点，构建适配多业态融合规律的系统性推进路径，可为新时代文商旅体展高质量融合提供理论支撑与实践指引。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026008001","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"8","authors":["冯学钢","黄文杰"],"affils":["华东师范大学经济与管理学院"],"title":"文商旅体展融合发展的理论意涵与创新路径","titleEn":"Theoretical Connotations and Innovative Paths of the Integrated Development of Culture, Business, Tourism, Sports and Exhibition","keywords":["文商旅体展融合发展","价值取向","动力机制","实践路径"],"keywordsEn":[],"abstract":"文商旅体展融合发展是助推服务业扩能提质、激活城市消费新动能的重要路径。明晰融合发展的核心特征、底层逻辑与动力机制，对推动产业高质量发展、完善现代服务业体系具有重要意义。融合发展的核心要义在于打破业态条块分割的发展桎梏，实现要素跨界流动、场景深度创新与价值协同共创。当前，文商旅体展融合发展的进程仍面临机制协同不畅、收益核算不清、要素流通阻滞、流量变现乏力及场景创新不足等多重结构性梗阻。厘清融合发展的核心堵点与关键难点，构建适配多业态融合规律的系统性推进路径，可为新时代文商旅体展高质量融合提供理论支撑与实践指引。","abstractEn":"","pages":"5-15","doi":"10.13644/j.cnki.cn31-1112.2026.08.011","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=Z0kxREJGL3hIbUExOXRNQmxNTnB4RVBNRkNROURIQ0pCWXRlRmVtZFl5RnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"冯学钢, 黄文杰. 文商旅体展融合发展的理论意涵与创新路径[J]. 社会科学, 2026(8): 5-15.","lenZh":257,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026008005","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"工作稳定性如何降低健康不平等？——基于“中国社会状况综合调查（2023）”的多重中介分析","summary":"促进高质量充分就业，是新时代新征程就业工作的新定位、新使命。本文将工作稳定性视为高质量充分就业的重要维度，依据健康不平等理论整合了工作稳定性影响健康的多重解释路径。研究采用“中国社会状况综合调查（2023）”中非农业就业劳动力样本的数据，从多个维度构建了工作稳定性指数，分析发现：工作越稳定者，其自评健康水平越高。主要路径表现为：稳定就业增加了劳动者的收入，同时降低了劳动者的日常生活压力和健康行为约束等，从而提升了他们的自评健康水平，其中日常生活压力的中介解释力最强。进一步分析发现，工作稳定性对不同性别、户口、教育程度和阶层地位劳动者自评健康的影响是广泛的。系统提升劳动者就业稳定性，推动实现更高质量、更充分的就业发展目标，有助于改善劳动者身心健康状况，并进一步增强广大劳动者的获得感、幸福感、安全感。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026008005","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"8","authors":["王甫勤"],"affils":["同济大学政治与国际关系学院","同济大学中国特色社会主义理论研究中心"],"title":"工作稳定性如何降低健康不平等？——基于“中国社会状况综合调查（2023）”的多重中介分析","titleEn":"How Does Job Stability Reduce Health Inequality? A Multiple Mediation Analysis Based on Data from the Chinese Social Survey (2023)","keywords":["高质量充分就业","工作稳定性","健康不平等","不稳定就业"],"keywordsEn":[],"abstract":"促进高质量充分就业，是新时代新征程就业工作的新定位、新使命。本文将工作稳定性视为高质量充分就业的重要维度，依据健康不平等理论整合了工作稳定性影响健康的多重解释路径。研究采用“中国社会状况综合调查（2023）”中非农业就业劳动力样本的数据，从多个维度构建了工作稳定性指数，分析发现：工作越稳定者，其自评健康水平越高。主要路径表现为：稳定就业增加了劳动者的收入，同时降低了劳动者的日常生活压力和健康行为约束等，从而提升了他们的自评健康水平，其中日常生活压力的中介解释力最强。进一步分析发现，工作稳定性对不同性别、户口、教育程度和阶层地位劳动者自评健康的影响是广泛的。系统提升劳动者就业稳定性，推动实现更高质量、更充分的就业发展目标，有助于改善劳动者身心健康状况，并进一步增强广大劳动者的获得感、幸福感、安全感。","abstractEn":"","pages":"51-64","doi":"10.13644/j.cnki.cn31-1112.2026.08.006","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=Z0kxREJGL3hIbUExOXRNQmxNTnB4Q2VHYzllelYwbVJQQitjTlpsY3dycHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"王甫勤. 工作稳定性如何降低健康不平等？——基于“中国社会状况综合调查（2023）”的多重中介分析[J]. 社会科学, 2026(8): 51-64.","lenZh":353,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026008006","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"从革命传统到当代治理：中国共产党治理传统的当代转化","summary":"中国共产党在革命与建设时期形成的治理方法广泛作用于当代国家治理实践。借鉴渐进制度变迁理论分析框架，立足中国共产党治理实践的内生逻辑，从“革命传统的存续形态”与“治理问题的延续形态”两个维度出发，构建一个分析革命传统延续性及其发挥作用方式的类型学框架，并概括出制度内调适、经验复制、功能替代与差异化激活四种作用机制，这四大机制以不同的方式持续影响着当代国家治理的制度与实践。通过考察“干部监督”与“地方治理”两项国家治理实践及其延续机制，本文认为，革命传统之所以能够在当代治理中持续发挥作用，并非简单的历史延续，而是在变化的社会环境、治理任务与实践场景中不断被重新组织、转换与激活，由此形成革命传统在当代治理中多样化的形态。通过带回“革命传统”，为理解中国共产党治理经验的历史连续性提供了一个兼具学理深度与经验基础的中观分析视角和理论框架，也为未来相关研究提供了可拓展的若干方向与核心议题。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026008006","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"8","authors":["陈立夫","张长东"],"affils":["华中师范大学政治学与国家治理研究院(中国农村研究院)","北京大学政府管理学院"],"title":"从革命传统到当代治理：中国共产党治理传统的当代转化","titleEn":"From Revolutionary Traditions to Contemporary Governance: The Contemporary Transformation of the Governance Traditions of the Communist Party of China","keywords":["历史持续性","中国共产党","革命传统","治理方法"],"keywordsEn":[],"abstract":"中国共产党在革命与建设时期形成的治理方法广泛作用于当代国家治理实践。借鉴渐进制度变迁理论分析框架，立足中国共产党治理实践的内生逻辑，从“革命传统的存续形态”与“治理问题的延续形态”两个维度出发，构建一个分析革命传统延续性及其发挥作用方式的类型学框架，并概括出制度内调适、经验复制、功能替代与差异化激活四种作用机制，这四大机制以不同的方式持续影响着当代国家治理的制度与实践。通过考察“干部监督”与“地方治理”两项国家治理实践及其延续机制，本文认为，革命传统之所以能够在当代治理中持续发挥作用，并非简单的历史延续，而是在变化的社会环境、治理任务与实践场景中不断被重新组织、转换与激活，由此形成革命传统在当代治理中多样化的形态。通过带回“革命传统”，为理解中国共产党治理经验的历史连续性提供了一个兼具学理深度与经验基础的中观分析视角和理论框架，也为未来相关研究提供了可拓展的若干方向与核心议题。","abstractEn":"","pages":"65-75","doi":"10.13644/j.cnki.cn31-1112.2026.08.002","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=Z0kxREJGL3hIbUExOXRNQmxNTnB4TXZKd0FXd0RmeW84MzV2VlQrcTFqOXZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"陈立夫, 张长东. 从革命传统到当代治理：中国共产党治理传统的当代转化[J]. 社会科学, 2026(8): 65-75.","lenZh":394,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026008007","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"数智时代资本主义的“数字塞壬”宰制与破解","summary":"数智时代算法技术与资本逻辑的深度融合催生了一种“数字塞壬”宰制机制，即新型时间殖民机制。区别于“数字异化”“数字统治”，“数字塞壬”具有“赋魅的诱惑性”“抵抗的无力性”等特征，使主体在知情状态下仍难以挣脱被宰制的困境，甚至陷入一种明知故犯的“享受”宰制的状态。资本主义的“数字塞壬”宰制根源于资本逻辑从生产性向掠夺性演变、技术统治从工具理性向意识形态神话蜕化以及生产关系从契约劳动向封建式人身依附退行。因此，对主体的宰制造成三重时间殖民后果：对时间量的掠夺、质的侵蚀、社会历史属性的割裂与重构。同时，这种宰制结构蕴含着自我否定的潜能，即时间占有的悖论推动政治觉醒，伪主体性的消解催生批判意识，而一般智力的共有化趋势则为超越私人占有逻辑和重建时间主权提供了可能进路。破除“数字塞壬”宰制的关键不是拒斥人工智能技术本身，而是使其摆脱资本逻辑的禁锢，从而真正成为人的自由全面发展的解放力量。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026008007","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"8","authors":["刘日明","毛以静"],"affils":["同济大学人文学院哲学系","同济大学技术与未来研究院"],"title":"数智时代资本主义的“数字塞壬”宰制与破解","titleEn":"The Domination and Countermeasures of the \"Digital Siren\" of Capitalism in the Era of Digital Intelligence","keywords":["数字塞壬","时间殖民","数智时代资本主义","一般智力"],"keywordsEn":[],"abstract":"数智时代算法技术与资本逻辑的深度融合催生了一种“数字塞壬”宰制机制，即新型时间殖民机制。区别于“数字异化”“数字统治”，“数字塞壬”具有“赋魅的诱惑性”“抵抗的无力性”等特征，使主体在知情状态下仍难以挣脱被宰制的困境，甚至陷入一种明知故犯的“享受”宰制的状态。资本主义的“数字塞壬”宰制根源于资本逻辑从生产性向掠夺性演变、技术统治从工具理性向意识形态神话蜕化以及生产关系从契约劳动向封建式人身依附退行。因此，对主体的宰制造成三重时间殖民后果：对时间量的掠夺、质的侵蚀、社会历史属性的割裂与重构。同时，这种宰制结构蕴含着自我否定的潜能，即时间占有的悖论推动政治觉醒，伪主体性的消解催生批判意识，而一般智力的共有化趋势则为超越私人占有逻辑和重建时间主权提供了可能进路。破除“数字塞壬”宰制的关键不是拒斥人工智能技术本身，而是使其摆脱资本逻辑的禁锢，从而真正成为人的自由全面发展的解放力量。","abstractEn":"","pages":"76-86","doi":"10.13644/j.cnki.cn31-1112.2026.08.009","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=Z0kxREJGL3hIbUExOXRNQmxNTnB4QkRvemdYOGZnbnRGSlJHU01nTDkxMXZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"刘日明, 毛以静. 数智时代资本主义的“数字塞壬”宰制与破解[J]. 社会科学, 2026(8): 76-86.","lenZh":392,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026008008","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"智能向善的尺度与基层治理的AI场景化路径","summary":"智能向善是人们反思数智技术应用与追求生活本质意义的共同产物，不仅指引着人类的彼岸目标，也关注在此岸世界的生活质量。作为AI应用的实践界面，基层治理共同体描绘的愿景同智能向善的价值塑造目标具有内在契合性，重点体现在人本、发展、安全、共享四个价值维度。这四个价值维度为基层治理数智化场景划定了伦理基准。基于从“工具”到“伙伴”的人机关系转型，智能向善引领基层治理需要在人的主体性、权力问责性、公平正义性、治理参与性、共同体目标导向性等方面实现价值对齐，以场景化为实践路径，推动数智共治、数智增能、数智韧性与数智普惠在基层治理中的落地。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026008008","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"8","authors":["吴新叶","汪子策"],"affils":["同济大学政治与国际关系学院","同济大学中国特色社会主义理论研究中心"],"title":"智能向善的尺度与基层治理的AI场景化路径","titleEn":"The Dimensions of AI for Good and the AI Scenario Path in the Process of Grassroots Governance","keywords":["基层治理","智能向善","人机伙伴关系","数字场景化"],"keywordsEn":[],"abstract":"智能向善是人们反思数智技术应用与追求生活本质意义的共同产物，不仅指引着人类的彼岸目标，也关注在此岸世界的生活质量。作为AI应用的实践界面，基层治理共同体描绘的愿景同智能向善的价值塑造目标具有内在契合性，重点体现在人本、发展、安全、共享四个价值维度。这四个价值维度为基层治理数智化场景划定了伦理基准。基于从“工具”到“伙伴”的人机关系转型，智能向善引领基层治理需要在人的主体性、权力问责性、公平正义性、治理参与性、共同体目标导向性等方面实现价值对齐，以场景化为实践路径，推动数智共治、数智增能、数智韧性与数智普惠在基层治理中的落地。","abstractEn":"","pages":"87-99","doi":"10.13644/j.cnki.cn31-1112.2026.08.001","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=Z0kxREJGL3hIbUExOXRNQmxNTnB4TEdrV3ltdHRVUWJnNEpnTWZiWFJTSnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"吴新叶, 汪子策. 智能向善的尺度与基层治理的AI场景化路径[J]. 社会科学, 2026(8): 87-99.","lenZh":265,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026007009","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"意识形态询唤的再生产语境与身体力量指向","summary":"阿尔都塞始终在再生产语境中讨论意识形态对人的询唤。因而，以个体和主体为典型，阿尔都塞意识形态询唤理论所有重要范畴的含义均扎根于这一语境，而不能只从字面上理解。综合来讲，阿尔都塞提出询唤概念的目的，是为了概括意识形态在再生产进程中发挥的独特作用，即让生存于生产关系规定之下的个体主动承担社会分工中的职能，成为按职能要求自行行事的主体。阿尔都塞先后从劳动力再生产与生产关系再生产这两个角度对此加以说明。在此过程中，意识形态的特性也就是意识形态询唤的实质，展现为对人的身体力量的根本性引导。澄清阿尔都塞意识形态理论的这一实质，不仅可矫正将询唤简化为个体心理机制的理论误读，也有助于在当代批判理论谱系中明确其定位。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026007009","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"7","authors":["王春明"],"affils":["复旦大学当代国外马克思主义研究中心","复旦大学哲学学院"],"title":"意识形态询唤的再生产语境与身体力量指向","titleEn":"Revisiting Ideological Interpellation: Reproduction as Context and Bodily Forces as Target","keywords":["意识形态","询唤","再生产","力量","身体"],"keywordsEn":[],"abstract":"阿尔都塞始终在再生产语境中讨论意识形态对人的询唤。因而，以个体和主体为典型，阿尔都塞意识形态询唤理论所有重要范畴的含义均扎根于这一语境，而不能只从字面上理解。综合来讲，阿尔都塞提出询唤概念的目的，是为了概括意识形态在再生产进程中发挥的独特作用，即让生存于生产关系规定之下的个体主动承担社会分工中的职能，成为按职能要求自行行事的主体。阿尔都塞先后从劳动力再生产与生产关系再生产这两个角度对此加以说明。在此过程中，意识形态的特性也就是意识形态询唤的实质，展现为对人的身体力量的根本性引导。澄清阿尔都塞意识形态理论的这一实质，不仅可矫正将询唤简化为个体心理机制的理论误读，也有助于在当代批判理论谱系中明确其定位。","abstractEn":"","pages":"106-114","doi":"10.13644/j.cnki.cn31-1112.2026.07.014","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=d29DdDJRejkrTjlLT3pnRFUvSDlCdzhaWkZWMFZOYlU4WVZxcEc3MUVZdHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"王春明. 意识形态询唤的再生产语境与身体力量指向[J]. 社会科学, 2026(7): 106-114.","lenZh":303,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026007010","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"五帝体系的融合与中华民族文化身份认同的形成","summary":"先秦两汉社会流传的众多五帝名单，可归结为古史先祖之五帝体系和阴阳五行之五帝体系。随着知识观念的衍生以及政治“大一统”的推动，这两种五帝体系由最初的相别逐渐走向交错、融合。特别是当炎帝与神农合体、炎帝与黄帝上升为“三皇”、少皞进入古史先祖谱系并成为五帝之首时，新型的“大一统”世系得以形成。相对于政治“大一统”，这是中华民族文化身份的“大一统”，具有超越血缘、族别、地域的性质。在两种五帝体系的融合过程中，不但少皞完成了谱系化的过程，炎帝与黄帝的先后次序也得以固定。由此，先秦时期的“黄炎之后”转变为“炎黄之后”，这一观念奠定了中华民族祖先认知、文化身份认同的基石。新型的古史先祖谱系不仅标志着中华民族文化身份的形成和确证，更代表着中华民族共同体意识的进阶和提升。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026007010","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"7","authors":["刘全志"],"affils":["北京师范大学文学院、中国传统文化研究中心"],"title":"五帝体系的融合与中华民族文化身份认同的形成","titleEn":"The Integration of the Five Emperors System and the Formation of the Chinese Nation's Cultural Identity","keywords":["五帝","炎黄","先祖谱系","中华民族","文化身份认同"],"keywordsEn":[],"abstract":"先秦两汉社会流传的众多五帝名单，可归结为古史先祖之五帝体系和阴阳五行之五帝体系。随着知识观念的衍生以及政治“大一统”的推动，这两种五帝体系由最初的相别逐渐走向交错、融合。特别是当炎帝与神农合体、炎帝与黄帝上升为“三皇”、少皞进入古史先祖谱系并成为五帝之首时，新型的“大一统”世系得以形成。相对于政治“大一统”，这是中华民族文化身份的“大一统”，具有超越血缘、族别、地域的性质。在两种五帝体系的融合过程中，不但少皞完成了谱系化的过程，炎帝与黄帝的先后次序也得以固定。由此，先秦时期的“黄炎之后”转变为“炎黄之后”，这一观念奠定了中华民族祖先认知、文化身份认同的基石。新型的古史先祖谱系不仅标志着中华民族文化身份的形成和确证，更代表着中华民族共同体意识的进阶和提升。","abstractEn":"","pages":"115-131","doi":"10.13644/j.cnki.cn31-1112.2026.07.012","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=dDBqV1BRa3p2Ky9LQWhHbzR5eFZoYlNZN052dWJhTGZGYTVMUThPZ3FuNXZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"刘全志. 五帝体系的融合与中华民族文化身份认同的形成[J]. 社会科学, 2026(7): 115-131.","lenZh":331,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026007011","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"回到“组织研究”：中国革命组织的运行方式再思考——以民国时期江苏第二监狱秘密党支部为例","summary":"本文以1927—1932年江苏第二监狱秘密党支部为例，在既有革命史研究的基础上，引入组织史与制度史的视角，考察高压环境下政治组织的存续方式。近代监狱制度虽以去政治化为目标，但秘密党支部的存续，正是在持续高压的极限场域内，通过精细分工、隐蔽联络与非暴力斗争等组织实践而艰难维系的历史结果。文章通过考察秘密党支部的形成过程、日常运作及转押后的策略转变，进一步阐释情感动员与集体经验在构建组织信任、强化政治认同方面的关键作用，从而有助于更深入地理解中国革命组织的运行方式及其历史形成过程。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026007011","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"7","authors":["杨玉娜"],"affils":["河南师范大学政治与公共管理学院"],"title":"回到“组织研究”：中国革命组织的运行方式再思考——以民国时期江苏第二监狱秘密党支部为例","titleEn":"Returning to \"Organizational Research\": Rethinking the Operational Modes of Chinese Revolutionary Organizations——A Case Study of the Secret Party Branch in Jiangsu Second Prison During the Republican Period","keywords":["民国时期","中共党史","秘密党支部","江苏第二监狱","革命组织"],"keywordsEn":[],"abstract":"本文以1927—1932年江苏第二监狱秘密党支部为例，在既有革命史研究的基础上，引入组织史与制度史的视角，考察高压环境下政治组织的存续方式。近代监狱制度虽以去政治化为目标，但秘密党支部的存续，正是在持续高压的极限场域内，通过精细分工、隐蔽联络与非暴力斗争等组织实践而艰难维系的历史结果。文章通过考察秘密党支部的形成过程、日常运作及转押后的策略转变，进一步阐释情感动员与集体经验在构建组织信任、强化政治认同方面的关键作用，从而有助于更深入地理解中国革命组织的运行方式及其历史形成过程。","abstractEn":"","pages":"132-145","doi":"10.13644/j.cnki.cn31-1112.2026.07.013","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=dDBqV1BRa3p2Ky9LQWhHbzR5eFZoVVBNRkNROURIQ0pCWXRlRmVtZFl5RnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"杨玉娜. 回到“组织研究”：中国革命组织的运行方式再思考——以民国时期江苏第二监狱秘密党支部为例[J]. 社会科学, 2026(7): 132-145.","lenZh":241,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026007012","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"论闻一多文学批评的世界性因素","summary":"在中国现代社会和文化转型的时代，闻一多和不少同时代人一样，都自觉把目光转向海外，这使得他的文学创作和批评都呈现出开放和包容的格局。具体在文学批评中，闻一多就充分吸收、借鉴了西方的新人文主义、唯美主义、意象派等文学理论。更为关键的是，他有意识地把这些理论运用到中国新文学的场域，对诸多文学现象提出了自己新的见解。他用新人文主义来节制和规范新文学的秩序；借用唯美主义守护诗歌极致美、纯粹美的空间，提升了中国新诗的审美品位和境界；用意象派理论沟通中西诗学之间的对话，重新激活中国古典文化的生命。这些实践虽然并非尽善尽美，有的地方甚至不乏偏激之处。但总体而言，闻一多提升了中国文学批评的科学性和阐释的有效性，增强了人们审美的自觉性，为中国文学批评注入了世界性和现代性的因素，他的文学批评也成为中国自主知识体系的有机组成部分。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026007012","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"7","authors":["文学武"],"affils":["上海交通大学人文学院"],"title":"论闻一多文学批评的世界性因素","titleEn":"Cosmopolitan Elements in Wen Yiduo's Literary Criticism","keywords":["闻一多","新人文主义","唯美主义","意象派","“三美”理论"],"keywordsEn":[],"abstract":"在中国现代社会和文化转型的时代，闻一多和不少同时代人一样，都自觉把目光转向海外，这使得他的文学创作和批评都呈现出开放和包容的格局。具体在文学批评中，闻一多就充分吸收、借鉴了西方的新人文主义、唯美主义、意象派等文学理论。更为关键的是，他有意识地把这些理论运用到中国新文学的场域，对诸多文学现象提出了自己新的见解。他用新人文主义来节制和规范新文学的秩序；借用唯美主义守护诗歌极致美、纯粹美的空间，提升了中国新诗的审美品位和境界；用意象派理论沟通中西诗学之间的对话，重新激活中国古典文化的生命。这些实践虽然并非尽善尽美，有的地方甚至不乏偏激之处。但总体而言，闻一多提升了中国文学批评的科学性和阐释的有效性，增强了人们审美的自觉性，为中国文学批评注入了世界性和现代性的因素，他的文学批评也成为中国自主知识体系的有机组成部分。","abstractEn":"","pages":"146-157","doi":"10.13644/j.cnki.cn31-1112.2026.07.009","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=dDBqV1BRa3p2Ky9LQWhHbzR5eFZoZHk2SFIzZG02UHA1V0kxa09OclJadHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"文学武. 论闻一多文学批评的世界性因素[J]. 社会科学, 2026(7): 146-157.","lenZh":358,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026007013","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"声音、方言与情感共同体：海派小说的新文学语言传统","summary":"文学革命以来的海派小说继承了以“言文一致”为核心理念的新文学语言传统。与穆时英、张爱玲、王安忆以“字本位”为基础的口语书写不同，《繁花》呈现整体性的声音书写，为海派小说提供了一种可称为“音本位”的语言实验路径。面对标准语与沪语熔铸的问题，张爱玲一般以审慎、保守的态度实践多语混杂的海派文体，金宇澄则通过自觉的语词协商创造出“上海官话语言”，在表征地方文化与实现语言跨区域流通之间获得了语义与情感的平衡。海派小说的语言实践所建构的情感认同、地方及民族国家认同，以一种“想象”的方式展开，情感结构中既有基于地方文化的独特内涵，也包含超越上海本土的民族史诗性，从而延续了五四语言传统中地方性与同质化辩证统一的复杂模式。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026007013","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"7","authors":["唐诗诗"],"affils":["上海社会科学院文学研究所"],"title":"声音、方言与情感共同体：海派小说的新文学语言传统","titleEn":"Voice, Dialect, and Emotional Community: The New Literary Language Tradition of Shanghai-Style Novels","keywords":["海派小说","言文一致","国语","方言","情感共同体"],"keywordsEn":[],"abstract":"文学革命以来的海派小说继承了以“言文一致”为核心理念的新文学语言传统。与穆时英、张爱玲、王安忆以“字本位”为基础的口语书写不同，《繁花》呈现整体性的声音书写，为海派小说提供了一种可称为“音本位”的语言实验路径。面对标准语与沪语熔铸的问题，张爱玲一般以审慎、保守的态度实践多语混杂的海派文体，金宇澄则通过自觉的语词协商创造出“上海官话语言”，在表征地方文化与实现语言跨区域流通之间获得了语义与情感的平衡。海派小说的语言实践所建构的情感认同、地方及民族国家认同，以一种“想象”的方式展开，情感结构中既有基于地方文化的独特内涵，也包含超越上海本土的民族史诗性，从而延续了五四语言传统中地方性与同质化辩证统一的复杂模式。","abstractEn":"","pages":"158-167","doi":"10.13644/j.cnki.cn31-1112.2026.07.002","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=dDBqV1BRa3p2Ky9LQWhHbzR5eFZoWHhmejlCU1dOZGtQNEdkcG5kdUUwbHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"唐诗诗. 声音、方言与情感共同体：海派小说的新文学语言传统[J]. 社会科学, 2026(7): 158-167.","lenZh":306,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026007014","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"中国私法语境下所有权的三种形态","summary":"建构中国物权法自主知识体系，是新时代民法典理解与适用的重要使命。当前，单一、同质的所有权概念已无法适配我国私法规范体系中所有权的多元形态。从功能主义视角出发，我国私法规范体系大体形成了公有所有权、私人所有权与担保型所有权三种形态，分别对应所有权的“管制功能”“自由功能”和“信用功能”。在社会主义公有制框架下，国家所有权与集体所有权构成公有所有权的基本形态，其权利行使以重要资源管控和公共利益实现为目标，呈现出公共性、组织性和义务性的制度特征，且须遵循程序法定的要求。与之相对，私人所有权主要发挥保障私主体支配自由与促进效率实现的功能，同时负有一定程度的社会化任务，并对各类财产权具有不同程度的示范效应。在信用经济发展与功能主义担保观的影响下，担保型所有权在担保法体系中的地位日趋重要，典型表现为所有权保留、融资租赁与让与担保等交易结构。此类制度虽然在形式上表现为所有权的移转或保留，但其权利内容与运行逻辑已经明显担保化。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026007014","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"7","authors":["袁野"],"affils":["武汉大学法学院"],"title":"中国私法语境下所有权的三种形态","titleEn":"Three Forms of Ownership in the Context of Chinese Private Law","keywords":["所有权","功能主义","公有所有权","私人所有权","担保型所有权"],"keywordsEn":[],"abstract":"建构中国物权法自主知识体系，是新时代民法典理解与适用的重要使命。当前，单一、同质的所有权概念已无法适配我国私法规范体系中所有权的多元形态。从功能主义视角出发，我国私法规范体系大体形成了公有所有权、私人所有权与担保型所有权三种形态，分别对应所有权的“管制功能”“自由功能”和“信用功能”。在社会主义公有制框架下，国家所有权与集体所有权构成公有所有权的基本形态，其权利行使以重要资源管控和公共利益实现为目标，呈现出公共性、组织性和义务性的制度特征，且须遵循程序法定的要求。与之相对，私人所有权主要发挥保障私主体支配自由与促进效率实现的功能，同时负有一定程度的社会化任务，并对各类财产权具有不同程度的示范效应。在信用经济发展与功能主义担保观的影响下，担保型所有权在担保法体系中的地位日趋重要，典型表现为所有权保留、融资租赁与让与担保等交易结构。此类制度虽然在形式上表现为所有权的移转或保留，但其权利内容与运行逻辑已经明显担保化。","abstractEn":"","pages":"168-180","doi":"10.13644/j.cnki.cn31-1112.2026.07.003","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=dDBqV1BRa3p2Ky9LQWhHbzR5eFZoV3U5ZmdSRWRRbk9zTTE0YXl6QjdQWnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"袁野. 中国私法语境下所有权的三种形态[J]. 社会科学, 2026(7): 168-180.","lenZh":411,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026007015","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"审限管理：民事诉讼的时间控制与程序保障","summary":"审限管理是民事诉讼审判时间管理的核心机制。实践中审限扣除的情形多样且复杂，概括式的计算方式与多主体间的博弈导致审限管理功能过度扩张，而当事人程序权利的保障功能却日趋式微，以至偏离了原来审限管理促进诉讼效率的功能期盼，甚至还影响到程序正义等价值的实现。破解我国审限管理的困境不是取消审限或延长审限，而应立足审限扣除在司法运作中的功能，明晰时间成本主体与责任主体。在权责匹配的逻辑框架下，可以有三项改革路径：以分段式审限取代概括式审限，由导致程序迟延的主体承担相应时间成本；赋予当事人程序迟延异议权，推动审限操作透明化；优化法院内部分工，保障法官将工作重心集中于实质裁判。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026007015","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"7","authors":["罗恬漩"],"affils":["同济大学法学院"],"title":"审限管理：民事诉讼的时间控制与程序保障","titleEn":"Trial Time Limit Management: Time Control and Procedural Protection in Civil Litigation","keywords":["审限","审限扣除","诉讼效率","正当程序"],"keywordsEn":[],"abstract":"审限管理是民事诉讼审判时间管理的核心机制。实践中审限扣除的情形多样且复杂，概括式的计算方式与多主体间的博弈导致审限管理功能过度扩张，而当事人程序权利的保障功能却日趋式微，以至偏离了原来审限管理促进诉讼效率的功能期盼，甚至还影响到程序正义等价值的实现。破解我国审限管理的困境不是取消审限或延长审限，而应立足审限扣除在司法运作中的功能，明晰时间成本主体与责任主体。在权责匹配的逻辑框架下，可以有三项改革路径：以分段式审限取代概括式审限，由导致程序迟延的主体承担相应时间成本；赋予当事人程序迟延异议权，推动审限操作透明化；优化法院内部分工，保障法官将工作重心集中于实质裁判。","abstractEn":"","pages":"181-192","doi":"10.13644/j.cnki.cn31-1112.2026.07.006","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=dDBqV1BRa3p2Ky9LQWhHbzR5eFZoU2VHYzllelYwbVJQQitjTlpsY3dycHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"罗恬漩. 审限管理：民事诉讼的时间控制与程序保障[J]. 社会科学, 2026(7): 181-192.","lenZh":284,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026007002","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"如何把人工智能红利转化为民生福祉？","summary":"对于提高劳动生产率和民生福祉的目标，人工智能既带来了前所未有的发展机遇，也带来了风险与挑战，只有防范风险和应对挑战，才能抓住科技革命和产业变革的重要机遇。这引申出一个重要命题：如何把人工智能红利转化为民生福祉？人工智能的技术和产业渗透，已经显露出生产率提高的确定性预期，也表现出对就业和收入分配的负面效应。把应对风险挑战同抓住发展机遇有机结合，是破解这个命题的总体思路。一方面，要在破解结构性就业矛盾的同时，防范人工智能可能造成的就业冲击；另一方面，应加大再分配力度，在改善收入分配的同时，实现从人工智能红利到民生福祉的转化。在政策方面，一是要确保人工智能研发和应用各环节对标共享发展理念；二是要从破解结构性就业矛盾入手，防范和应对人工智能就业冲击；三是要增强基本公共服务供给的基础性、普惠性、兜底性，通过再分配分享生产率红利。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026007002","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"7","authors":["蔡昉"],"affils":["中国社会科学院"],"title":"如何把人工智能红利转化为民生福祉？","titleEn":"How to Translate the Dividends of Artifi cial Intelligence into People's Well-Being","keywords":["人工智能红利","民生福祉","劳动生产率“分式两难”","“投资于人”"],"keywordsEn":[],"abstract":"对于提高劳动生产率和民生福祉的目标，人工智能既带来了前所未有的发展机遇，也带来了风险与挑战，只有防范风险和应对挑战，才能抓住科技革命和产业变革的重要机遇。这引申出一个重要命题：如何把人工智能红利转化为民生福祉？人工智能的技术和产业渗透，已经显露出生产率提高的确定性预期，也表现出对就业和收入分配的负面效应。把应对风险挑战同抓住发展机遇有机结合，是破解这个命题的总体思路。一方面，要在破解结构性就业矛盾的同时，防范人工智能可能造成的就业冲击；另一方面，应加大再分配力度，在改善收入分配的同时，实现从人工智能红利到民生福祉的转化。在政策方面，一是要确保人工智能研发和应用各环节对标共享发展理念；二是要从破解结构性就业矛盾入手，防范和应对人工智能就业冲击；三是要增强基本公共服务供给的基础性、普惠性、兜底性，通过再分配分享生产率红利。","abstractEn":"","pages":"20-27","doi":"10.13644/j.cnki.cn31-1112.2026.07.011","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=d29DdDJRejkrTjlLT3pnRFUvSDlCOXk2SFIzZG02UHA1V0kxa09OclJadHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"蔡昉. 如何把人工智能红利转化为民生福祉？[J]. 社会科学, 2026(7): 20-27.","lenZh":364,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026007003","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"新兴产业与传统产业收入差距：典型事实、结构特征与形成机制","summary":"新兴产业与传统产业之间的收入差距，已成为中国经济转型中不可回避的分配问题。本文基于2010—2024年中国A股上市公司数据，对战略性新兴产业与代表性传统产业的薪酬分布进行描述，归纳出三项典型事实：其一，新兴产业薪酬优势高度集中于小规模企业，就业加权后优势明显收缩，大规模就业主体未能同步受益；其二，新兴产业内部薪酬分化持续加剧，在服务业与工业新兴产业之间、工业新兴产业各细分领域之间均形成明显差距；其三，学历结构仅能解释新兴产业与传统产业薪酬差距的约45%，且在战略性新兴产业与非战略性新兴产业的比较中呈现负向影响。在此基础上，本文从两个维度分析收入差距的形成机制：一是技术进步对不同产业的选择性推动造成行业间生产率分化，二是新兴产业各细分领域在创新租金的形成条件与竞争壁垒保护程度上的差异，导致新兴产业内部也形成了多层次的薪酬差距。政策上，应区分差距来源并进行分类干预：推动技术扩散向传统产业渗透，引导大型新兴企业建立普惠性薪酬共享机制，对创新租金被快速侵蚀的细分领域给予差异化支持，推进管制性行业开放以矫正与人力资本贡献脱钩的工资溢价。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026007003","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"7","authors":["杨虎涛","潘东奇"],"affils":["中国社会科学院经济研究所","中国社会科学院大学经济学院"],"title":"新兴产业与传统产业收入差距：典型事实、结构特征与形成机制","titleEn":"Income Disparity Between Emerging and Traditional Industries: Stylized Facts, Structural Characteristics, and Underlying Mechanisms","keywords":["收入差距","新兴产业","传统产业","技术偏向性变革"],"keywordsEn":[],"abstract":"新兴产业与传统产业之间的收入差距，已成为中国经济转型中不可回避的分配问题。本文基于2010—2024年中国A股上市公司数据，对战略性新兴产业与代表性传统产业的薪酬分布进行描述，归纳出三项典型事实：其一，新兴产业薪酬优势高度集中于小规模企业，就业加权后优势明显收缩，大规模就业主体未能同步受益；其二，新兴产业内部薪酬分化持续加剧，在服务业与工业新兴产业之间、工业新兴产业各细分领域之间均形成明显差距；其三，学历结构仅能解释新兴产业与传统产业薪酬差距的约45%，且在战略性新兴产业与非战略性新兴产业的比较中呈现负向影响。在此基础上，本文从两个维度分析收入差距的形成机制：一是技术进步对不同产业的选择性推动造成行业间生产率分化，二是新兴产业各细分领域在创新租金的形成条件与竞争壁垒保护程度上的差异，导致新兴产业内部也形成了多层次的薪酬差距。政策上，应区分差距来源并进行分类干预：推动技术扩散向传统产业渗透，引导大型新兴企业建立普惠性薪酬共享机制，对创新租金被快速侵蚀的细分领域给予差异化支持，推进管制性行业开放以矫正与人力资本贡献脱钩的工资溢价。","abstractEn":"","pages":"28-40","doi":"10.13644/j.cnki.cn31-1112.2026.07.004","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=d29DdDJRejkrTjlLT3pnRFUvSDlCM3hmejlCU1dOZGtQNEdkcG5kdUUwbHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"杨虎涛, 潘东奇. 新兴产业与传统产业收入差距：典型事实、结构特征与形成机制[J]. 社会科学, 2026(7): 28-40.","lenZh":470,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026007004","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"平台经济会影响劳动收入分配平等性吗？——来自微观企业层面的经验证据","summary":"随着平台经济的快速发展，其对收入分配格局的影响日益凸显。本文采用中国工商企业注册数据、上市公司年报及线上招聘数据等，从平台经济基础设施条件、平台企业发展水平和平台用工需求三个维度测算地区平台经济发展水平，探讨平台经济兴起对劳资收益分配平等性的影响，以及劳动力议价能力和地区劳动保障力度在其中的机制作用，并进一步检验企业平台化及供应链平台化对企业内部收益分配的影响。研究发现：（1）平台经济规模扩张通过削弱劳动力议价能力并降低劳动保障力度，加剧劳资收益分配失衡，其中弱势群体受到的不利冲击更为明显；（2）在市场竞争较弱、融资环境较为宽松以及失业率或迁入率较高的城市中，平台经济扩张引致收入分配更加偏向资本，进而对劳动者产生负面影响；（3）企业平台化显著推动收入分配向资本倾斜，供应链平台化也会带来负向溢出效应，降低企业内部劳动收入占比。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026007004","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"7","authors":["董直庆","张潇"],"affils":["华东师范大学经济与管理学院"],"title":"平台经济会影响劳动收入分配平等性吗？——来自微观企业层面的经验证据","titleEn":"Does the Platform Economy Aff ect Labor Income Distribution: Empirical Evidence from Listed Firms","keywords":["平台经济","劳资收入分配格局","企业平台化"],"keywordsEn":[],"abstract":"随着平台经济的快速发展，其对收入分配格局的影响日益凸显。本文采用中国工商企业注册数据、上市公司年报及线上招聘数据等，从平台经济基础设施条件、平台企业发展水平和平台用工需求三个维度测算地区平台经济发展水平，探讨平台经济兴起对劳资收益分配平等性的影响，以及劳动力议价能力和地区劳动保障力度在其中的机制作用，并进一步检验企业平台化及供应链平台化对企业内部收益分配的影响。研究发现：（1）平台经济规模扩张通过削弱劳动力议价能力并降低劳动保障力度，加剧劳资收益分配失衡，其中弱势群体受到的不利冲击更为明显；（2）在市场竞争较弱、融资环境较为宽松以及失业率或迁入率较高的城市中，平台经济扩张引致收入分配更加偏向资本，进而对劳动者产生负面影响；（3）企业平台化显著推动收入分配向资本倾斜，供应链平台化也会带来负向溢出效应，降低企业内部劳动收入占比。","abstractEn":"","pages":"41-53","doi":"10.13644/j.cnki.cn31-1112.2026.07.015","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=d29DdDJRejkrTjlLT3pnRFUvSDlCMnU5ZmdSRWRRbk9zTTE0YXl6QjdQWnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"董直庆, 张潇. 平台经济会影响劳动收入分配平等性吗？——来自微观企业层面的经验证据[J]. 社会科学, 2026(7): 41-53.","lenZh":367,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026007001","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"从“理论输入”到“体系生成”：中国特色传播政治经济学的构建逻辑及学科图景","summary":"中国特色传播政治经济学学科体系的构建旨在回应中国传播学自主知识体系建设中的核心问题，即如何从中国传播实践出发，形成能够解释当代数字传播结构变迁的理论框架。以马克思主义政治经济学和历史唯物主义为方法论基础，以智力劳动与知识价值论为分析主轴，传播被界定为社会关系、价值秩序与权力结构再生产的重要机制，平台、数据、算法与人工智能则构成知识商品化、劳动组织化与传播秩序重构的关键中介。围绕传播资源的占有与控制，传播劳动的组织与价值化，技术权力的生成与扩展以及传播秩序对社会公平、文化认同、国家安全和人的全面发展的影响，学科体系可进一步展开为“国家—市场—平台—技术—劳动—公众—文化”相互联动的范畴结构。该体系的构建，不但有助于推动中国传播实践由经验材料上升为媒介传播研究的理论资源，而且能够为平台治理、网络综合治理、人工智能治理以及公共传播体系建设提供结构性解释，并在此基础上推进中国传播经验向自主知识生产和国际学术对话的转化。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026007001","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"7","authors":["杨嵘均"],"affils":["华东师范大学马克思主义学院"],"title":"从“理论输入”到“体系生成”：中国特色传播政治经济学的构建逻辑及学科图景","titleEn":"From \"Theory Import\" to \"System Emergence\": The Construction Logic and Disciplinary Landscape of the Political Economy of Communication with Chinese Characteristics","keywords":["中国特色传播政治经济学","马克思主义政治经济学","学科体系构建","自主知识体系","智力劳动"],"keywordsEn":[],"abstract":"中国特色传播政治经济学学科体系的构建旨在回应中国传播学自主知识体系建设中的核心问题，即如何从中国传播实践出发，形成能够解释当代数字传播结构变迁的理论框架。以马克思主义政治经济学和历史唯物主义为方法论基础，以智力劳动与知识价值论为分析主轴，传播被界定为社会关系、价值秩序与权力结构再生产的重要机制，平台、数据、算法与人工智能则构成知识商品化、劳动组织化与传播秩序重构的关键中介。围绕传播资源的占有与控制，传播劳动的组织与价值化，技术权力的生成与扩展以及传播秩序对社会公平、文化认同、国家安全和人的全面发展的影响，学科体系可进一步展开为“国家—市场—平台—技术—劳动—公众—文化”相互联动的范畴结构。该体系的构建，不但有助于推动中国传播实践由经验材料上升为媒介传播研究的理论资源，而且能够为平台治理、网络综合治理、人工智能治理以及公共传播体系建设提供结构性解释，并在此基础上推进中国传播经验向自主知识生产和国际学术对话的转化。","abstractEn":"","pages":"5-19","doi":"10.13644/j.cnki.cn31-1112.2026.07.005","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=d29DdDJRejkrTjlLT3pnRFUvSDlCMFBNRkNROURIQ0pCWXRlRmVtZFl5RnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"杨嵘均. 从“理论输入”到“体系生成”：中国特色传播政治经济学的构建逻辑及学科图景[J]. 社会科学, 2026(7): 5-19.","lenZh":411,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026007005","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"生命的成全：面向“第四年龄”的养老服务及保障体系建设","summary":"当前我国养老服务存在结构性供需错配，即按法定年龄实施普惠供给，市场资源向健康老年群体集聚，中重度失能失智“第四年龄”群体刚性照护保障不足。这不仅使人民满足感、安全感未达到政策预期，也制约了这项民生保障工程的经济发展效能。如何破题，成为“十五五”时期扎实推进共同富裕的重要议题。为此，通过反思个体本位生命观及养老服务制度模式，立足中国家文化、生命文化中的“关系性”“群体性”生命观与“代际生命一体性”，本文构建“生命—制度”分析框架，提出以“生命的成全”为核心价值，以家为本位，聚焦“第四年龄”群体服务精准供给与综合赋能效应，破除生命历程、代际关系、经济社会、技术人文的二元对立，构建中国式养老服务体系，为推动中国式现代化与共同富裕目标实现提供理论支撑与实践方案。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026007005","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"7","authors":["贾玉娇"],"affils":["吉林大学哲学社会学院"],"title":"生命的成全：面向“第四年龄”的养老服务及保障体系建设","titleEn":"The Fulfi llment of Life: Construction and Development Empowerment of the Elderly Care Service System for the \"Fourth 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54-64.","lenZh":331,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026007006","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"政府回应过程中的事权重构及其困境：基于12345市民热线的个案研究","summary":"市民热线是政民互动的重要平台，对市民热线的研究有助于全面检视政府回应公众诉求的复杂过程与内在张力。本文以12345市民热线工作中的派单与退单问题为例，通过引入事权重构的分析框架，深描中国政府回应公众诉求的动态图景。研究表明，在信息整合、归口包干与提级治理三大机制的作用下，政府部门通过对事权的梳理，实现了“有呼必应”的目标，完成了派单任务。由于存在信息模糊、责任悬浮和事权错配等问题，退单行为频繁出现，市民热线陷入“有应无解”的局面。派单与退单的矛盾折射出政府部门间分工与跨部门协同的内在困境，事权重构仍是一个亟待完成的任务。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026007006","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"7","authors":["戴康","熊易寒"],"affils":["复旦大学国际关系与公共事务学院"],"title":"政府回应过程中的事权重构及其困境：基于12345市民热线的个案研究","titleEn":"The Reconfi guration and Its Dilemma of Shiquan in Government Response Process: A Case Study Based on the 12345 Citizen Hotline","keywords":["派单","退单","回应性","市民热线","事权"],"keywordsEn":[],"abstract":"市民热线是政民互动的重要平台，对市民热线的研究有助于全面检视政府回应公众诉求的复杂过程与内在张力。本文以12345市民热线工作中的派单与退单问题为例，通过引入事权重构的分析框架，深描中国政府回应公众诉求的动态图景。研究表明，在信息整合、归口包干与提级治理三大机制的作用下，政府部门通过对事权的梳理，实现了“有呼必应”的目标，完成了派单任务。由于存在信息模糊、责任悬浮和事权错配等问题，退单行为频繁出现，市民热线陷入“有应无解”的局面。派单与退单的矛盾折射出政府部门间分工与跨部门协同的内在困境，事权重构仍是一个亟待完成的任务。","abstractEn":"","pages":"65-77","doi":"10.13644/j.cnki.cn31-1112.2026.07.007","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=d29DdDJRejkrTjlLT3pnRFUvSDlCOHZKd0FXd0RmeW84MzV2VlQrcTFqOXZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"戴康, 熊易寒. 政府回应过程中的事权重构及其困境：基于12345市民热线的个案研究[J]. 社会科学, 2026(7): 65-77.","lenZh":263,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026007007","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"意义空间——尼采与现象学","summary":"尼采哲学的内在统一性问题历来在哲学界聚讼不已。以往的主流解释是将“权力意志”视为尼采哲学的一个标志性概念，并以此为根据把他的哲学看成是一种形而上学或自然主义理论体系。但是，近年来的文本考证表明，该词频次极低且含义游移，并不能构成尼采哲学的一个标志性概念。事实上，尼采哲学并没有任何标志性概念，它的统一性也不是由某个标志性概念来刻画，而是体现为对人之生存的悖谬性——即人创造意义却又被意义反噬——的持续追问。借用美国学者克洛威尔的“意义空间”之说，我们可以将尼采哲学看成是一种“现象学之前的现象学”。尼采早期视认识为生命的一种“简化和伪造”，其中后期则将这种简化和伪造看成是一种积极的意义创造，并且认为这种意义创造是一种不断的“自我克服”。不仅如此，尼采还认为意义空间对于人的生命也有一种反向制约的可能，由此形成了一种奴隶道德。奴隶道德基于对有限生命的“怨恨”，借由表层且易错的意识虚构出自由意志与道德主体，将动态的意义空间固化为一种绝对永恒的“苦修理想”。这就是虚无主义的哲学根源。由此可见，与现象学相比，尼采哲学更敏锐地剖析了意义空间对生命的反向构造或压制，在常态经验之下发掘出一个纵深的维度，更彻底地践行了“回到实事本身”的原则。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026007007","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"7","authors":["吴增定"],"affils":["北京大学美学与美育研究中心","北京大学哲学系"],"title":"意义空间——尼采与现象学","titleEn":"The Space of Meaning: Nietzsche and Phenomenology","keywords":["意义空间","权力意志","意识","视域","苦修理想"],"keywordsEn":[],"abstract":"尼采哲学的内在统一性问题历来在哲学界聚讼不已。以往的主流解释是将“权力意志”视为尼采哲学的一个标志性概念，并以此为根据把他的哲学看成是一种形而上学或自然主义理论体系。但是，近年来的文本考证表明，该词频次极低且含义游移，并不能构成尼采哲学的一个标志性概念。事实上，尼采哲学并没有任何标志性概念，它的统一性也不是由某个标志性概念来刻画，而是体现为对人之生存的悖谬性——即人创造意义却又被意义反噬——的持续追问。借用美国学者克洛威尔的“意义空间”之说，我们可以将尼采哲学看成是一种“现象学之前的现象学”。尼采早期视认识为生命的一种“简化和伪造”，其中后期则将这种简化和伪造看成是一种积极的意义创造，并且认为这种意义创造是一种不断的“自我克服”。不仅如此，尼采还认为意义空间对于人的生命也有一种反向制约的可能，由此形成了一种奴隶道德。奴隶道德基于对有限生命的“怨恨”，借由表层且易错的意识虚构出自由意志与道德主体，将动态的意义空间固化为一种绝对永恒的“苦修理想”。这就是虚无主义的哲学根源。由此可见，与现象学相比，尼采哲学更敏锐地剖析了意义空间对生命的反向构造或压制，在常态经验之下发掘出一个纵深的维度，更彻底地践行了“回到实事本身”的原则。","abstractEn":"","pages":"78-94","doi":"10.13644/j.cnki.cn31-1112.2026.07.008","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=d29DdDJRejkrTjlLT3pnRFUvSDlCeERvemdYOGZnbnRGSlJHU01nTDkxMXZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"吴增定. 意义空间——尼采与现象学[J]. 社会科学, 2026(7): 78-94.","lenZh":517,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026007008","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"社会正义的三种概念图式","summary":"当代政治哲学常将罗尔斯、诺齐克与哈耶克置于支持与反对“社会正义”的对立谱系中，并据此把相关争论理解为平等与自由、国家与市场之间的对立。本文认为，这一流行叙事的根本问题，不仅在于夸大了三者的价值分歧，更在于预先压平了他们各自指向的首要评价对象。围绕社会正义，现代政治哲学中至少并存着三种常被混同的理论图式：配置型、生成型与结构型。三者真正的分野，不在于是否赞成平等或市场，而在于正义究竟首先评价既有结果格局、持有的形成史，还是持续塑造合作条件的制度框架。循此可见，罗尔斯并非通常所说的配置型再分配论者，诺齐克也不只是社会正义的对立面，哈耶克猛烈批评的主要是把社会拟人化为分配者的配置型图式，而非一切社会正义理论。借助这一重构，本文尝试把既有“对立叙事”改写为一幅更为准确的概念地形图。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026007008","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"7","authors":["葛四友","王丹妮"],"affils":["武汉大学哲学学院"],"title":"社会正义的三种概念图式","titleEn":"Three Conceptual Schemas of Social Justice","keywords":["社会正义","配置型正义","生成型正义","结构型正义"],"keywordsEn":[],"abstract":"当代政治哲学常将罗尔斯、诺齐克与哈耶克置于支持与反对“社会正义”的对立谱系中，并据此把相关争论理解为平等与自由、国家与市场之间的对立。本文认为，这一流行叙事的根本问题，不仅在于夸大了三者的价值分歧，更在于预先压平了他们各自指向的首要评价对象。围绕社会正义，现代政治哲学中至少并存着三种常被混同的理论图式：配置型、生成型与结构型。三者真正的分野，不在于是否赞成平等或市场，而在于正义究竟首先评价既有结果格局、持有的形成史，还是持续塑造合作条件的制度框架。循此可见，罗尔斯并非通常所说的配置型再分配论者，诺齐克也不只是社会正义的对立面，哈耶克猛烈批评的主要是把社会拟人化为分配者的配置型图式，而非一切社会正义理论。借助这一重构，本文尝试把既有“对立叙事”改写为一幅更为准确的概念地形图。","abstractEn":"","pages":"95-105","doi":"10.13644/j.cnki.cn31-1112.2026.07.010","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=d29DdDJRejkrTjlLT3pnRFUvSDlCN0drV3ltdHRVUWJnNEpnTWZiWFJTSnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"葛四友, 王丹妮. 社会正义的三种概念图式[J]. 社会科学, 2026(7): 95-105.","lenZh":341,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026006002","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"中国城市政治学知识生产机制研究：基于比较城市主义的分析","summary":"在城市化格局被全球南方城市重组的背景下，既有以全球北方经验为基础的城市政治学面临日益突出的解释力困境。比较城市主义主张解构知识生产的等级结构，赋予所有城市实践以平等的理论生成权利。基于此，本文构建了一个以去中心化、关系嵌入与机制生成为原则的比较分析框架，从为何比较、如何比较和比较什么三个层面进行分析，通过对现代化人民城市的价值规定性、发展政治的生产逻辑、单一制政治体制的治理弹性和国家与社会共生的复合治理形态等概念和中层政治机制的提炼，尝试将中国城市政治实践转化为具备全球对话能力的学术议题，系统探讨中国城市政治学在比较城市主义视野下的知识生产路径。这一具有反思性和生成性的比较路径，不仅可以深化对本土城市政治逻辑的理解，还可以参与全球城市政治学的知识生产。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026006002","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"6","authors":["宋道雷"],"affils":["复旦大学人民城市研究院","复旦大学马克思主义学院"],"title":"中国城市政治学知识生产机制研究：基于比较城市主义的分析","titleEn":"A Study of Knowledge Production Mechanisms in Chinese Urban Politics: An Analysis Based on Comparative Urbanism","keywords":["城市政治学","比较城市主义","现代化人民城市","全球南方","知识生产"],"keywordsEn":[],"abstract":"在城市化格局被全球南方城市重组的背景下，既有以全球北方经验为基础的城市政治学面临日益突出的解释力困境。比较城市主义主张解构知识生产的等级结构，赋予所有城市实践以平等的理论生成权利。基于此，本文构建了一个以去中心化、关系嵌入与机制生成为原则的比较分析框架，从为何比较、如何比较和比较什么三个层面进行分析，通过对现代化人民城市的价值规定性、发展政治的生产逻辑、单一制政治体制的治理弹性和国家与社会共生的复合治理形态等概念和中层政治机制的提炼，尝试将中国城市政治实践转化为具备全球对话能力的学术议题，系统探讨中国城市政治学在比较城市主义视野下的知识生产路径。这一具有反思性和生成性的比较路径，不仅可以深化对本土城市政治逻辑的理解，还可以参与全球城市政治学的知识生产。","abstractEn":"","pages":"15-24","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=YTZZWG9xd1V0d05qTlcyUituakQ5ZHk2SFIzZG02UHA1V0kxa09OclJadHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"宋道雷. 中国城市政治学知识生产机制研究：基于比较城市主义的分析[J]. 社会科学, 2026(6): 15-24.","lenZh":330,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026006003","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"“十五五”时期区域协调发展的内涵演进与重点任务——基于战略叠加视角的分析","summary":"在加快构建新发展格局、实现中国式现代化的背景下，如何从战略叠加的视角审视区域协调发展，是破解区域发展不平衡不充分问题的关键。本文构建了涵盖“目标—要素—空间—动力”的分析框架，系统阐述了新时期区域协调发展的内涵演进与重点任务。战略叠加视角下，区域协调发展的内涵发生了深刻转变：目标定位从差距缩小转向功能协同、要素流动从硬件联通转向机制畅通、空间形态从单核辐射转向多极协同、动力来源从要素投入转向创新驱动。基于上述分析，本文提出新时期区域协调发展的重点任务，包括构建优势互补的产业分工格局，加快推进全国统一大市场建设，培育多中心、网络化的城镇体系和完善区域创新体系、打造科创共同体。为保障以上任务的有效落实，建议加强区域战略的统筹与衔接、完善区域利益协调机制、提升跨区域协同治理能力，为构建优势互补、高质量发展的区域经济布局提供理论支撑。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026006003","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"6","authors":["孙久文","董红燕"],"affils":["中国人民大学应用经济学院"],"title":"“十五五”时期区域协调发展的内涵演进与重点任务——基于战略叠加视角的分析","titleEn":"The Evolving Connotation and Key Tasks of Regional Coordinated Development During the 15th Five-Year Plan Period: An Analysis from the Perspective of \"Regional Strategy Superposition\"","keywords":["“十五五”时期","战略叠加","区域协调发展"],"keywordsEn":[],"abstract":"在加快构建新发展格局、实现中国式现代化的背景下，如何从战略叠加的视角审视区域协调发展，是破解区域发展不平衡不充分问题的关键。本文构建了涵盖“目标—要素—空间—动力”的分析框架，系统阐述了新时期区域协调发展的内涵演进与重点任务。战略叠加视角下，区域协调发展的内涵发生了深刻转变：目标定位从差距缩小转向功能协同、要素流动从硬件联通转向机制畅通、空间形态从单核辐射转向多极协同、动力来源从要素投入转向创新驱动。基于上述分析，本文提出新时期区域协调发展的重点任务，包括构建优势互补的产业分工格局，加快推进全国统一大市场建设，培育多中心、网络化的城镇体系和完善区域创新体系、打造科创共同体。为保障以上任务的有效落实，建议加强区域战略的统筹与衔接、完善区域利益协调机制、提升跨区域协同治理能力，为构建优势互补、高质量发展的区域经济布局提供理论支撑。","abstractEn":"","pages":"25-35","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=YTZZWG9xd1V0d05qTlcyUituakQ5WHhmejlCU1dOZGtQNEdkcG5kdUUwbHZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"孙久文, 董红燕. “十五五”时期区域协调发展的内涵演进与重点任务——基于战略叠加视角的分析[J]. 社会科学, 2026(6): 25-35.","lenZh":368,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026006004","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"未来产业的全球竞争格局：特征、范式与新增长空间重构","summary":"站在“两个一百年”奋斗目标的历史交汇点，科技革命浪潮正推动着全球竞争格局的深度重塑。未来产业正在以指数级的速度改写全球价值链。变革不仅是产业形态的升级，更被大国视为一场关于权力平衡的战略博弈。由此，颠覆性技术发展正在加速未来产业构建，未来产业的竞争也已不再局限于市场份额的争夺，而是上升为对全球经济地理空间、技术生态与制度规则的重新划定。竞争格局重塑决定着产业链的高地分布，技术生态正在锁定未来发展空间，而制度安全化与绿色化则在重新界定全球主要经济体的行动边界。只有抢占技术制高点、构建完整的颠覆性技术与未来产业生态，积极参与全球规则制定，才能打破旧有的空间格局。未来产业不是简单的“新兴产业”或“高科技”标签，而是支撑拓展新一轮增长空间、引领人类社会迈向高质量发展的关键引擎。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026006004","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"6","authors":["戢仕铭"],"affils":["华东师范大学政治与国际关系学院"],"title":"未来产业的全球竞争格局：特征、范式与新增长空间重构","titleEn":"Global Competitive Landscape of Future Industries: Characteristics, Paradigms, and the Restructuring of New Growth Spaces","keywords":["未来产业","新增长空间","颠覆性技术","技术生态","新兴领域安全"],"keywordsEn":[],"abstract":"站在“两个一百年”奋斗目标的历史交汇点，科技革命浪潮正推动着全球竞争格局的深度重塑。未来产业正在以指数级的速度改写全球价值链。变革不仅是产业形态的升级，更被大国视为一场关于权力平衡的战略博弈。由此，颠覆性技术发展正在加速未来产业构建，未来产业的竞争也已不再局限于市场份额的争夺，而是上升为对全球经济地理空间、技术生态与制度规则的重新划定。竞争格局重塑决定着产业链的高地分布，技术生态正在锁定未来发展空间，而制度安全化与绿色化则在重新界定全球主要经济体的行动边界。只有抢占技术制高点、构建完整的颠覆性技术与未来产业生态，积极参与全球规则制定，才能打破旧有的空间格局。未来产业不是简单的“新兴产业”或“高科技”标签，而是支撑拓展新一轮增长空间、引领人类社会迈向高质量发展的关键引擎。","abstractEn":"","pages":"36-51","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=YTZZWG9xd1V0d05qTlcyUituakQ5V3U5ZmdSRWRRbk9zTTE0YXl6QjdQWnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"戢仕铭. 未来产业的全球竞争格局：特征、范式与新增长空间重构[J]. 社会科学, 2026(6): 36-51.","lenZh":339,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026006001","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"原美：关于美的内涵的理本论探索","summary":"以自然美为起点，通过现象学还原，可以发现美并非主观情感或客观属性，而是一种“自足性的显象”，其核心机制在于对象以其内在的完满性使审美主体进入一种被动领受状态。美在形式上呈现为时间性，在内容上呈现为空间性。艺术美与自然美共享美的本质，但因人文因素的融入而更具复杂性。艺术美同样源于主体让渡的自足性，但艺术创作主体通过长期自觉训练达到非自觉的自由状态，让渡的是一种“自由的自足性”。艺术创作的自由体现在对材料的驾驭以及对非目的性的自觉追求。因此，艺术美不仅内含创作主体所赋予的自由，其自足性也更丰富、更隽永，能超越自然美的表现局限。艺术创造是人类自由精神的最高体现，但并非主观任意，需受传统和材料限制。审美活动能被动地陶冶主体，内化艺术美的自足、自由和丰富性。对真、善、美内在统一的追求，不仅关乎艺术创造的分寸与责任，亦对人格的养成与共同体的教化具有根本意义。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026006001","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"6","authors":["杨立华"],"affils":["北京大学哲学系"],"title":"原美：关于美的内涵的理本论探索","titleEn":"Original Beauty: An Exploration of the Connotation of Beauty from the Foundation-of-Principle Theory","keywords":["美","理本论","自足性","时间性与空间性","艺术美"],"keywordsEn":[],"abstract":"以自然美为起点，通过现象学还原，可以发现美并非主观情感或客观属性，而是一种“自足性的显象”，其核心机制在于对象以其内在的完满性使审美主体进入一种被动领受状态。美在形式上呈现为时间性，在内容上呈现为空间性。艺术美与自然美共享美的本质，但因人文因素的融入而更具复杂性。艺术美同样源于主体让渡的自足性，但艺术创作主体通过长期自觉训练达到非自觉的自由状态，让渡的是一种“自由的自足性”。艺术创作的自由体现在对材料的驾驭以及对非目的性的自觉追求。因此，艺术美不仅内含创作主体所赋予的自由，其自足性也更丰富、更隽永，能超越自然美的表现局限。艺术创造是人类自由精神的最高体现，但并非主观任意，需受传统和材料限制。审美活动能被动地陶冶主体，内化艺术美的自足、自由和丰富性。对真、善、美内在统一的追求，不仅关乎艺术创造的分寸与责任，亦对人格的养成与共同体的教化具有根本意义。","abstractEn":"","pages":"5-14","doi":"","url":"https://www.ncpssd.org/Literature/secure/articleinfo?params=YTZZWG9xd1V0d05qTlcyUituakQ5VVBNRkNROURIQ0pCWXRlRmVtZFl5RnZVNWgzbnV3NUVaSXRHUUNuYVVGSlBrWTg1M2o4N3RJcExYaHBXNHVyWnFGd1E5ZHdqMTNUYStDZUdRK0JubjF1WUplQytadk82WFY1TEgyeE1xTmY","cite":"杨立华. 原美：关于美的内涵的理本论探索[J]. 社会科学, 2026(6): 5-14.","lenZh":378,"lenEn":0}},{"id":"archive/cssci-100:SHKX2026006005","collection":"archive/cssci-100","collection_title":"CSSCI原站100条双语语料（历史归档）","kind":"历史归档","title":"稳预期以促消费：双曲贴现机制下社会保险的消费促进效应研究","summary":"社会保险制度对促进居民消费、构建新发展格局具有重要意义。本文引入双曲贴现假说，从改善预期的角度构建了参加社会保险对家庭消费影响的解释框架，并分析了参加不同类型社会保险对家庭消费的差异性影响。基于中国家庭金融调查（CHFS）数据的实证分析表明，家庭参加社会保险总体上有利于促进家庭消费。相比而言，社会养老保险因为周期较长且待遇支付的条件明确，在双曲贴现的作用效应下对消费的影响具有时变性，而失业保险对消费的促进效应更加显著。同时，参加不同社会保险对不同类型家庭的消费影响和对消费结构的影响也具有异质性。基于此，建议持续扩大失业保险覆盖面、进一步降低养老保险缴费率，更加充分发挥各项社会保险制度对促进消费的积极作用。","url":"papers-archive.html","source":"data/archive/cssci-100.json","details":{"id":"SHKX2026006005","journal":"社会科学","journalEn":"Journal of Social Sciences","year":"2026","issue":"6","authors":["鲁全","周文静"],"affils":["中国人民大学劳动人事学院"],"title":"稳预期以促消费：双曲贴现机制下社会保险的消费促进效应研究","titleEn":"Promoting Household Consumption Through Stabilized Expectations: The Consumption-Enhancing Effects of Social Insurance Participation Under Hyperbolic Discounting 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DOI:10.19744/j.cnki.11-1235/f.2026.0099（卷期及完整页码待核）","doi":"10.19744/j.cnki.11-1235/f.2026.0099","url":"https://doi.org/10.19744/j.cnki.11-1235/f.2026.0099","locator":"原文第 141 页","kind":"已提供论文页面截图","processing":"整页截图；含原文上下文","sha256":"77bd7df389a24a29cefca667fe3eca08bee74c98045efe73888029b16d16a677","rights":"原刊与作者保留权利；许可未逐图核定","provenance":"用户要求归档的既有图表资料","verified_on":"2026-10-23"}},{"id":"figures:F026","collection":"figures","collection_title":"论文图表与图源","kind":"图表","title":"原图7_多面板结果图_原文143页","summary":"","url":"figures.html","source":"data/figures.json","details":{"id":"F026","title":"原图7_多面板结果图_原文143页","image":"assets/figures/figure-026.png","journal":"管理世界","citation":"臧文斌, 吴舒祥, 王一如, 陈晨. 分级诊疗制度下的医疗控费机制——基于大样本病案数据的研究证据[J]. 管理世界, 2026. DOI:10.19744/j.cnki.11-1235/f.2026.0099（卷期及完整页码待核）","doi":"10.19744/j.cnki.11-1235/f.2026.0099","url":"https://doi.org/10.19744/j.cnki.11-1235/f.2026.0099","locator":"原文第 143 页","kind":"已提供论文页面截图","processing":"整页截图；含原文上下文","sha256":"a8ba33f7e9b8f319dd34d36820da4228186c9441ada819dc3b96c1884b0744d4","rights":"原刊与作者保留权利；许可未逐图核定","provenance":"用户要求归档的既有图表资料","verified_on":"2026-10-23"}},{"id":"figures:F027","collection":"figures","collection_title":"论文图表与图源","kind":"图表","title":"原图8_总体与单位差异_原文146页","summary":"","url":"figures.html","source":"data/figures.json","details":{"id":"F027","title":"原图8_总体与单位差异_原文146页","image":"assets/figures/figure-027.png","journal":"管理世界","citation":"臧文斌, 吴舒祥, 王一如, 陈晨. 分级诊疗制度下的医疗控费机制——基于大样本病案数据的研究证据[J]. 管理世界, 2026. DOI:10.19744/j.cnki.11-1235/f.2026.0099（卷期及完整页码待核）","doi":"10.19744/j.cnki.11-1235/f.2026.0099","url":"https://doi.org/10.19744/j.cnki.11-1235/f.2026.0099","locator":"原文第 146 页","kind":"已提供论文页面截图","processing":"整页截图；含原文上下文","sha256":"383cc74449bfd974019f4c30d35c8f4fe6b84a23c72ca6fdbf6cdcf60a9add64","rights":"原刊与作者保留权利；许可未逐图核定","provenance":"用户要求归档的既有图表资料","verified_on":"2026-10-23"}},{"id":"methods:A1","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"横断面研究","summary":"**适用**：在某一时点同时测量暴露与结局，描述疾病分布或探索暴露-结局关联（如国家卫生服务调查、体检数据、居民健康素养调查）。\n\n**要点**：核心是一次性调查，暴露与结局同时收集、无时间先后，只能得关联不能定因果。患病率 = 现患病例数 / 调查人口数（时点患病率）；关联指标推荐患病率比 PR（log-binomial）或暴露患病比值比 POR，结局常见时 OR 会高估关联。抽样常为多阶段/分层抽样，需加权估计总体；典型偏倚有奈曼偏倚（病程长短影响入选，长病程者过度代表）、无应答偏倚与回忆偏倚。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nproportion disease                       // 患病率及 CI（默认 Wald）\nci proportions disease, wilson           // Wilson CI，小样本更稳\nglm disease exposed, family(binomial) link(log) eform   // PR；不收敛时换 poisson disease exposed, irr vce(robust)\n\n* ===== R =====\nx <- 45; n <- 300                        # 患病人数与调查人数\nprop.test(x, n, correct = FALSE)         # 患病率及近似 CI\nbinom","url":"methods.html","source":"data/methods.json","details":{"id":"A1","name":"横断面研究","category":"A","content":"**适用**：在某一时点同时测量暴露与结局，描述疾病分布或探索暴露-结局关联（如国家卫生服务调查、体检数据、居民健康素养调查）。\n\n**要点**：核心是一次性调查，暴露与结局同时收集、无时间先后，只能得关联不能定因果。患病率 = 现患病例数 / 调查人口数（时点患病率）；关联指标推荐患病率比 PR（log-binomial）或暴露患病比值比 POR，结局常见时 OR 会高估关联。抽样常为多阶段/分层抽样，需加权估计总体；典型偏倚有奈曼偏倚（病程长短影响入选，长病程者过度代表）、无应答偏倚与回忆偏倚。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nproportion disease                       // 患病率及 CI（默认 Wald）\nci proportions disease, wilson           // Wilson CI，小样本更稳\nglm disease exposed, family(binomial) link(log) eform   // PR；不收敛时换 poisson disease exposed, irr vce(robust)\n\n* ===== R =====\nx <- 45; n <- 300                        # 患病人数与调查人数\nprop.test(x, n, correct = FALSE)         # 患病率及近似 CI\nbinom.test(x, n)                         # 精确 Clopper-Pearson CI\nm <- glm(disease ~ exposed, family = binomial(link = \"log\"), data = d)\nexp(cbind(PR = coef(m), confint.default(m)))   # 患病率比 PR\n```\n\n**报告要点**：报告调查时点、抽样设计与加权方法；患病率须附 95% CI（注明方法），关联指标用 PR/POR 并说明为何不选 OR。\n\n**常见错误**：用 OR 解读高患病率结局、忽略复杂抽样权重直接做点估计。","source":"skills/references/methods-54.md"}},{"id":"methods:A2","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"病例对照研究","summary":"**适用**：结局已发生、回顾比较病例与对照的既往暴露，适合罕见病、长潜伏期结局的病因探索（如肿瘤、罕见不良反应）。\n\n**要点**：按结局状态选病例组与对照组（来自同一源人群），回顾收集暴露史；指标 OR = (a×d)/(b×c) = 病例组暴露比值 / 对照组暴露比值，罕见病时近似 RR。匹配设计（个体/频数匹配）提高效率并控制混杂，个体匹配通常采用条件 logistic；频数匹配可采用调整匹配因素的非条件 logistic，需按设计选择；不能直接估计发病率。典型偏倚：回忆偏倚、选择偏倚（如 Berkson 偏倚）、暴露测量偏倚。\n\n**代码**：\n\n```stata\n* ===== Stata =====\ntabulate exposed case, chi2\ncci 40 20 30 70          // 病例暴露/非暴露=40/20，对照暴露/非暴露=30/70 → OR 与 CI\nlogistic case exposed age, or              // 不匹配：多因素校正 OR\nclogit case exposed, group(pairid) or      // 1:1 匹配：条件 logistic\n\n* ===== R =====\nm <- matrix(c(40, 20, 30, 70), nrow = 2)   # 病例暴露/非暴露、对照暴露/非暴露\nfisher.test(m)                         ","url":"methods.html","source":"data/methods.json","details":{"id":"A2","name":"病例对照研究","category":"A","content":"**适用**：结局已发生、回顾比较病例与对照的既往暴露，适合罕见病、长潜伏期结局的病因探索（如肿瘤、罕见不良反应）。\n\n**要点**：按结局状态选病例组与对照组（来自同一源人群），回顾收集暴露史；指标 OR = (a×d)/(b×c) = 病例组暴露比值 / 对照组暴露比值，罕见病时近似 RR。匹配设计（个体/频数匹配）提高效率并控制混杂，个体匹配通常采用条件 logistic；频数匹配可采用调整匹配因素的非条件 logistic，需按设计选择；不能直接估计发病率。典型偏倚：回忆偏倚、选择偏倚（如 Berkson 偏倚）、暴露测量偏倚。\n\n**代码**：\n\n```stata\n* ===== Stata =====\ntabulate exposed case, chi2\ncci 40 20 30 70          // 病例暴露/非暴露=40/20，对照暴露/非暴露=30/70 → OR 与 CI\nlogistic case exposed age, or              // 不匹配：多因素校正 OR\nclogit case exposed, group(pairid) or      // 1:1 匹配：条件 logistic\n\n* ===== R =====\nm <- matrix(c(40, 20, 30, 70), nrow = 2)   # 病例暴露/非暴露、对照暴露/非暴露\nfisher.test(m)                             # 精确 OR 与 CI\nglm(case ~ exposed + age, family = binomial(), data = d)   # 校正 OR\nlibrary(survival)\nclogit(case ~ exposed + strata(pairid), data = d)          # 匹配条件 logistic\n```\n\n**报告要点**：报告病例/对照来源、匹配变量与匹配比、应答率；报告 OR 与 95% CI、校正变量与模型类型（条件/非条件）。\n\n**常见错误**：匹配数据用非条件 logistic（偏倚）、结局不罕见时把 OR 当 RR 解释。","source":"skills/references/methods-54.md"}},{"id":"methods:A3","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"队列研究","summary":"**适用**：按暴露状态分组、随访观察结局，用于验证病因或评估暴露效应，可直接计算发病率、同时观察多个结局（如出生队列、职业队列）。\n\n**要点**：暴露组与非暴露组前瞻（或回顾）随访；核心指标：累积发病率 CI = 发病数/期初人数、发病密度 ID = 发病数/人时。关联：RR = 暴露组发病率/非暴露组发病率（2×2 数据），人时数据用 Poisson 回归得 IRR，含删失的生存数据用 Cox 回归得 HR。典型偏倚：失访偏倚、健康工人效应、暴露/结局测量偏倚，混杂用分层 MH 或多变量模型控制。\n\n**代码**：\n\n```stata\n* ===== Stata =====\ncs disease exposed, exact                  // 2×2：RR 及 CI\nir disease exposed                         // 发病密度比 IRR\nstset time, failure(disease)               // 人时/生存数据\nstrate exposed                             // 各组人时与发病率\npoisson disease exposed, exposure(time) irr\nstcox exposed age                          // 校正 HR\n\n* ===== R =====\nlibrary(epiR)\nepi.2b","url":"methods.html","source":"data/methods.json","details":{"id":"A3","name":"队列研究","category":"A","content":"**适用**：按暴露状态分组、随访观察结局，用于验证病因或评估暴露效应，可直接计算发病率、同时观察多个结局（如出生队列、职业队列）。\n\n**要点**：暴露组与非暴露组前瞻（或回顾）随访；核心指标：累积发病率 CI = 发病数/期初人数、发病密度 ID = 发病数/人时。关联：RR = 暴露组发病率/非暴露组发病率（2×2 数据），人时数据用 Poisson 回归得 IRR，含删失的生存数据用 Cox 回归得 HR。典型偏倚：失访偏倚、健康工人效应、暴露/结局测量偏倚，混杂用分层 MH 或多变量模型控制。\n\n**代码**：\n\n```stata\n* ===== Stata =====\ncs disease exposed, exact                  // 2×2：RR 及 CI\nir disease exposed                         // 发病密度比 IRR\nstset time, failure(disease)               // 人时/生存数据\nstrate exposed                             // 各组人时与发病率\npoisson disease exposed, exposure(time) irr\nstcox exposed age                          // 校正 HR\n\n* ===== R =====\nlibrary(epiR)\nepi.2by2(matrix(c(45, 955, 20, 980), nrow = 2), method = \"cohort.count\")\nglm(disease ~ exposed + offset(log(time)), family = poisson(), data = d)  # IRR\nlibrary(survival)\ncoxph(Surv(time, disease) ~ exposed + age, data = d)                      # HR\n```\n\n**报告要点**：报告随访起止、人时、失访率与删失规则；报告累积发病率/发病密度及 RR/IRR/HR 的 95% CI 与校正模型。\n\n**常见错误**：忽略人时把发病密度当累积发病率，或随访长短不一却不用生存分析。","source":"skills/references/methods-54.md"}},{"id":"methods:A4","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"随机对照试验 RCT","summary":"**适用**：评价干预效果的金标准，随机分配均衡已知与未知混杂，用于药物、诊疗方案与卫生服务干预的效果评价。\n\n**要点**：要素为随机分配（简单/区组/分层）、对照（安慰剂/标准治疗）、盲法（单盲/双盲），平行组为主，另有交叉与整群设计。首要分析人群为 ITT（意向性分析）；效应量：二分类结局用 RR、RD 与 NNT（NNT = 1/RD），连续结局用均数差 MD。报告遵循 CONSORT（注册号、随机序列、分配隐藏、盲法、流程图）。整群 RCT 需按设计效应 DE = 1 + (m−1)×ICC 扩大样本量。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nset obs 120\nset seed 2026\negen block = seq(), to(30) block(4)       // 30 个区组、组内 4 人\ngen treat = rbinomial(1, 0.5)             // 简单随机（严格 2:2 区组需再抽样）\ntab treat\ncsi 20 10 25 5                            // 干预组事件/非事件=20/10，对照组=25/5 → RD、RR 及 CI；NNT=1/RD\nttest y, by(treat)                        // 连续结局：MD\nregress y treat, vce(robust)\n\n* ===== R =====","url":"methods.html","source":"data/methods.json","details":{"id":"A4","name":"随机对照试验 RCT","category":"A","content":"**适用**：评价干预效果的金标准，随机分配均衡已知与未知混杂，用于药物、诊疗方案与卫生服务干预的效果评价。\n\n**要点**：要素为随机分配（简单/区组/分层）、对照（安慰剂/标准治疗）、盲法（单盲/双盲），平行组为主，另有交叉与整群设计。首要分析人群为 ITT（意向性分析）；效应量：二分类结局用 RR、RD 与 NNT（NNT = 1/RD），连续结局用均数差 MD。报告遵循 CONSORT（注册号、随机序列、分配隐藏、盲法、流程图）。整群 RCT 需按设计效应 DE = 1 + (m−1)×ICC 扩大样本量。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nset obs 120\nset seed 2026\negen block = seq(), to(30) block(4)       // 30 个区组、组内 4 人\ngen treat = rbinomial(1, 0.5)             // 简单随机（严格 2:2 区组需再抽样）\ntab treat\ncsi 20 10 25 5                            // 干预组事件/非事件=20/10，对照组=25/5 → RD、RR 及 CI；NNT=1/RD\nttest y, by(treat)                        // 连续结局：MD\nregress y treat, vce(robust)\n\n* ===== R =====\nset.seed(2026)\nd$treat <- sample(rep(0:1, 60))           # 120 人简单随机（可用 blockrand 做区组）\nlibrary(epiR)\nepi.2by2(table(d$treat, d$outcome), method = \"cohort.count\")   # RR/RD/NNT\nt.test(y ~ treat, data = d)\nlm(y ~ treat, data = d)\n```\n\n**报告要点**：报告注册号、随机方法与分配隐藏、盲法、CONSORT 流程图；同时报告 ITT（与 PP）结果、效应量及 95% CI（NNT 须附 CI）。\n\n**常见错误**：用 PP 替代 ITT 下结论、未报告分配隐藏，或整群随机忽略设计效应。","source":"skills/references/methods-54.md"}},{"id":"methods:A5","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"准实验设计","summary":"**适用**：无法随机分配时评价政策/干预效果的次优选择，如自然实验、政策评估（医改前后比较、地区间政策差异）。\n\n**要点**：常见类型：前后对照、非等同对照组（DID）、间断时间序列 ITS、断点回归 RDD。DID =（干预组前后差）−（对照组前后差），以组×时间交互项系数估计，前提是平行趋势；ITS 用分段回归，干预后水平变化与斜率变化分开估计。典型威胁：历史因素、向均数回归、霍桑效应与选择偏倚，须做平行趋势/安慰剂检验。\n\n**代码**：\n\n```stata\n* ===== Stata =====\ngen did = treat * post\nregress y treat post did, vce(cluster id)        // did 系数即 DID 估计\nxtset id time\nxtreg y ib0.time#i.treat i.time, fe vce(cluster id)   // 事件研究：检验平行趋势\ngen time_after = max(0, time - t0)               // ITS 分段回归（t0 = 干预时点）\nnewey y time post time_after, lag(1)             // Newey-West 处理自相关\n\n* ===== R =====\nm <- lm(y ~ treat * post, data = d)              # 交互项系数即 DID\n","url":"methods.html","source":"data/methods.json","details":{"id":"A5","name":"准实验设计","category":"A","content":"**适用**：无法随机分配时评价政策/干预效果的次优选择，如自然实验、政策评估（医改前后比较、地区间政策差异）。\n\n**要点**：常见类型：前后对照、非等同对照组（DID）、间断时间序列 ITS、断点回归 RDD。DID =（干预组前后差）−（对照组前后差），以组×时间交互项系数估计，前提是平行趋势；ITS 用分段回归，干预后水平变化与斜率变化分开估计。典型威胁：历史因素、向均数回归、霍桑效应与选择偏倚，须做平行趋势/安慰剂检验。\n\n**代码**：\n\n```stata\n* ===== Stata =====\ngen did = treat * post\nregress y treat post did, vce(cluster id)        // did 系数即 DID 估计\nxtset id time\nxtreg y ib0.time#i.treat i.time, fe vce(cluster id)   // 事件研究：检验平行趋势\ngen time_after = max(0, time - t0)               // ITS 分段回归（t0 = 干预时点）\nnewey y time post time_after, lag(1)             // Newey-West 处理自相关\n\n* ===== R =====\nm <- lm(y ~ treat * post, data = d)              # 交互项系数即 DID\nlibrary(sandwich); library(lmtest)\ncoeftest(m, vcov = vcovCL(m, cluster = ~id))     # 聚类稳健标准误\nd$time_after <- pmax(0, d$time - t0)\nits <- lm(y ~ time + post + time_after, data = d)   # ITS 分段回归\n```\n\n**报告要点**：报告设计类型与可比性证据（基线平衡、平行趋势图、安慰剂检验）；报告 DID 系数或 ITS 水平/斜率变化及 95% CI 与稳健标准误类型。\n\n**常见错误**：不检验平行趋势就下因果结论，或 ITS 忽略自相关直接用 OLS 标准误。","source":"skills/references/methods-54.md"}},{"id":"methods:A6","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"生态学研究","summary":"**适用**：以群体（国家/地区/年份）为分析单位探索宏观暴露与结局关联，用于跨国比较、政策环境因素分析与假说生成，数据易得、成本低。\n\n**要点**：分析单位是群体，暴露与结局均为群体汇总值，关联指标为相关系数、加权回归斜率或标化率比；比较地区率前须标准化年龄结构。核心局限是生态学谬误——群体关联不能推论到个体（跨层推论需多水平模型）；典型混杂为未测量的群体因素（医疗可及性、经济水平）。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nregress rate exposure [aweight = pop]           // 按地区人口加权\ncorr rate exposure\ndstdize deaths pop, by(agegrp) using(stdpop)    // 直接标化；stdpop.dta 含 agegrp 与 pop\n\n* ===== R =====\nm <- lm(rate ~ exposure, data = d, weights = pop)\ncor(d$rate, d$exposure)\nlibrary(epitools)\nageadjust.direct(count = d$deaths, pop = d$pop, stdpop = ref$stdpop)  # 直接标化\n```\n\n**报告要点**：报告分析单位与群体指标定义、是否加权/标化；明确“生态学谬误”局限，结论限定在群体层面。\n\n**常见错误**","url":"methods.html","source":"data/methods.json","details":{"id":"A6","name":"生态学研究","category":"A","content":"**适用**：以群体（国家/地区/年份）为分析单位探索宏观暴露与结局关联，用于跨国比较、政策环境因素分析与假说生成，数据易得、成本低。\n\n**要点**：分析单位是群体，暴露与结局均为群体汇总值，关联指标为相关系数、加权回归斜率或标化率比；比较地区率前须标准化年龄结构。核心局限是生态学谬误——群体关联不能推论到个体（跨层推论需多水平模型）；典型混杂为未测量的群体因素（医疗可及性、经济水平）。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nregress rate exposure [aweight = pop]           // 按地区人口加权\ncorr rate exposure\ndstdize deaths pop, by(agegrp) using(stdpop)    // 直接标化；stdpop.dta 含 agegrp 与 pop\n\n* ===== R =====\nm <- lm(rate ~ exposure, data = d, weights = pop)\ncor(d$rate, d$exposure)\nlibrary(epitools)\nageadjust.direct(count = d$deaths, pop = d$pop, stdpop = ref$stdpop)  # 直接标化\n```\n\n**报告要点**：报告分析单位与群体指标定义、是否加权/标化；明确“生态学谬误”局限，结论限定在群体层面。\n\n**常见错误**：把群体关联外推为个体因果（生态学谬误），或不标化年龄结构直接比较地区率。","source":"skills/references/methods-54.md"}},{"id":"methods:A7","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"混合方法研究（定性+定量）","summary":"**适用**：单一方法不足以回答研究问题时，定性（机制、体验、实施障碍）与定量（测量、检验）互补，常见于卫生服务研究、实施科学与量表开发。\n\n**要点**：核心设计：聚敛（并行）、解释性顺序（先量后质）、探索性顺序（先质后量）、嵌入式；须明确整合点与整合方式（如 joint display 联合展示）。抽样上定量用概率抽样、定性用目的抽样（最大变异/典型/极端案例），定性样本量以信息饱和为原则。质量报告：定性遵循 COREQ，整体遵循 GRAMMS/MMARS，三角互证提升效度。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nttest score, by(group)                 // 定量侧比较\ntab theme group, chi2                  // 定性主题（编码后）与分组交叉：量化整合\n\n* ===== R =====\nlibrary(quanteda)\ndfm <- dfm(tokens(interview_text, remove_punct = TRUE))   # 定性文本：词频辅助主题\ntopfeatures(dfm, 20)\nt.test(score ~ group, data = d)                           # 定量侧\nchisq.test(table(d$theme, d$group))                       # 主题×分组整","url":"methods.html","source":"data/methods.json","details":{"id":"A7","name":"混合方法研究（定性+定量）","category":"A","content":"**适用**：单一方法不足以回答研究问题时，定性（机制、体验、实施障碍）与定量（测量、检验）互补，常见于卫生服务研究、实施科学与量表开发。\n\n**要点**：核心设计：聚敛（并行）、解释性顺序（先量后质）、探索性顺序（先质后量）、嵌入式；须明确整合点与整合方式（如 joint display 联合展示）。抽样上定量用概率抽样、定性用目的抽样（最大变异/典型/极端案例），定性样本量以信息饱和为原则。质量报告：定性遵循 COREQ，整体遵循 GRAMMS/MMARS，三角互证提升效度。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nttest score, by(group)                 // 定量侧比较\ntab theme group, chi2                  // 定性主题（编码后）与分组交叉：量化整合\n\n* ===== R =====\nlibrary(quanteda)\ndfm <- dfm(tokens(interview_text, remove_punct = TRUE))   # 定性文本：词频辅助主题\ntopfeatures(dfm, 20)\nt.test(score ~ group, data = d)                           # 定量侧\nchisq.test(table(d$theme, d$group))                       # 主题×分组整合\n```\n\n**报告要点**：报告设计类型（顺序/并行）与整合点、两部分抽样与分析方法；定性部分附 COREQ 清单，整合结果用 joint display 呈现。\n\n**常见错误**：两部分平行发表而无真正整合，或定性样本套用概率抽样逻辑、未报告理论视角与反思性。","source":"skills/references/methods-54.md"}},{"id":"methods:A8","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"德尔菲法（Delphi）","summary":"**适用**：证据不足或意见分歧时，通过多轮匿名专家咨询收敛共识，用于指标筛选、指南推荐、评价体系构建（如卫生管理指标体系）。\n\n**要点**：匿名多轮问卷（通常 2–3 轮），每轮反馈统计汇总（均数、变异系数 CV、满分比），专家据反馈修改意见直至收敛。指标：专家积极系数（回收率）、权威系数 Cr = (Ca + Cs)/2（Ca 判断依据、Cs 熟悉程度）、协调度（肯德尔和谐系数 W、CV）。专家一般 15–50 人；条目筛选常用均数 ≥ 3.5 且 CV ≤ 0.25（阈值因研究而异）。\n\n**代码**：\n\n```stata\n* ===== Stata =====\negen mean_score = mean(score), by(item)\negen sd_score = sd(score), by(item)\ngen cv = sd_score / mean_score\nlist item mean_score cv if mean_score >= 3.5 & cv <= 0.25   // 示例筛选阈值\nssc install concord\nconcord score1-score15                       // 各专家评分变量 → 肯德尔 W\n\n* ===== R =====\nlibrary(dplyr)\nd |> group_by(item) |>\n  summarise(m = mean(score), cv = sd(score)/m","url":"methods.html","source":"data/methods.json","details":{"id":"A8","name":"德尔菲法（Delphi）","category":"A","content":"**适用**：证据不足或意见分歧时，通过多轮匿名专家咨询收敛共识，用于指标筛选、指南推荐、评价体系构建（如卫生管理指标体系）。\n\n**要点**：匿名多轮问卷（通常 2–3 轮），每轮反馈统计汇总（均数、变异系数 CV、满分比），专家据反馈修改意见直至收敛。指标：专家积极系数（回收率）、权威系数 Cr = (Ca + Cs)/2（Ca 判断依据、Cs 熟悉程度）、协调度（肯德尔和谐系数 W、CV）。专家一般 15–50 人；条目筛选常用均数 ≥ 3.5 且 CV ≤ 0.25（阈值因研究而异）。\n\n**代码**：\n\n```stata\n* ===== Stata =====\negen mean_score = mean(score), by(item)\negen sd_score = sd(score), by(item)\ngen cv = sd_score / mean_score\nlist item mean_score cv if mean_score >= 3.5 & cv <= 0.25   // 示例筛选阈值\nssc install concord\nconcord score1-score15                       // 各专家评分变量 → 肯德尔 W\n\n* ===== R =====\nlibrary(dplyr)\nd |> group_by(item) |>\n  summarise(m = mean(score), cv = sd(score)/mean(score)) |>\n  filter(m >= 3.5 & cv <= 0.25)              # 示例筛选阈值\nlibrary(irr)\nw <- kendall(t(as.matrix(xtabs(score ~ expert + item, data = d))))   # 行为条目、列为专家\nw$value                                      # 肯德尔和谐系数 W\n```\n\n**报告要点**：报告轮数、每轮专家数与积极系数、权威系数、筛选标准与保留/删除条目清单、协调系数及其显著性检验。\n\n**常见错误**：轮间无统计反馈直接计分、只看均数忽略离散度与协调度检验，专家过于同质却称“共识”。\n\n---","source":"skills/references/methods-54.md"}},{"id":"methods:B1","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"描述统计与正态性检验","summary":"**适用**：任何连续型变量的分布描述与正态性判断，是选择后续参数或非参数方法的起点。\n\n**要点**：连续资料先报 n、均值±SD（正态）或中位数（P25–P75，偏态）与最小–最大值，`summarize, detail` 还给出偏度/峰度。正态性判断不能只看 Shapiro-Wilk 检验（大样本时几乎总拒绝），要结合直方图与 Q-Q 图，并参考 |偏度|<2、|峰度|<7 等经验界值。偏态明显时报告并分析中位数（IQR），可先尝试对数/平方根变换，或直接改用非参数方法。\n\n**代码**：\n\n```stata+r\n* Stata\nsummarize y, detail              // n、均值、百分位数、偏度峰度\nswilk y                          // Shapiro-Wilk 正态性检验\nhistogram y, normal              // 直方图叠加正态密度曲线\nqnorm y                          // 分位-分位（Q-Q）图","url":"methods.html","source":"data/methods.json","details":{"id":"B1","name":"描述统计与正态性检验","category":"B","content":"**适用**：任何连续型变量的分布描述与正态性判断，是选择后续参数或非参数方法的起点。\n\n**要点**：连续资料先报 n、均值±SD（正态）或中位数（P25–P75，偏态）与最小–最大值，`summarize, detail` 还给出偏度/峰度。正态性判断不能只看 Shapiro-Wilk 检验（大样本时几乎总拒绝），要结合直方图与 Q-Q 图，并参考 |偏度|<2、|峰度|<7 等经验界值。偏态明显时报告并分析中位数（IQR），可先尝试对数/平方根变换，或直接改用非参数方法。\n\n**代码**：\n\n```stata+r\n* Stata\nsummarize y, detail              // n、均值、百分位数、偏度峰度\nswilk y                          // Shapiro-Wilk 正态性检验\nhistogram y, normal              // 直方图叠加正态密度曲线\nqnorm y                          // 分位-分位（Q-Q）图","source":"skills/references/methods-54.md"}},{"id":"methods:B2","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"t 检验与方差分析","summary":"**适用**：两组（t 检验）或三组及以上（方差分析）连续结局的组间比较。\n\n**要点**：两组独立样本用独立样本 t 检验，方差齐性不满足时用 Welch 校正（`unequal`）；同一对象前后两次测量用配对 t 检验。三组及以上用单因素方差分析（ANOVA），F 显著后再做两两比较并用 Bonferroni/Tukey 校正多重性。多组反复做两两 t 检验会膨胀 I 类错误。\n\n**代码**：\n\n```stata+r\n* Stata\nttest y, by(group) unequal       // Welch t 检验（不假定方差齐）\nttest y1 == y2                   // 配对 t 检验\noneway y group, bonferroni       // 单因素 ANOVA + Bonferroni 两两比较\nanova y group                    // 方差分析表","url":"methods.html","source":"data/methods.json","details":{"id":"B2","name":"t 检验与方差分析","category":"B","content":"**适用**：两组（t 检验）或三组及以上（方差分析）连续结局的组间比较。\n\n**要点**：两组独立样本用独立样本 t 检验，方差齐性不满足时用 Welch 校正（`unequal`）；同一对象前后两次测量用配对 t 检验。三组及以上用单因素方差分析（ANOVA），F 显著后再做两两比较并用 Bonferroni/Tukey 校正多重性。多组反复做两两 t 检验会膨胀 I 类错误。\n\n**代码**：\n\n```stata+r\n* Stata\nttest y, by(group) unequal       // Welch t 检验（不假定方差齐）\nttest y1 == y2                   // 配对 t 检验\noneway y group, bonferroni       // 单因素 ANOVA + Bonferroni 两两比较\nanova y group                    // 方差分析表","source":"skills/references/methods-54.md"}},{"id":"methods:B3","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"卡方检验与非参数检验","summary":"**适用**：分类结局的组间比较，以及不满足正态/方差齐性条件的连续或等级资料的组间比较。\n\n**要点**：四格表先看期望频数：n≥40 且所有期望频数≥5 用 Pearson 卡方，否则用 Fisher 精确检验。等级资料或严重偏态的连续资料，两独立组用 Mann-Whitney U（`ranksum`），配对用 Wilcoxon 符号秩（`signrank`），多组用 Kruskal-Wallis（`kwallis`）。非参数检验比较的是分布/秩次而非均数，结论措辞要相应调整。\n\n**代码**：\n\n```stata+r\n* Stata\ntab a b, chi2 exact              // Pearson 卡方 + Fisher 精确检验\nranksum y, by(group)             // Mann-Whitney U（Wilcoxon 秩和）\nsignrank y1 = y2                 // 配对 Wilcoxon 符号秩检验\nkwallis y, by(group)             // Kruskal-Wallis 检验","url":"methods.html","source":"data/methods.json","details":{"id":"B3","name":"卡方检验与非参数检验","category":"B","content":"**适用**：分类结局的组间比较，以及不满足正态/方差齐性条件的连续或等级资料的组间比较。\n\n**要点**：四格表先看期望频数：n≥40 且所有期望频数≥5 用 Pearson 卡方，否则用 Fisher 精确检验。等级资料或严重偏态的连续资料，两独立组用 Mann-Whitney U（`ranksum`），配对用 Wilcoxon 符号秩（`signrank`），多组用 Kruskal-Wallis（`kwallis`）。非参数检验比较的是分布/秩次而非均数，结论措辞要相应调整。\n\n**代码**：\n\n```stata+r\n* Stata\ntab a b, chi2 exact              // Pearson 卡方 + Fisher 精确检验\nranksum y, by(group)             // Mann-Whitney U（Wilcoxon 秩和）\nsignrank y1 = y2                 // 配对 Wilcoxon 符号秩检验\nkwallis y, by(group)             // Kruskal-Wallis 检验","source":"skills/references/methods-54.md"}},{"id":"methods:B4","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"相关与简单线性回归","summary":"**适用**：两个连续变量间线性关联的方向与强度描述（相关），及一个变量预测另一个变量的简单线性回归。\n\n**要点**：双变量均近正态时用 Pearson r，否则用 Spearman ρ；r 只衡量线性关联强度，不表示因果。回归分析前必须画散点图确认线性趋势并识别离群点，回归系数 β 报告 95% CI，R² 表示结局方差被解释的比例。拟合后应检查残差的正态性、独立性与方差齐性。\n\n**代码**：\n\n```stata+r\n* Stata\nscatter y x                     // 先画散点图判断线性\npwcorr x y, sig                 // Pearson r 及 p 值\nspearman x y                    // Spearman ρ\nregress y x                     // 简单线性回归","url":"methods.html","source":"data/methods.json","details":{"id":"B4","name":"相关与简单线性回归","category":"B","content":"**适用**：两个连续变量间线性关联的方向与强度描述（相关），及一个变量预测另一个变量的简单线性回归。\n\n**要点**：双变量均近正态时用 Pearson r，否则用 Spearman ρ；r 只衡量线性关联强度，不表示因果。回归分析前必须画散点图确认线性趋势并识别离群点，回归系数 β 报告 95% CI，R² 表示结局方差被解释的比例。拟合后应检查残差的正态性、独立性与方差齐性。\n\n**代码**：\n\n```stata+r\n* Stata\nscatter y x                     // 先画散点图判断线性\npwcorr x y, sig                 // Pearson r 及 p 值\nspearman x y                    // Spearman ρ\nregress y x                     // 简单线性回归","source":"skills/references/methods-54.md"}},{"id":"methods:B5","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"二元 logistic 回归","summary":"**适用**：二分类结局（患病与否、是否就诊、是否满意）的影响因素分析，结果以比值比 OR 解释。\n\n**要点**：模型为 logit(p)=β₀+Σβᵢxᵢ，OR=exp(β) 表示自变量每增加 1 个单位时结局「发生/不发生」比值的变化倍数；分类自变量必须设参照组（虚拟变量）。报告整体模型的似然比检验、区分度（AUC）与校准（Hosmer-Lemeshow）。结局事件数太少（每自变量不足约 10 个事件，EPV<10）时模型不稳定，应减少自变量。\n\n**代码**：\n\n```stata+r\n* Stata\nlogit y x1 x2, or               // 二元 logistic，报告 OR\nestat gof, group(10)            // Hosmer-Lemeshow 校准检验\nlroc                            // ROC 曲线与 AUC","url":"methods.html","source":"data/methods.json","details":{"id":"B5","name":"二元 logistic 回归","category":"B","content":"**适用**：二分类结局（患病与否、是否就诊、是否满意）的影响因素分析，结果以比值比 OR 解释。\n\n**要点**：模型为 logit(p)=β₀+Σβᵢxᵢ，OR=exp(β) 表示自变量每增加 1 个单位时结局「发生/不发生」比值的变化倍数；分类自变量必须设参照组（虚拟变量）。报告整体模型的似然比检验、区分度（AUC）与校准（Hosmer-Lemeshow）。结局事件数太少（每自变量不足约 10 个事件，EPV<10）时模型不稳定，应减少自变量。\n\n**代码**：\n\n```stata+r\n* Stata\nlogit y x1 x2, or               // 二元 logistic，报告 OR\nestat gof, group(10)            // Hosmer-Lemeshow 校准检验\nlroc                            // ROC 曲线与 AUC","source":"skills/references/methods-54.md"}},{"id":"methods:B6","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"有序/多项 logistic 回归","summary":"**适用**：等级结局（Likert 量表、疾病分期等有序分类）与无序多分类结局（就医机构类型、职业类别等）的影响因素分析。\n\n**要点**：有序结局用有序 logistic（比例优势模型），前提是比例优势假设（各切点回归系数相同），用 Brant 检验验证；满足时报一个公共 OR，不满足改用广义有序 logit（`gologit2`）或多项模型。无序多分类用多项 logistic，选定一类为参照，其余各类分别与参照类比较，系数指数化后为相对风险比 RRR。\n\n**代码**：\n\n```stata+r\n* Stata\nologit y x1 x2, or              // 有序 logistic（比例优势模型）\nbrant, detail                   // 比例优势/平行线假设检验（SSC 安装）\nmlogit y x1 x2, rrr base(1)     // 多项 logistic，参照组=1，报 RRR","url":"methods.html","source":"data/methods.json","details":{"id":"B6","name":"有序/多项 logistic 回归","category":"B","content":"**适用**：等级结局（Likert 量表、疾病分期等有序分类）与无序多分类结局（就医机构类型、职业类别等）的影响因素分析。\n\n**要点**：有序结局用有序 logistic（比例优势模型），前提是比例优势假设（各切点回归系数相同），用 Brant 检验验证；满足时报一个公共 OR，不满足改用广义有序 logit（`gologit2`）或多项模型。无序多分类用多项 logistic，选定一类为参照，其余各类分别与参照类比较，系数指数化后为相对风险比 RRR。\n\n**代码**：\n\n```stata+r\n* Stata\nologit y x1 x2, or              // 有序 logistic（比例优势模型）\nbrant, detail                   // 比例优势/平行线假设检验（SSC 安装）\nmlogit y x1 x2, rrr base(1)     // 多项 logistic，参照组=1，报 RRR","source":"skills/references/methods-54.md"}},{"id":"methods:B7","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"Poisson 与负二项回归","summary":"**适用**：计数结局（就诊次数、住院天数、跌倒次数、处方数等）的回归建模。\n\n**要点**：Poisson 回归假设均数=方差；存在过度离散（方差明显大于均数）时标准误被低估，改用负二项回归（`nbreg`/`glm.nb`）或 Poisson+稳健标准误。随访/暴露时间不等时必须加 `exposure()`（Stata）或 `offset(log(t))`（R）按人时调整。结果以发病率比 IRR=exp(β) 解释；零计数过多可考虑零膨胀模型（zip/zinb）。\n\n**代码**：\n\n```stata+r\n* Stata\npoisson y x1 x2, irr vce(robust)          // Poisson + 稳健标准误\nnbreg y x1 x2, irr                        // 负二项回归\npoisson y x1 x2, irr exposure(t)          // 随访时间不等：按 t 调整","url":"methods.html","source":"data/methods.json","details":{"id":"B7","name":"Poisson 与负二项回归","category":"B","content":"**适用**：计数结局（就诊次数、住院天数、跌倒次数、处方数等）的回归建模。\n\n**要点**：Poisson 回归假设均数=方差；存在过度离散（方差明显大于均数）时标准误被低估，改用负二项回归（`nbreg`/`glm.nb`）或 Poisson+稳健标准误。随访/暴露时间不等时必须加 `exposure()`（Stata）或 `offset(log(t))`（R）按人时调整。结果以发病率比 IRR=exp(β) 解释；零计数过多可考虑零膨胀模型（zip/zinb）。\n\n**代码**：\n\n```stata+r\n* Stata\npoisson y x1 x2, irr vce(robust)          // Poisson + 稳健标准误\nnbreg y x1 x2, irr                        // 负二项回归\npoisson y x1 x2, irr exposure(t)          // 随访时间不等：按 t 调整","source":"skills/references/methods-54.md"}},{"id":"methods:B8","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"生存分析（Kaplan-Meier 与 Cox）","summary":"**适用**：事件发生时间结局（再入院时间、死亡时间、并发症出现时间等），数据含删失。\n\n**要点**：先用 `stset` 定义随访时间与事件指示变量（0=删失、1=事件），编码弄反会使 KM 曲线与风险集全部出错。Kaplan-Meier 法估计生存函数并画曲线，log-rank 检验比较组间差异；Cox 模型估计风险比 HR 并做多变量调整。必须用 Schoenfeld 残差（`estat phtest`/`cox.zph`）检验比例风险（PH）假设，违反时改用分层 Cox 或时依协变量模型。\n\n**代码**：\n\n```stata+r\n* Stata\nstset time, failure(event)               // event=1 事件，0 删失\nsts graph, by(group)                     // Kaplan-Meier 生存曲线\nsts test group                           // log-rank 检验\nstcox x1 x2                              // Cox 比例风险模型（HR）\nestat phtest, detail                     // PH 假设检验","url":"methods.html","source":"data/methods.json","details":{"id":"B8","name":"生存分析（Kaplan-Meier 与 Cox）","category":"B","content":"**适用**：事件发生时间结局（再入院时间、死亡时间、并发症出现时间等），数据含删失。\n\n**要点**：先用 `stset` 定义随访时间与事件指示变量（0=删失、1=事件），编码弄反会使 KM 曲线与风险集全部出错。Kaplan-Meier 法估计生存函数并画曲线，log-rank 检验比较组间差异；Cox 模型估计风险比 HR 并做多变量调整。必须用 Schoenfeld 残差（`estat phtest`/`cox.zph`）检验比例风险（PH）假设，违反时改用分层 Cox 或时依协变量模型。\n\n**代码**：\n\n```stata+r\n* Stata\nstset time, failure(event)               // event=1 事件，0 删失\nsts graph, by(group)                     // Kaplan-Meier 生存曲线\nsts test group                           // log-rank 检验\nstcox x1 x2                              // Cox 比例风险模型（HR）\nestat phtest, detail                     // PH 假设检验","source":"skills/references/methods-54.md"}},{"id":"methods:B9","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"重复测量与混合效应模型","summary":"**适用**：同一对象多次随访的重复测量数据，或患者嵌套于医院/村等层级（聚类）数据的建模。\n\n**要点**：重复测量数据各次观测彼此相关，不能当独立样本处理；球形假设满足时可用重复测量 ANOVA，否则用线性混合效应模型。混合模型以随机截距/随机斜率刻画个体轨迹差异，报告重点是固定效应 β 及其 95% CI，随机效应与协方差结构的选择用 AIC/BIC 或似然比检验判断。二分类结局改用广义混合模型（`melogit`/`glmer`）。\n\n**代码**：\n\n```stata+r\n* Stata\nxtset id time\nmixed y x##c.time || id: time, reml      // 随机截距 + 随机斜率\nmixed y x##c.time || id:                 // 仅随机截距（供比较）\nestat ic                                 // AIC/BIC 比较\n* 二分类结局改用：melogit y x || id:","url":"methods.html","source":"data/methods.json","details":{"id":"B9","name":"重复测量与混合效应模型","category":"B","content":"**适用**：同一对象多次随访的重复测量数据，或患者嵌套于医院/村等层级（聚类）数据的建模。\n\n**要点**：重复测量数据各次观测彼此相关，不能当独立样本处理；球形假设满足时可用重复测量 ANOVA，否则用线性混合效应模型。混合模型以随机截距/随机斜率刻画个体轨迹差异，报告重点是固定效应 β 及其 95% CI，随机效应与协方差结构的选择用 AIC/BIC 或似然比检验判断。二分类结局改用广义混合模型（`melogit`/`glmer`）。\n\n**代码**：\n\n```stata+r\n* Stata\nxtset id time\nmixed y x##c.time || id: time, reml      // 随机截距 + 随机斜率\nmixed y x##c.time || id:                 // 仅随机截距（供比较）\nestat ic                                 // AIC/BIC 比较\n* 二分类结局改用：melogit y x || id:","source":"skills/references/methods-54.md"}},{"id":"methods:B10","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"主成分分析与因子分析","summary":"**适用**：多个指标降维合成综合得分（主成分分析），或探索量表/问卷的潜在结构（探索性因子分析）。\n\n**要点**：先做适用性检验：KMO>0.6 可接受（>0.8 好）、Bartlett 球形检验 p<0.05，不满足则不适合因子分析。主成分数按特征值>1、碎石图肘点或累计方差解释率（如≥60–80%）确定；因子分析定因子数优先用平行分析，并选择旋转方法（正交 varimax 或斜交 promax）。载荷 |λ|≥0.4 视为有实际意义，量表类结果还应报告 Cronbach's α。\n\n**代码**：\n\n```stata+r\n* Stata\npca x1-x10                          // 主成分分析\nfactor x1-x10, pcf                  // 因子分析（主成分因子法）\nestat kmo                           // KMO（Bartlett 检验：ssc install factortest）\nrotate, varimax                     // 正交旋转后看载荷\nalpha x1-x10                        // Cronbach's α","url":"methods.html","source":"data/methods.json","details":{"id":"B10","name":"主成分分析与因子分析","category":"B","content":"**适用**：多个指标降维合成综合得分（主成分分析），或探索量表/问卷的潜在结构（探索性因子分析）。\n\n**要点**：先做适用性检验：KMO>0.6 可接受（>0.8 好）、Bartlett 球形检验 p<0.05，不满足则不适合因子分析。主成分数按特征值>1、碎石图肘点或累计方差解释率（如≥60–80%）确定；因子分析定因子数优先用平行分析，并选择旋转方法（正交 varimax 或斜交 promax）。载荷 |λ|≥0.4 视为有实际意义，量表类结果还应报告 Cronbach's α。\n\n**代码**：\n\n```stata+r\n* Stata\npca x1-x10                          // 主成分分析\nfactor x1-x10, pcf                  // 因子分析（主成分因子法）\nestat kmo                           // KMO（Bartlett 检验：ssc install factortest）\nrotate, varimax                     // 正交旋转后看载荷\nalpha x1-x10                        // Cronbach's α","source":"skills/references/methods-54.md"}},{"id":"methods:C1","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"成本测算","summary":"**适用**：测算某一疾病、干预或服务在特定视角与时间段内消耗的资源总量（货币化成本），为成本分析、CEA/BIA 提供成本输入。\n**要点**：先明确测算视角（社会 / 卫生体系 / 医保 / 患者）与时间范围，视角决定纳入范围。\n成本分直接成本（直接医疗 + 直接非医疗）与间接成本（生产力损失，常用人力资本法按工资估算）。\n按「识别—测量—估值」三步：识别资源项目 → 测量消耗数量 → 以单位成本估值（数量 × 单价）。\n微观成本法（bottom-up）逐项自下而上加总，自上而下法（top-down）用总费用分摊，二者结果应交叉校验；跨年度成本须按医疗 CPI 等调整价格并按贴现率折现。\n**代码**：\n\n```stata\n* 成本均数与 bootstrap 95% CI（成本右偏时优于正态 CI）\nbootstrap r(mean), reps(1000) seed(2026): summarize cost\nci means cost\n```\n\n```r\nset.seed(2026)\nboot <- replicate(1000, mean(sample(cost, replace = TRUE)))\nmean(cost)                       # 点估计\nquantile(boot, c(.025, .975))    # 95% 百分位置信区间\n```\n\n**报告要点**：报告测算视角、时间范围、数据与单价来源（年份）、贴现率与价格调整方式；均数","url":"methods.html","source":"data/methods.json","details":{"id":"C1","name":"成本测算","category":"C","content":"**适用**：测算某一疾病、干预或服务在特定视角与时间段内消耗的资源总量（货币化成本），为成本分析、CEA/BIA 提供成本输入。\n**要点**：先明确测算视角（社会 / 卫生体系 / 医保 / 患者）与时间范围，视角决定纳入范围。\n成本分直接成本（直接医疗 + 直接非医疗）与间接成本（生产力损失，常用人力资本法按工资估算）。\n按「识别—测量—估值」三步：识别资源项目 → 测量消耗数量 → 以单位成本估值（数量 × 单价）。\n微观成本法（bottom-up）逐项自下而上加总，自上而下法（top-down）用总费用分摊，二者结果应交叉校验；跨年度成本须按医疗 CPI 等调整价格并按贴现率折现。\n**代码**：\n\n```stata\n* 成本均数与 bootstrap 95% CI（成本右偏时优于正态 CI）\nbootstrap r(mean), reps(1000) seed(2026): summarize cost\nci means cost\n```\n\n```r\nset.seed(2026)\nboot <- replicate(1000, mean(sample(cost, replace = TRUE)))\nmean(cost)                       # 点估计\nquantile(boot, c(.025, .975))    # 95% 百分位置信区间\n```\n\n**报告要点**：报告测算视角、时间范围、数据与单价来源（年份）、贴现率与价格调整方式；均数附 95% CI。\n**常见错误**：混淆视角（把患者自负费用计入医保视角成本），或把不同年份未经价格调整的成本直接相加。\n\n---","source":"skills/references/methods-54.md"}},{"id":"methods:C2","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"成本-效果分析 CEA 与成本-效用分析 CUA","summary":"**适用**：比较两种及以上干预的成本与健康产出，产出为自然单位（CEA）或 QALY（CUA）。\n**要点**：核心指标 ICER = ΔC/ΔE = (C₁−C₀)/(E₁−E₀)，与支付意愿阈值（国内常用 1–3 倍人均 GDP/QALY，口径须注明）比较判断经济性。\n不确定性用 bootstrap 或概率敏感性分析（PSA）刻画，绘制成本-效果平面与成本-效果可接受曲线（CEAC）；净货币效益 NMB = λ·ΔE − ΔC > 0 等价于在阈值 λ 下可接受。\n增量必须基于同一人群、同一时间范围计算，不能简单用两组均值相除代替统计推断。\n**代码**：\n\n```stata\n* ICER 增量计算（arm：1 = 干预，0 = 对照；ttest 按组值升序，组 2 为 arm=1）\nttest c, by(arm)\nscalar dC = r(mu_2) - r(mu_1)\nttest e, by(arm)\nscalar dE = r(mu_2) - r(mu_1)\ndi \"ICER = \" dC / dE\n```\n\n```r\ndC <- with(dat, mean(c[arm == 1]) - mean(c[arm == 0]))\ndE <- with(dat, mean(e[arm == 1]) - mean(e[arm == 0]))\nset.seed(2026)\nicers <- replicate(1000, with(dat[sample(nrow(dat)","url":"methods.html","source":"data/methods.json","details":{"id":"C2","name":"成本-效果分析 CEA 与成本-效用分析 CUA","category":"C","content":"**适用**：比较两种及以上干预的成本与健康产出，产出为自然单位（CEA）或 QALY（CUA）。\n**要点**：核心指标 ICER = ΔC/ΔE = (C₁−C₀)/(E₁−E₀)，与支付意愿阈值（国内常用 1–3 倍人均 GDP/QALY，口径须注明）比较判断经济性。\n不确定性用 bootstrap 或概率敏感性分析（PSA）刻画，绘制成本-效果平面与成本-效果可接受曲线（CEAC）；净货币效益 NMB = λ·ΔE − ΔC > 0 等价于在阈值 λ 下可接受。\n增量必须基于同一人群、同一时间范围计算，不能简单用两组均值相除代替统计推断。\n**代码**：\n\n```stata\n* ICER 增量计算（arm：1 = 干预，0 = 对照；ttest 按组值升序，组 2 为 arm=1）\nttest c, by(arm)\nscalar dC = r(mu_2) - r(mu_1)\nttest e, by(arm)\nscalar dE = r(mu_2) - r(mu_1)\ndi \"ICER = \" dC / dE\n```\n\n```r\ndC <- with(dat, mean(c[arm == 1]) - mean(c[arm == 0]))\ndE <- with(dat, mean(e[arm == 1]) - mean(e[arm == 0]))\nset.seed(2026)\nicers <- replicate(1000, with(dat[sample(nrow(dat), replace = TRUE), ],\n              (mean(c[arm==1]) - mean(c[arm==0])) / (mean(e[arm==1]) - mean(e[arm==0]))))\nquantile(icers, c(.025, .975))   # bootstrap ICER 置信区间\n```\n\n**报告要点**：并列报告两组成本与效果均数（含 95% CI）、ICER 及 bootstrap/PSA 结果（CEAC 上阈值处的接受概率），并注明阈值来源。\n**常见错误**：增量效果趋近 0 时 ICER 数值爆炸仍直接解读，或不做不确定性分析即断言结论稳健。\n\n---","source":"skills/references/methods-54.md"}},{"id":"methods:C3","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"QALY 与 DALY 计算","summary":"**适用**：把生存时间与生命质量合成为单一健康产出（QALY），或按死亡与伤残损失合成疾病负担（DALY），用于 CUA 与负担比较。\n**要点**：QALY = Σ 生存年 × 效用权重（0–1），效用来自 EQ-5D-5L（使用中国效用值集）、SF-6D 或 TTO/SG，纵向数据用曲线下面积法按时间区间加总。\nDALY = YLL + YLD：YLL = 死亡数 × 标准期望寿命，YLD = 患病人数 × 伤残权重 × 病程（权重查 GBD 表）。\nDALY 常用 GBD 参数（3% 贴现、年龄权重）；QALY 与 DALY 互为近似镜像但不能直接互换。\n**代码**：\n\n```stata\n* QALY：效用曲线下面积（q 为效用、t 为时点，按 id 与 t 排序）\nbysort id (t): gen qaly_step = (q + q[_n-1]) / 2 * (t - t[_n-1]) if _n > 1\nbysort id: egen qaly = total(qaly_step)\n* DALY：YLL + YLD（yll、yld 按 GBD 公式预先计算）\ngen daly = yll + yld\n```\n\n```r\ndat <- dat[order(dat$id, dat$t), ]\nqaly <- tapply(seq_len(nrow(dat)), dat$id, function(i) {\n  t <- dat$t[i]; q <- dat$q[i]","url":"methods.html","source":"data/methods.json","details":{"id":"C3","name":"QALY 与 DALY 计算","category":"C","content":"**适用**：把生存时间与生命质量合成为单一健康产出（QALY），或按死亡与伤残损失合成疾病负担（DALY），用于 CUA 与负担比较。\n**要点**：QALY = Σ 生存年 × 效用权重（0–1），效用来自 EQ-5D-5L（使用中国效用值集）、SF-6D 或 TTO/SG，纵向数据用曲线下面积法按时间区间加总。\nDALY = YLL + YLD：YLL = 死亡数 × 标准期望寿命，YLD = 患病人数 × 伤残权重 × 病程（权重查 GBD 表）。\nDALY 常用 GBD 参数（3% 贴现、年龄权重）；QALY 与 DALY 互为近似镜像但不能直接互换。\n**代码**：\n\n```stata\n* QALY：效用曲线下面积（q 为效用、t 为时点，按 id 与 t 排序）\nbysort id (t): gen qaly_step = (q + q[_n-1]) / 2 * (t - t[_n-1]) if _n > 1\nbysort id: egen qaly = total(qaly_step)\n* DALY：YLL + YLD（yll、yld 按 GBD 公式预先计算）\ngen daly = yll + yld\n```\n\n```r\ndat <- dat[order(dat$id, dat$t), ]\nqaly <- tapply(seq_len(nrow(dat)), dat$id, function(i) {\n  t <- dat$t[i]; q <- dat$q[i]\n  sum(diff(t) * (head(q, -1) + tail(q, -1)) / 2)     # 梯形面积法\n})\n```\n\n**报告要点**：报告效用工具、值集来源与测量时点，说明 QALY 加总方法（AUC/梯形法）与 DALY 的伤残权重、贴现参数。\n**常见错误**：把疾病严重度评分当效用权重，或直接混用 QALY 与 DALY 做比较。\n\n---","source":"skills/references/methods-54.md"}},{"id":"methods:C4","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"预算影响分析 BIA","summary":"**适用**：从预算持有人（医保基金、医院或政府）视角，估计采纳新干预后 3–5 年内逐年及累计的财务影响。\n**要点**：预算影响 =（新干预下目标人群总费用）−（现行干预下总费用），按「目标人群规模 × 渗透/替代率 × 人均年费用」逐年滚动测算。\n需设定市场吸纳曲线（uptake）、替代与增量使用比例、人群自然增长与价格变化；ISPOR 建议按年度现金流报告、不做贴现，且与 CEA 分开呈现。\n**代码**：\n\n```stata\n* 逐年预算影响 = 目标人群 × 渗透率 ×（新方案 − 现行方案）人均年费用\ngen bi = pop * uptake * (c_new - c_cur)\nlist year bi\nsum bi                         // 研究期内合计\n```\n\n```r\nbia <- data.frame(year = 1:5, pop = 1e5, uptake = c(.2, .4, .6, .8, .9))\nbia$bi <- bia$pop * bia$uptake * (c_new - c_cur)\nbia                            # 逐年预算影响；sum(bia$bi) 为合计\n```\n\n**报告要点**：报告视角、时间范围、目标人群与 uptake/替代假设，并给出单因素与情景（乐观/悲观）敏感性分析。\n**常见错误**：把预算影响结果当作成本-效果结论，或漏报 uptake 与人群增长","url":"methods.html","source":"data/methods.json","details":{"id":"C4","name":"预算影响分析 BIA","category":"C","content":"**适用**：从预算持有人（医保基金、医院或政府）视角，估计采纳新干预后 3–5 年内逐年及累计的财务影响。\n**要点**：预算影响 =（新干预下目标人群总费用）−（现行干预下总费用），按「目标人群规模 × 渗透/替代率 × 人均年费用」逐年滚动测算。\n需设定市场吸纳曲线（uptake）、替代与增量使用比例、人群自然增长与价格变化；ISPOR 建议按年度现金流报告、不做贴现，且与 CEA 分开呈现。\n**代码**：\n\n```stata\n* 逐年预算影响 = 目标人群 × 渗透率 ×（新方案 − 现行方案）人均年费用\ngen bi = pop * uptake * (c_new - c_cur)\nlist year bi\nsum bi                         // 研究期内合计\n```\n\n```r\nbia <- data.frame(year = 1:5, pop = 1e5, uptake = c(.2, .4, .6, .8, .9))\nbia$bi <- bia$pop * bia$uptake * (c_new - c_cur)\nbia                            # 逐年预算影响；sum(bia$bi) 为合计\n```\n\n**报告要点**：报告视角、时间范围、目标人群与 uptake/替代假设，并给出单因素与情景（乐观/悲观）敏感性分析。\n**常见错误**：把预算影响结果当作成本-效果结论，或漏报 uptake 与人群增长假设。\n\n---","source":"skills/references/methods-54.md"}},{"id":"methods:C5","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"卫生技术评估 HTA 流程","summary":"**适用**：为新技术（药品、器械、诊断、服务包）的准入、定价与报销决策整合临床与经济证据的规范化评估。\n**要点**：标准流程：明确评估问题（PICO 与决策问题）→ 系统检索与证据评价 → 临床效果（必要时间接比较/网状 Meta）→ 经济评价（CEA/CUA + BIA）→ 伦理、组织、公平等非经济维度 → 综合报告供委员会决策。\n核心产出是相对阈值的经济性判断与预算影响估计；国内对应医保谈判/准入的申报与专家评审流程。\n**代码**：\n\n```stata\n* PSA 决策判断：净货币效益 NMB = λ·ΔE − ΔC，λ 为阈值（如 1–3 倍人均 GDP）\ngen nmb = lambda * (e_new - e_cur) - (c_new - c_cur)\ncount if nmb > 0\ndi \"成本-效果概率 = \" r(N) / _N\n```\n\n```r\nnmb <- lambda * (e_new - e_cur) - (c_new - c_cur)\nmean(nmb > 0)                  # NMB > 0 的比例 → CEAC 上阈值 λ 处的接受概率\n```\n\n**报告要点**：写明评估视角、关键参数来源与假设、模型结构及不确定性，并独立核验申办方提交的参数与模型。\n**常见错误**：直接采信申办方模型结论而不做独立校验，或把 HTA 简化为只看 ICER 的经济评价。\n\n---","url":"methods.html","source":"data/methods.json","details":{"id":"C5","name":"卫生技术评估 HTA 流程","category":"C","content":"**适用**：为新技术（药品、器械、诊断、服务包）的准入、定价与报销决策整合临床与经济证据的规范化评估。\n**要点**：标准流程：明确评估问题（PICO 与决策问题）→ 系统检索与证据评价 → 临床效果（必要时间接比较/网状 Meta）→ 经济评价（CEA/CUA + BIA）→ 伦理、组织、公平等非经济维度 → 综合报告供委员会决策。\n核心产出是相对阈值的经济性判断与预算影响估计；国内对应医保谈判/准入的申报与专家评审流程。\n**代码**：\n\n```stata\n* PSA 决策判断：净货币效益 NMB = λ·ΔE − ΔC，λ 为阈值（如 1–3 倍人均 GDP）\ngen nmb = lambda * (e_new - e_cur) - (c_new - c_cur)\ncount if nmb > 0\ndi \"成本-效果概率 = \" r(N) / _N\n```\n\n```r\nnmb <- lambda * (e_new - e_cur) - (c_new - c_cur)\nmean(nmb > 0)                  # NMB > 0 的比例 → CEAC 上阈值 λ 处的接受概率\n```\n\n**报告要点**：写明评估视角、关键参数来源与假设、模型结构及不确定性，并独立核验申办方提交的参数与模型。\n**常见错误**：直接采信申办方模型结论而不做独立校验，或把 HTA 简化为只看 ICER 的经济评价。\n\n---","source":"skills/references/methods-54.md"}},{"id":"methods:C6","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"随机前沿分析 SFA 与数据包络分析 DEA（效率评价）","summary":"**适用**：评价医院、科室、疾控机构等决策单元（DMU）的技术效率及其影响因素。\n**要点**：DEA 为非参数确定性前沿：CCR（规模报酬不变）与 BCC（可变），允许多投入多产出、无需函数形式，但对离群值敏感且不区分随机误差与无效率。\nSFA 为参数随机前沿：把误差分解为统计噪声 v 与无效率 u（半正态/截断正态等分布假设），可检验影响因素，但一般仅处理单一产出。\n两者常组合使用：DEA 得效率得分，再以 Tobit/截断回归或 SFA 分析效率影响因素。\n**代码**：\n\n```stata\n* SFA：Cobb-Douglas 随机前沿，半正态无效率\nsfcross y x1 x2, distribution(hnormal)\npredict te_sfa, te\n* DEA：投入导向 BCC（先 ssc install dea）\ndea x1 x2 = y, rts(vrs) ort(in)   // 效率得分；选项以 help dea 为准\n```\n\n```r\nlibrary(Benchmarking)\nr_dea <- dea(X = cbind(x1, x2), Y = y, RTS = \"vrs\")    # 投入导向 BCC\neff_dea <- r_dea$eff                                    # Farrell 效率 E ∈ (0,1]\nlibrary(frontier)\neff_sfa <- efficienci","url":"methods.html","source":"data/methods.json","details":{"id":"C6","name":"随机前沿分析 SFA 与数据包络分析 DEA（效率评价）","category":"C","content":"**适用**：评价医院、科室、疾控机构等决策单元（DMU）的技术效率及其影响因素。\n**要点**：DEA 为非参数确定性前沿：CCR（规模报酬不变）与 BCC（可变），允许多投入多产出、无需函数形式，但对离群值敏感且不区分随机误差与无效率。\nSFA 为参数随机前沿：把误差分解为统计噪声 v 与无效率 u（半正态/截断正态等分布假设），可检验影响因素，但一般仅处理单一产出。\n两者常组合使用：DEA 得效率得分，再以 Tobit/截断回归或 SFA 分析效率影响因素。\n**代码**：\n\n```stata\n* SFA：Cobb-Douglas 随机前沿，半正态无效率\nsfcross y x1 x2, distribution(hnormal)\npredict te_sfa, te\n* DEA：投入导向 BCC（先 ssc install dea）\ndea x1 x2 = y, rts(vrs) ort(in)   // 效率得分；选项以 help dea 为准\n```\n\n```r\nlibrary(Benchmarking)\nr_dea <- dea(X = cbind(x1, x2), Y = y, RTS = \"vrs\")    # 投入导向 BCC\neff_dea <- r_dea$eff                                    # Farrell 效率 E ∈ (0,1]\nlibrary(frontier)\neff_sfa <- efficiencies(sfa(log(y) ~ log(x1) + log(x2), data = dat))\n```\n\n**报告要点**：报告前沿模型设定（方向、规模报酬、分布假设）、效率得分分布（均值/四分位）与影响因素回归结果。\n**常见错误**：样本量相对投入产出维度过小导致多数 DMU 效率 = 1 仍做解释，或 SFA 无效率分布假设未检验。\n\n---","source":"skills/references/methods-54.md"}},{"id":"methods:C7","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"医保支付标准测算（DRG/DIP 权重与点值测算思路）","summary":"**适用**：为 DRG/DIP 付费改革测算病组权重、费率与病种分值、点值，作为医保基金按病种结算的支付标准。\n**要点**：DRG：权重 RW = 该病组例均费用 ÷ 全部病例例均费用（基于历史住院费用数据，再经临床论证与成本校正）；费率 = 住院基金预算总额 ÷ Σ(RW × 组病例数)，支付标准 = RW × 费率 × 调整系数。\nDIP：病种分值 = 该病种例均费用 ÷ 基准例均费用（或相对点数），点值 = 区域基金总额 ÷ Σ(病种分值 × 病例数)，总量控制下按年度清算校正。\n权重/分值应剔除不合理费用结构影响（药品耗材占比、人次人头比）并建立动态调整机制。\n**代码**：\n\n```stata\n* DRG 权重：病组例均费用 ÷ 全样本例均费用\nbysort drg: egen fee_drg = mean(fee)\negen fee_all = mean(fee)\ngen rw = fee_drg / fee_all\n* DIP 点值：区域基金总额 ÷ Σ(病种分值 × 病例数)\ngen point_amt = dip_score * n_cases\nsum point_amt\ndi \"每分值金额 = \" fund_budget / r(sum)\n```\n\n```r\nlibrary(dplyr)\nrw <- dat |> group_by(drg) |> summarise(rw = mean(fee) / mean(dat$fee))\ndip_value <- ","url":"methods.html","source":"data/methods.json","details":{"id":"C7","name":"医保支付标准测算（DRG/DIP 权重与点值测算思路）","category":"C","content":"**适用**：为 DRG/DIP 付费改革测算病组权重、费率与病种分值、点值，作为医保基金按病种结算的支付标准。\n**要点**：DRG：权重 RW = 该病组例均费用 ÷ 全部病例例均费用（基于历史住院费用数据，再经临床论证与成本校正）；费率 = 住院基金预算总额 ÷ Σ(RW × 组病例数)，支付标准 = RW × 费率 × 调整系数。\nDIP：病种分值 = 该病种例均费用 ÷ 基准例均费用（或相对点数），点值 = 区域基金总额 ÷ Σ(病种分值 × 病例数)，总量控制下按年度清算校正。\n权重/分值应剔除不合理费用结构影响（药品耗材占比、人次人头比）并建立动态调整机制。\n**代码**：\n\n```stata\n* DRG 权重：病组例均费用 ÷ 全样本例均费用\nbysort drg: egen fee_drg = mean(fee)\negen fee_all = mean(fee)\ngen rw = fee_drg / fee_all\n* DIP 点值：区域基金总额 ÷ Σ(病种分值 × 病例数)\ngen point_amt = dip_score * n_cases\nsum point_amt\ndi \"每分值金额 = \" fund_budget / r(sum)\n```\n\n```r\nlibrary(dplyr)\nrw <- dat |> group_by(drg) |> summarise(rw = mean(fee) / mean(dat$fee))\ndip_value <- fund_budget / sum(dat$dip_score * dat$n_cases)   # DIP 点值\n```\n\n**报告要点**：报告测算数据年份与费用口径、权重/分值校正方法（专家论证、费用结构剔除）及点值年度清算与校正规则。\n**常见错误**：直接使用未校正的历史例均费用当权重/分值，或忽略总额控制下点值年终清算的浮动风险。\n\n---","source":"skills/references/methods-54.md"}},{"id":"methods:C8","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"卫生服务利用公平性（集中指数 CI 与不平等分解）","summary":"**适用**：量化卫生服务利用（或健康结局、费用）随经济状况分布的不平等程度及其来源。\n**要点**：集中指数 CI = 2·cov(y, R)/ȳ，R 为按经济状况（收入/财富）排序的分数秩（(rank−0.5)/n），CI ∈ [−1, 1]，负值表示利用偏向较穷人群，0 为绝对平等；标准误用回归法或专门命令计算，跨人群比较时建议先做年龄性别标化。\n不平等分解：CI = Σ(βk·x̄k/ȳ)·CIk + 残差，即每个影响因素按「弹性 × 该变量的集中指数」贡献，用于定位不平等的主要驱动因素。\n**代码**：\n\n```stata\nssc install conindex, replace\nconindex y, rankvar(r)      // r：按经济状况排序的分数秩；选项以 help conindex 为准\n```\n\n```r\nr  <- (rank(ses) - 0.5) / length(ses)   # 按经济状况排序的分数秩\nci <- 2 * cov(y, r) / mean(y)           # 协方差法集中指数\n```\n\n**报告要点**：报告 CI 及其标准误/置信区间（是否显著偏离 0），分解表给出各因素的弹性、集中指数与贡献占比。\n**常见错误**：分数秩按 y 自身而非经济状况排序，或不做标化直接跨人群比较 CI。\n\n---","url":"methods.html","source":"data/methods.json","details":{"id":"C8","name":"卫生服务利用公平性（集中指数 CI 与不平等分解）","category":"C","content":"**适用**：量化卫生服务利用（或健康结局、费用）随经济状况分布的不平等程度及其来源。\n**要点**：集中指数 CI = 2·cov(y, R)/ȳ，R 为按经济状况（收入/财富）排序的分数秩（(rank−0.5)/n），CI ∈ [−1, 1]，负值表示利用偏向较穷人群，0 为绝对平等；标准误用回归法或专门命令计算，跨人群比较时建议先做年龄性别标化。\n不平等分解：CI = Σ(βk·x̄k/ȳ)·CIk + 残差，即每个影响因素按「弹性 × 该变量的集中指数」贡献，用于定位不平等的主要驱动因素。\n**代码**：\n\n```stata\nssc install conindex, replace\nconindex y, rankvar(r)      // r：按经济状况排序的分数秩；选项以 help conindex 为准\n```\n\n```r\nr  <- (rank(ses) - 0.5) / length(ses)   # 按经济状况排序的分数秩\nci <- 2 * cov(y, r) / mean(y)           # 协方差法集中指数\n```\n\n**报告要点**：报告 CI 及其标准误/置信区间（是否显著偏离 0），分解表给出各因素的弹性、集中指数与贡献占比。\n**常见错误**：分数秩按 y 自身而非经济状况排序，或不做标化直接跨人群比较 CI。\n\n---","source":"skills/references/methods-54.md"}},{"id":"methods:D1","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"倾向得分匹配（PSM）","summary":"**适用**：观察性数据中处理（治疗/政策暴露）分配非随机、但可观测混杂充分时，用倾向得分匹配估计平均处理效应（ATT/ATE）。\n**要点**：核心思想是用协变量 X 估计倾向得分 p(X) = P(T=1|X)，将处理组与对照组按得分匹配，使两组在可观测协变量上可比，近似「准随机化」。\n匹配前须检查**共同支撑**（common support）：两组 p(X) 分布应充分重叠，可画密度重叠图或用 `teffects overlap`。\n匹配后须检验**平衡性**：各协变量标准化均值差 SMD，|SMD| < 0.1 视为平衡，否则调整模型设定或更换匹配算法。\nPSM 只控制可观测混杂，不能解决未观测混杂。\n**代码**：\n\n```stata\n* Stata：1:1 近邻匹配估计 ATT（y 结局、t 处理、x1 x2 协变量）\nteffects psmatch (y) (t x1 x2), atet\ntebalance summarize          // 平衡性：SMD 与方差比\ntebalance density            // 共同支撑：倾向得分重叠图\n```\n\n```r\nlibrary(MatchIt)\nm <- matchit(t ~ x1 + x2, data = dat, method = \"nearest\")\nmd <- match.data(m)\nlm(y ~ t + x1 + x2, data = md, weights = weigh","url":"methods.html","source":"data/methods.json","details":{"id":"D1","name":"倾向得分匹配（PSM）","category":"D","content":"**适用**：观察性数据中处理（治疗/政策暴露）分配非随机、但可观测混杂充分时，用倾向得分匹配估计平均处理效应（ATT/ATE）。\n**要点**：核心思想是用协变量 X 估计倾向得分 p(X) = P(T=1|X)，将处理组与对照组按得分匹配，使两组在可观测协变量上可比，近似「准随机化」。\n匹配前须检查**共同支撑**（common support）：两组 p(X) 分布应充分重叠，可画密度重叠图或用 `teffects overlap`。\n匹配后须检验**平衡性**：各协变量标准化均值差 SMD，|SMD| < 0.1 视为平衡，否则调整模型设定或更换匹配算法。\nPSM 只控制可观测混杂，不能解决未观测混杂。\n**代码**：\n\n```stata\n* Stata：1:1 近邻匹配估计 ATT（y 结局、t 处理、x1 x2 协变量）\nteffects psmatch (y) (t x1 x2), atet\ntebalance summarize          // 平衡性：SMD 与方差比\ntebalance density            // 共同支撑：倾向得分重叠图\n```\n\n```r\nlibrary(MatchIt)\nm <- matchit(t ~ x1 + x2, data = dat, method = \"nearest\")\nmd <- match.data(m)\nlm(y ~ t + x1 + x2, data = md, weights = weights)\ncobalt::love.plot(m, abs = TRUE)   # SMD 平衡图\n```\n\n**报告要点**：报告匹配算法与卡尺、匹配前后样本量与 SMD 平衡表，效应量附稳健标准误与 95% CI，并声明结果仅对可观测混杂成立。\n**常见错误**：把匹配后结果当作随机对照试验证据，或不报告共同支撑与平衡性检查。\n\n---","source":"skills/references/methods-54.md"}},{"id":"methods:D2","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"工具变量（IV）","summary":"**适用**：处理变量存在内生性（遗漏变量、反向因果、测量误差）且能找到有效工具变量 Z 时，用工具变量法估计因果效应（2SLS）。\n**要点**：有效 IV 需满足：①相关性——Z 与内生处理 T 强相关，一阶段 F 统计量 > 10（弱 IV 会放大偏误）；②排他性——Z 仅通过 T 影响 Y，不可直接检验，需靠研究设计论证；③同质性或单调性假设下，2SLS 识别的是依从者平均处理效应（LATE）。\n多工具变量时可用过度识别检验（Sargan/Hansen J）检验工具变量的有效性。\n工具变量的有效性首先取决于研究设计与背景论证，而非统计检验本身。\n2SLS 分两阶段：第一阶段 T 对 Z（及外生协变量）回归得拟合值，第二阶段 Y 对拟合值回归；标准误由 `ivregress`/`ivreg` 自动校正。\n弱 IV 时除 F 统计量外，可加做对弱工具变量稳健的推断（如 Anderson–Rubin 检验）。\nR 公式 y ~ t | z 中，`|` 右侧为工具变量，t 为内生处理。\n**代码**：\n\n```stata\n* Stata：2SLS（核心命令）\nivregress 2sls y (t = z), first vce(robust)\nestat firststage            // 一阶段 F，>10 为强工具变量\n* 多工具变量：ivregress 2sls y (t = z1 z2), first，再用 estat overid 做过度识别检验\n```","url":"methods.html","source":"data/methods.json","details":{"id":"D2","name":"工具变量（IV）","category":"D","content":"**适用**：处理变量存在内生性（遗漏变量、反向因果、测量误差）且能找到有效工具变量 Z 时，用工具变量法估计因果效应（2SLS）。\n**要点**：有效 IV 需满足：①相关性——Z 与内生处理 T 强相关，一阶段 F 统计量 > 10（弱 IV 会放大偏误）；②排他性——Z 仅通过 T 影响 Y，不可直接检验，需靠研究设计论证；③同质性或单调性假设下，2SLS 识别的是依从者平均处理效应（LATE）。\n多工具变量时可用过度识别检验（Sargan/Hansen J）检验工具变量的有效性。\n工具变量的有效性首先取决于研究设计与背景论证，而非统计检验本身。\n2SLS 分两阶段：第一阶段 T 对 Z（及外生协变量）回归得拟合值，第二阶段 Y 对拟合值回归；标准误由 `ivregress`/`ivreg` 自动校正。\n弱 IV 时除 F 统计量外，可加做对弱工具变量稳健的推断（如 Anderson–Rubin 检验）。\nR 公式 y ~ t | z 中，`|` 右侧为工具变量，t 为内生处理。\n**代码**：\n\n```stata\n* Stata：2SLS（核心命令）\nivregress 2sls y (t = z), first vce(robust)\nestat firststage            // 一阶段 F，>10 为强工具变量\n* 多工具变量：ivregress 2sls y (t = z1 z2), first，再用 estat overid 做过度识别检验\n```\n\n```r\nlibrary(AER)\nm_iv <- ivreg(y ~ t | z, data = dat)\nsummary(m_iv, diagnostics = TRUE)   # 含弱工具变量诊断\n```\n\n**报告要点**：报告一阶段 F、工具变量的相关性与排他性论证、2SLS 系数及稳健标准误，并把效应解读为对依从者成立的 LATE。\n**常见错误**：弱工具变量（一阶段 F<10）仍下强因果结论，或把排他性假设当作可检验。\n\n---","source":"skills/references/methods-54.md"}},{"id":"methods:D3","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"断点回归（RDD）","summary":"**适用**：处理由连续运行变量是否越过临界值决定（如分数线录取、年龄阈值报销）时，估计断点附近的局部因果效应。\n**要点**：RDD 依据「断点附近个体近似随机落入两侧」，比较临界值两侧的局部平均结果之差。\n须做三类检验：①运行变量密度在断点处连续（无操纵，McCrary 检验）；②前定协变量在断点处平衡；③结果对带宽选择稳健。\n主流做法为局部线性回归 + 三角核权重，带宽按 MSE 最优或 Calonico–Cattaneo–Titiunik (2014) 方法选择，并报告 RD 图。\n效应是断点处的 LATE，向远离断点的人群外推需谨慎。\n模糊断点（fuzzy RD）以「是否越线」为工具变量估计：Stata `rdrobust y x, c(0) fuzzy(t)`，R `rdrobust` 加 `fuzzy = \"t\"`。\n**代码**：\n\n```stata\nssc install rdrobust\nssc install rddensity\nrdrobust y x, c(0)          // x 为中心化运行变量\nrdplot y x, c(0)            // RD 图\nrddensity x, c(0)           // 密度操纵检验\n```\n\n```r\nlibrary(rdrobust)\nrdrobust(y = dat$y, x = dat$x, c = 0)\nrddensity::rddensity(dat$x, c = 0)  ","url":"methods.html","source":"data/methods.json","details":{"id":"D3","name":"断点回归（RDD）","category":"D","content":"**适用**：处理由连续运行变量是否越过临界值决定（如分数线录取、年龄阈值报销）时，估计断点附近的局部因果效应。\n**要点**：RDD 依据「断点附近个体近似随机落入两侧」，比较临界值两侧的局部平均结果之差。\n须做三类检验：①运行变量密度在断点处连续（无操纵，McCrary 检验）；②前定协变量在断点处平衡；③结果对带宽选择稳健。\n主流做法为局部线性回归 + 三角核权重，带宽按 MSE 最优或 Calonico–Cattaneo–Titiunik (2014) 方法选择，并报告 RD 图。\n效应是断点处的 LATE，向远离断点的人群外推需谨慎。\n模糊断点（fuzzy RD）以「是否越线」为工具变量估计：Stata `rdrobust y x, c(0) fuzzy(t)`，R `rdrobust` 加 `fuzzy = \"t\"`。\n**代码**：\n\n```stata\nssc install rdrobust\nssc install rddensity\nrdrobust y x, c(0)          // x 为中心化运行变量\nrdplot y x, c(0)            // RD 图\nrddensity x, c(0)           // 密度操纵检验\n```\n\n```r\nlibrary(rdrobust)\nrdrobust(y = dat$y, x = dat$x, c = 0)\nrddensity::rddensity(dat$x, c = 0)   # 操纵检验\n```\n\n**报告要点**：报告带宽选择方法与不同带宽下的稳健性、RD 图（断点两侧拟合线 + 置信区间）、操纵检验与协变量连续性检验。\n**常见错误**：不做密度与协变量连续性检验、不报带宽敏感性就下因果结论。\n\n---","source":"skills/references/methods-54.md"}},{"id":"methods:D4","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"合成控制法（SCM）","summary":"**适用**：政策只影响一个（或少数）处理单元、且有较长政策前后面板与多个未处理对照单元时，估计政策对处理单元的效应。\n**要点**：用对照组单元的加权组合构造「合成对照」，权重在政策前时期最小化处理单元与合成对照在结局及预测协变量上的差异；政策后两者的差距即政策效应轨迹。\n推断依靠**安慰剂检验**：把每个对照单元依次当作「假处理」单元重复估计，看真实效应是否显著大于假效应分布（可换算置换 p 值）。\n要求政策前时期足够长、合成拟合良好（政策前 RMSPE 小）。\n权重通常非负且和为 1；Stata `synth` 的 `nested` 选项与 R 侧嵌套求解参数（如 `optimxmethod`）控制权重求解（以帮助文档为准）。\n**代码**：\n\n```stata\nssc install synth\ntsset id time\nsynth y x1 x2 y(1990(1)1999), trunit(1) trperiod(2000) fig\n* 安慰剂检验：对每个对照单元重复 synth 比较效应轨迹（以 help synth 为准）\n```\n\n```r\ndp <- Synth::dataprep(foo = dat, predictors = c(\"x1\", \"x2\"), dependent = \"y\",\n                      unit.variable = \"id\", time.variable = \"time\",\n                ","url":"methods.html","source":"data/methods.json","details":{"id":"D4","name":"合成控制法（SCM）","category":"D","content":"**适用**：政策只影响一个（或少数）处理单元、且有较长政策前后面板与多个未处理对照单元时，估计政策对处理单元的效应。\n**要点**：用对照组单元的加权组合构造「合成对照」，权重在政策前时期最小化处理单元与合成对照在结局及预测协变量上的差异；政策后两者的差距即政策效应轨迹。\n推断依靠**安慰剂检验**：把每个对照单元依次当作「假处理」单元重复估计，看真实效应是否显著大于假效应分布（可换算置换 p 值）。\n要求政策前时期足够长、合成拟合良好（政策前 RMSPE 小）。\n权重通常非负且和为 1；Stata `synth` 的 `nested` 选项与 R 侧嵌套求解参数（如 `optimxmethod`）控制权重求解（以帮助文档为准）。\n**代码**：\n\n```stata\nssc install synth\ntsset id time\nsynth y x1 x2 y(1990(1)1999), trunit(1) trperiod(2000) fig\n* 安慰剂检验：对每个对照单元重复 synth 比较效应轨迹（以 help synth 为准）\n```\n\n```r\ndp <- Synth::dataprep(foo = dat, predictors = c(\"x1\", \"x2\"), dependent = \"y\",\n                      unit.variable = \"id\", time.variable = \"time\",\n                      treatment.identifier = 1, controls.identifier = 2:20,\n                      time.predictors.prior = 1990:1999, time.optimize.ssr = 1990:1999)\ns1 <- Synth::synth(dp)\n```\n\n**报告要点**：报告合成对照的权重构成与政策前拟合优度（RMSPE）、政策后效应轨迹图，以及安慰剂/置换检验结果。\n**常见错误**：政策前拟合差（RMSPE 大）仍解读效应，或不做安慰剂检验。\n\n---","source":"skills/references/methods-54.md"}},{"id":"methods:D5","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"事件研究法","summary":"**适用**：政策冲击时点明确、需考察效应随时间的动态变化（事前趋势与事后轨迹）时使用，适用于面板或重复截面数据。\n**要点**：以相对事件时间（relative time）生成各期虚拟变量并与处理组交互，取政策前一期（-1）为参照。\n**事前系数应不显著**（平行趋势检验），事后系数轨迹刻画效应的动态演化；事件研究图（系数 + 置信区间）是标准呈现。\n基准设定为双向固定效应 + 聚类稳健标准误；交错采用（异质性处理时点）时双向固定效应估计可能有偏，应加做 Sun–Abraham (2021) 或 Callaway–Sant'Anna (2021) 稳健性。\n各期系数均相对 -1 期解读（-1 期系数被吸收进常数项）。\n正式平行趋势检验可对事前交互项做联合检验（`reghdfe` 后用 `test` 联合检验事前系数）。\n若为重复截面而非面板，个体固定效应不可用，改用群组固定效应并按群组聚类。\n**代码**：\n\n```stata\nssc install reghdfe\nssc install coefplot\nreghdfe y ib(-1).time#i.treat, absorb(id time) vce(cluster id)\ncoefplot, keep(*.time#1.treat)          // 事件研究图\n```\n\n```r\nlibrary(fixest)\nm <- feols(y ~ i(time, treat, ref = -1) | id + tim","url":"methods.html","source":"data/methods.json","details":{"id":"D5","name":"事件研究法","category":"D","content":"**适用**：政策冲击时点明确、需考察效应随时间的动态变化（事前趋势与事后轨迹）时使用，适用于面板或重复截面数据。\n**要点**：以相对事件时间（relative time）生成各期虚拟变量并与处理组交互，取政策前一期（-1）为参照。\n**事前系数应不显著**（平行趋势检验），事后系数轨迹刻画效应的动态演化；事件研究图（系数 + 置信区间）是标准呈现。\n基准设定为双向固定效应 + 聚类稳健标准误；交错采用（异质性处理时点）时双向固定效应估计可能有偏，应加做 Sun–Abraham (2021) 或 Callaway–Sant'Anna (2021) 稳健性。\n各期系数均相对 -1 期解读（-1 期系数被吸收进常数项）。\n正式平行趋势检验可对事前交互项做联合检验（`reghdfe` 后用 `test` 联合检验事前系数）。\n若为重复截面而非面板，个体固定效应不可用，改用群组固定效应并按群组聚类。\n**代码**：\n\n```stata\nssc install reghdfe\nssc install coefplot\nreghdfe y ib(-1).time#i.treat, absorb(id time) vce(cluster id)\ncoefplot, keep(*.time#1.treat)          // 事件研究图\n```\n\n```r\nlibrary(fixest)\nm <- feols(y ~ i(time, treat, ref = -1) | id + time, data = dat)\ncoefplot(m)                              // 事件研究图\n```\n\n**报告要点**：报告事件研究图（事前系数应为零、事后轨迹与显著性）、参照期与聚类层级；交错采用时说明稳健性检验。\n**常见错误**：只看平均效应不看动态轨迹，或事前趋势不平行仍解读为因果。\n\n---","source":"skills/references/methods-54.md"}},{"id":"methods:D6","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"双重差分（DID）","summary":"**适用**：政策在某时点只冲击处理组、且有政策前后数据时，比较两组变化之差估计处理效应。\n**要点**：识别假设为**平行趋势**（无政策时两组结局变化相同）；交互项 treat × post 的系数即 DID 估计量；交错采用时注意异质性处理效应偏误（见 D5 稳健性建议）。\n**代码**：\n```stata\n* Stata：2×2 DID（交互项系数即效应）\nreg y i.treat##i.post, vce(cluster id)\n```\n```r","url":"methods.html","source":"data/methods.json","details":{"id":"D6","name":"双重差分（DID）","category":"D","content":"**适用**：政策在某时点只冲击处理组、且有政策前后数据时，比较两组变化之差估计处理效应。\n**要点**：识别假设为**平行趋势**（无政策时两组结局变化相同）；交互项 treat × post 的系数即 DID 估计量；交错采用时注意异质性处理效应偏误（见 D5 稳健性建议）。\n**代码**：\n```stata\n* Stata：2×2 DID（交互项系数即效应）\nreg y i.treat##i.post, vce(cluster id)\n```\n```r","source":"skills/references/methods-54.md"}},{"id":"methods:D7","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"间断时间序列（ITS）","summary":"**适用**：单一单元（或合并总体）有政策前后多期连续时间序列时，检验政策的水平与斜率改变。\n**要点**：分段回归 Y = β0 + β1·time + β2·post + β3·time_after；β2 为水平（即时）改变，β3 为斜率（长期）改变；须用 Newey–West 或 Prais–Winsten 校正自相关（季节性需处理）。\n**代码**：\n```stata\ntsset time\ngen post = (time >= t0)          // t0 = 政策实施期\ngen time_after = (time - t0) * post\nnewey y time post time_after, lag(1)\n```\n```r\nm <- lm(y ~ time + post + time_after, data = dat)\nlmtest::coeftest(m, vcov = sandwich::NeweyWest(m, lag = 1))\n```\n**报告要点**：报告 β2、β3 与自相关校正方法，并画拟合线与无政策反事实对照。\n**常见错误**：忽略自相关导致标准误低估，或政策前观测点太少无法识别趋势。\n> 详见 ITS skill：完整流程、自相关诊断与季节性处理。\n\n---","url":"methods.html","source":"data/methods.json","details":{"id":"D7","name":"间断时间序列（ITS）","category":"D","content":"**适用**：单一单元（或合并总体）有政策前后多期连续时间序列时，检验政策的水平与斜率改变。\n**要点**：分段回归 Y = β0 + β1·time + β2·post + β3·time_after；β2 为水平（即时）改变，β3 为斜率（长期）改变；须用 Newey–West 或 Prais–Winsten 校正自相关（季节性需处理）。\n**代码**：\n```stata\ntsset time\ngen post = (time >= t0)          // t0 = 政策实施期\ngen time_after = (time - t0) * post\nnewey y time post time_after, lag(1)\n```\n```r\nm <- lm(y ~ time + post + time_after, data = dat)\nlmtest::coeftest(m, vcov = sandwich::NeweyWest(m, lag = 1))\n```\n**报告要点**：报告 β2、β3 与自相关校正方法，并画拟合线与无政策反事实对照。\n**常见错误**：忽略自相关导致标准误低估，或政策前观测点太少无法识别趋势。\n> 详见 ITS skill：完整流程、自相关诊断与季节性处理。\n\n---","source":"skills/references/methods-54.md"}},{"id":"methods:D8","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"三重差分（DDD）","summary":"**适用**：DID 中处理组与对照组存在不同时间趋势或额外混淆时，加入第三个维度（地区/人群/结果）作双重对照。\n**要点**：DDD = 处理 × 时间 × 第三维度的三重交互项系数，通过「差异中的差异的差异」消除两组各自时间趋势与第三维度的共同冲击。\n应同时报告各双重交互项，并说明第三维度剔除何种混淆。\n**代码**：\n```stata\n* Stata：DDD（三重交互项系数即效应）\nreg y i.treat##i.post##i.group, vce(cluster id)\n```\n```r","url":"methods.html","source":"data/methods.json","details":{"id":"D8","name":"三重差分（DDD）","category":"D","content":"**适用**：DID 中处理组与对照组存在不同时间趋势或额外混淆时，加入第三个维度（地区/人群/结果）作双重对照。\n**要点**：DDD = 处理 × 时间 × 第三维度的三重交互项系数，通过「差异中的差异的差异」消除两组各自时间趋势与第三维度的共同冲击。\n应同时报告各双重交互项，并说明第三维度剔除何种混淆。\n**代码**：\n```stata\n* Stata：DDD（三重交互项系数即效应）\nreg y i.treat##i.post##i.group, vce(cluster id)\n```\n```r","source":"skills/references/methods-54.md"}},{"id":"methods:E1","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"缺失数据与多重插补","summary":"**适用**：数据存在缺失值但需完整数据集进行回归/模型分析时，用多重插补替代删除法以减少偏倚与效能损失（如问卷调查、纵向随访、电子病历数据）。\n\n**要点**：缺失机制决定处理策略——完全随机缺失 MCAR（缺失与任何变量无关）、随机缺失 MAR（缺失与已观测变量有关，条件于已观测变量后可忽略）、非随机缺失 MNAR（缺失与缺失值本身有关）。多重插补（MI）用链式方程生成 m 个（建议 20–50 次，缺失比例越高 m 越大）完整数据集，分别分析后用 Rubin 规则合并点估计与方差，正确体现插补不确定性；优于均值填补与完整病例分析（后者仅在 MCAR 且缺失比例很小时基本无偏）。MNAR 下 MI 仍有偏，需做敏感性分析（如 worst-case、δ 调整）；插补模型应纳入与缺失性及结局相关的辅助变量。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nmi set mlong                                  // 声明多重插补 long 格式\nmi register imputed y x                       // 登记含缺失的待插补变量\nmi impute chained (regress) y (logit) x = z age, add(20) rseed(2026)\n                                              // y 连续用 reg","url":"methods.html","source":"data/methods.json","details":{"id":"E1","name":"缺失数据与多重插补","category":"E","content":"**适用**：数据存在缺失值但需完整数据集进行回归/模型分析时，用多重插补替代删除法以减少偏倚与效能损失（如问卷调查、纵向随访、电子病历数据）。\n\n**要点**：缺失机制决定处理策略——完全随机缺失 MCAR（缺失与任何变量无关）、随机缺失 MAR（缺失与已观测变量有关，条件于已观测变量后可忽略）、非随机缺失 MNAR（缺失与缺失值本身有关）。多重插补（MI）用链式方程生成 m 个（建议 20–50 次，缺失比例越高 m 越大）完整数据集，分别分析后用 Rubin 规则合并点估计与方差，正确体现插补不确定性；优于均值填补与完整病例分析（后者仅在 MCAR 且缺失比例很小时基本无偏）。MNAR 下 MI 仍有偏，需做敏感性分析（如 worst-case、δ 调整）；插补模型应纳入与缺失性及结局相关的辅助变量。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nmi set mlong                                  // 声明多重插补 long 格式\nmi register imputed y x                       // 登记含缺失的待插补变量\nmi impute chained (regress) y (logit) x = z age, add(20) rseed(2026)\n                                              // y 连续用 regress、x 二分类用 logit；z age 为完整辅助变量\nmi estimate: regress y x age                  // 逐集估计 + Rubin 规则合并\n* ===== R =====\nlibrary(mice)\nimp <- mice(data, m = 20, seed = 2026)        # 链式方程多重插补\ndensityplot(imp)                              # 检查插补值分布\nfit <- with(imp, glm(y ~ x + age, family = binomial()))   # 逐集估计\npool(fit)                                     # Rubin 规则合并\n```\n\n**报告要点**：报告各变量缺失比例与缺失模式，明确缺失机制假设（MCAR/MAR/MNAR 判断依据）、插补模型与 m 值、Rubin 合并规则；并与完整病例分析并列作敏感性比较。\n\n**常见错误**：用均值/末次观测填补、只插补 1 次（m=1）当多重插补，或插补模型漏掉预测缺失与结局的关键变量。","source":"skills/references/methods-54.md"}},{"id":"methods:E2","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"样本量与统计效能计算","summary":"**适用**：研究设计阶段确定所需样本量、或评估既有样本的检验效能，保证结果可解释（RCT、两率比较、抽样调查、预测模型开发）。\n\n**要点**：先固定四个量：效应量（取最小临床重要差异 MCID，或文献/预试验值）、显著性水平 α（通常 0.05）、目标效能 1−β（通常 0.8/0.9）与设计参数（组间比、方差），已知任意三个可解出第四个。效应量须由专业判断或外部证据给定，不能用当前观测数据倒推（事后效能无意义）。连续结局用标准化均数差 d = (μ1−μ2)/σ，分类结局用率差或 OR；分层、整群与重复测量设计须乘设计效应 DE = 1 + (m−1)ICC 并预留脱落率。\n\n**代码**：\n\n```stata\n* ===== Stata =====\npower twomeans 100 110, sd(20) power(0.8)    // 两组均数 100 vs 110、SD 20 → 每组 n\npower twomeans 100 110, sd(20) n(64)         // 固定每组 n=64 → 检验效能\npower twoproportions 0.30 0.20, power(0.8)   // 两率 0.30 vs 0.20 → 每组 n\npower oneproportion 0.20 0.25                // 单组率 0.20 对目标 0.25\npower twomeans 100 110, sd(20) power(0","url":"methods.html","source":"data/methods.json","details":{"id":"E2","name":"样本量与统计效能计算","category":"E","content":"**适用**：研究设计阶段确定所需样本量、或评估既有样本的检验效能，保证结果可解释（RCT、两率比较、抽样调查、预测模型开发）。\n\n**要点**：先固定四个量：效应量（取最小临床重要差异 MCID，或文献/预试验值）、显著性水平 α（通常 0.05）、目标效能 1−β（通常 0.8/0.9）与设计参数（组间比、方差），已知任意三个可解出第四个。效应量须由专业判断或外部证据给定，不能用当前观测数据倒推（事后效能无意义）。连续结局用标准化均数差 d = (μ1−μ2)/σ，分类结局用率差或 OR；分层、整群与重复测量设计须乘设计效应 DE = 1 + (m−1)ICC 并预留脱落率。\n\n**代码**：\n\n```stata\n* ===== Stata =====\npower twomeans 100 110, sd(20) power(0.8)    // 两组均数 100 vs 110、SD 20 → 每组 n\npower twomeans 100 110, sd(20) n(64)         // 固定每组 n=64 → 检验效能\npower twoproportions 0.30 0.20, power(0.8)   // 两率 0.30 vs 0.20 → 每组 n\npower oneproportion 0.20 0.25                // 单组率 0.20 对目标 0.25\npower twomeans 100 110, sd(20) power(0.8) nratio(2)   // 组间比 1:2 设计\n* ===== R =====\nlibrary(pwr)\npwr.t.test(d = 0.5, sig.level = 0.05, power = 0.8, type = \"two.sample\")  // 每组 n\npwr.t.test(d = 0.5, sig.level = 0.05, n = 64, type = \"two.sample\")       // 固定 n → 效能\npwr.2p.test(h = ES.h(0.3, 0.2), sig.level = 0.05, power = 0.8)           // 两率\npwr.anova.test(k = 3, f = 0.25, sig.level = 0.05, power = 0.8)           // 多组方差分析\npwr.chisq.test(w = 0.1, df = 1, sig.level = 0.05, power = 0.8)           // 卡方检验\n```\n\n**报告要点**：报告四个要素（效应量及其来源、α、1−β、设计参数），给出每组所需样本量、考虑脱落后最终样本量；附效能曲线或关键参数敏感性表。\n\n**常见错误**：用观察到的效应量做“事后效能”论证、忽略脱落率与整群/重复测量的设计效应，或多重比较不校正 α。","source":"skills/references/methods-54.md"}},{"id":"methods:E3","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"LASSO/弹性网与正则化","summary":"**适用**：高维数据（变量数接近或超过样本量）下的变量筛选与惩罚回归，防止过拟合。\n\n**要点**：LASSO 用 L1 惩罚把部分系数压缩为 0，实现变量选择；岭回归用 L2 收缩但不稀疏；弹性网混合两者，变量高度相关时更稳。λ 用交叉验证选择（常取 1-SE 规则，模型更简约）；结局决定 family（连续/二分类）。LASSO 系数有收缩偏倚，P 值需谨慎（样本拆分或去偏）。完整预处理、调参与评估流程详见机器学习 skill。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nlasso linear y x1-x200, selection(cv) nolog    // 连续结局，CV 选 λ\nlasso logit y x1-x200, selection(cv)           // 二分类结局\n* ===== R =====\ncvfit <- glmnet::cv.glmnet(x, y, family = \"binomial\", alpha = 0.5)   // 弹性网\ncoef(cvfit, s = \"lambda.1se\")                  // 1-SE λ 的系数\n```\n\n**报告要点**：报告惩罚类型与 α、λ 选择规则（min/1se）与 CV 折数、最终保留变量及系数（注明标准化与否）。\n**常见错误**：把 LASSO 系数直接报显著性 P 值、在测试集上选 λ、连续变量不标准化。","url":"methods.html","source":"data/methods.json","details":{"id":"E3","name":"LASSO/弹性网与正则化","category":"E","content":"**适用**：高维数据（变量数接近或超过样本量）下的变量筛选与惩罚回归，防止过拟合。\n\n**要点**：LASSO 用 L1 惩罚把部分系数压缩为 0，实现变量选择；岭回归用 L2 收缩但不稀疏；弹性网混合两者，变量高度相关时更稳。λ 用交叉验证选择（常取 1-SE 规则，模型更简约）；结局决定 family（连续/二分类）。LASSO 系数有收缩偏倚，P 值需谨慎（样本拆分或去偏）。完整预处理、调参与评估流程详见机器学习 skill。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nlasso linear y x1-x200, selection(cv) nolog    // 连续结局，CV 选 λ\nlasso logit y x1-x200, selection(cv)           // 二分类结局\n* ===== R =====\ncvfit <- glmnet::cv.glmnet(x, y, family = \"binomial\", alpha = 0.5)   // 弹性网\ncoef(cvfit, s = \"lambda.1se\")                  // 1-SE λ 的系数\n```\n\n**报告要点**：报告惩罚类型与 α、λ 选择规则（min/1se）与 CV 折数、最终保留变量及系数（注明标准化与否）。\n**常见错误**：把 LASSO 系数直接报显著性 P 值、在测试集上选 λ、连续变量不标准化。","source":"skills/references/methods-54.md"}},{"id":"methods:E4","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"随机森林与 XGBoost","summary":"**适用**：非线性、含复杂交互或高维的分类/回归预测任务，作为灵活强基线模型（风险预测、结局分类）。\n\n**要点**：随机森林 = 多棵决策树 bagging + 随机特征子集，抗过拟合、稳健，袋外（OOB）误差可作内部验证；XGBoost = 梯度提升迭代修正残差，精度常更高但对超参敏感、需早停。两者均须调参（树数/深度/学习率）并在独立测试集或交叉验证评估。完整调参、变量重要性与模型解释流程详见机器学习 skill。\n\n**代码**：\n\n```stata\n* ===== Stata =====\n* Stata 无内置实现；可经 PyStata 调用 Python（sklearn/xgboost）\n* ===== R =====\nlibrary(randomForest); library(xgboost); set.seed(2026)\nrf <- randomForest(y ~ ., data = train, ntree = 500, importance = TRUE)   # 分类/回归自动识别\nbst <- xgboost(data = xgb.DMatrix(as.matrix(train_x), label = train_y),\n               nrounds = 100, eta = 0.1, max_depth = 3, objective = \"binary:logistic\")\n```\n\n**报告要点**：报告调参范围、重抽样策略（C","url":"methods.html","source":"data/methods.json","details":{"id":"E4","name":"随机森林与 XGBoost","category":"E","content":"**适用**：非线性、含复杂交互或高维的分类/回归预测任务，作为灵活强基线模型（风险预测、结局分类）。\n\n**要点**：随机森林 = 多棵决策树 bagging + 随机特征子集，抗过拟合、稳健，袋外（OOB）误差可作内部验证；XGBoost = 梯度提升迭代修正残差，精度常更高但对超参敏感、需早停。两者均须调参（树数/深度/学习率）并在独立测试集或交叉验证评估。完整调参、变量重要性与模型解释流程详见机器学习 skill。\n\n**代码**：\n\n```stata\n* ===== Stata =====\n* Stata 无内置实现；可经 PyStata 调用 Python（sklearn/xgboost）\n* ===== R =====\nlibrary(randomForest); library(xgboost); set.seed(2026)\nrf <- randomForest(y ~ ., data = train, ntree = 500, importance = TRUE)   # 分类/回归自动识别\nbst <- xgboost(data = xgb.DMatrix(as.matrix(train_x), label = train_y),\n               nrounds = 100, eta = 0.1, max_depth = 3, objective = \"binary:logistic\")\n```\n\n**报告要点**：报告调参范围、重抽样策略（CV/独立测试集）与区分度、校准指标（AUC、Brier）。\n**常见错误**：只在训练集报告精度（过拟合乐观），或不做调优直接套用默认参数。","source":"skills/references/methods-54.md"}},{"id":"methods:E5","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"聚类分析（K-means/层次聚类）","summary":"**适用**：无监督地将个体或变量分成同质组，用于人群分型（患者亚组、健康素养/就医行为类型）、变量降维与问卷条目归类。\n\n**要点**：分析前须标准化（量纲不同时）；K-means 将样本迭代分配到最近质心、最小化组内平方和，需预先指定 K；层次聚类（凝聚/分裂）不预设 K，按距离逐层合并并用树状图展示。K 的选择：肘部法（组内平方和随 K 的拐点）、轮廓系数（越接近 1 越好）或树状图截断，最终由专业可解释性定夺。结果依赖距离度量（欧氏/曼哈顿）、标准化与初始值，须设种子并比较多种方案验证稳定性。\n\n**代码**：\n\n```stata\n* ===== Stata =====\ncluster kmeans x1 x2, k(3) start(krandom(2026)) name(k3)   // K-means（x1 x2 宜先标准化）\ncluster stop, rule(duda)                      // 肘部指标辅助选 K（Duda-Hart）\ncluster wardslinkage x1 x2, name(hc)          // 层次聚类（Ward 法）\ncluster dendrogram hc, labels(id)             // 树状图\ncluster generate g = groups(k3)               // 保存 K-means 分组结果\n* ===== R =====\nset.seed","url":"methods.html","source":"data/methods.json","details":{"id":"E5","name":"聚类分析（K-means/层次聚类）","category":"E","content":"**适用**：无监督地将个体或变量分成同质组，用于人群分型（患者亚组、健康素养/就医行为类型）、变量降维与问卷条目归类。\n\n**要点**：分析前须标准化（量纲不同时）；K-means 将样本迭代分配到最近质心、最小化组内平方和，需预先指定 K；层次聚类（凝聚/分裂）不预设 K，按距离逐层合并并用树状图展示。K 的选择：肘部法（组内平方和随 K 的拐点）、轮廓系数（越接近 1 越好）或树状图截断，最终由专业可解释性定夺。结果依赖距离度量（欧氏/曼哈顿）、标准化与初始值，须设种子并比较多种方案验证稳定性。\n\n**代码**：\n\n```stata\n* ===== Stata =====\ncluster kmeans x1 x2, k(3) start(krandom(2026)) name(k3)   // K-means（x1 x2 宜先标准化）\ncluster stop, rule(duda)                      // 肘部指标辅助选 K（Duda-Hart）\ncluster wardslinkage x1 x2, name(hc)          // 层次聚类（Ward 法）\ncluster dendrogram hc, labels(id)             // 树状图\ncluster generate g = groups(k3)               // 保存 K-means 分组结果\n* ===== R =====\nset.seed(2026); km <- kmeans(scale(x), centers = 3, nstart = 25)   # K-means（scale 标准化）\nd <- dist(scale(x)); hc <- hclust(d, method = \"ward.D2\")           # 层次聚类（Ward 法）\nplot(hc); table(cutree(hc, k = 3), km$cluster)                     # 树状图；两法结果交叉\nlibrary(cluster)\nmean(silhouette(km$cluster, dist(scale(x)))[, 3])                  # 平均轮廓系数（选 K）\n```\n\n**报告要点**：报告标准化方式、距离度量与聚类方法、K 的选择依据（肘部/轮廓系数/树状图）与各组样本量；描述各组特征并验证稳定性（不同种子/子样本重复）。\n\n**常见错误**：不标准化直接聚类、凭主观随意定 K 不报告依据，或聚类后不验证稳定性与可解释性。","source":"skills/references/methods-54.md"}},{"id":"methods:E6","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"贝叶斯统计入门","summary":"**适用**：需要结合先验知识、小样本推断、或输出参数完整后验分布与不确定性区间的分析（网状 meta 分析、卫生经济学评价、适应性设计、层次模型）。\n\n**要点**：贝叶斯推断 = 后验 ∝ 先验 × 似然，先验（文献/专家/无信息弱信息）与数据结合更新信念；区间用 95% 可信区间（credible interval：参数有 95% 概率落于其中），区别于频率学派的置信区间。用 MCMC 抽样近似后验，必须检查收敛（链轨迹混合、R-hat < 1.01、有效样本量 ESS 充足）并报告链数、迭代数与预热期。先验选择需说明依据并做先验敏感性分析（不同先验下结论是否稳健）。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nbayes, mcmcsize(10000) rseed(2026): regress y x   // 默认弱信息先验的贝叶斯回归\nbayesgraph diagnostics {y:x}                      // 轨迹/自相关/R-hat 收敛诊断\nbayesstats ess                                   // 有效样本量\nbayesstats summary, credible                     // 后验均值与 95% 可信区间\nbayes, prior({y:x}, normal(0, 10)) rseed(2026): regress y","url":"methods.html","source":"data/methods.json","details":{"id":"E6","name":"贝叶斯统计入门","category":"E","content":"**适用**：需要结合先验知识、小样本推断、或输出参数完整后验分布与不确定性区间的分析（网状 meta 分析、卫生经济学评价、适应性设计、层次模型）。\n\n**要点**：贝叶斯推断 = 后验 ∝ 先验 × 似然，先验（文献/专家/无信息弱信息）与数据结合更新信念；区间用 95% 可信区间（credible interval：参数有 95% 概率落于其中），区别于频率学派的置信区间。用 MCMC 抽样近似后验，必须检查收敛（链轨迹混合、R-hat < 1.01、有效样本量 ESS 充足）并报告链数、迭代数与预热期。先验选择需说明依据并做先验敏感性分析（不同先验下结论是否稳健）。\n\n**代码**：\n\n```stata\n* ===== Stata =====\nbayes, mcmcsize(10000) rseed(2026): regress y x   // 默认弱信息先验的贝叶斯回归\nbayesgraph diagnostics {y:x}                      // 轨迹/自相关/R-hat 收敛诊断\nbayesstats ess                                   // 有效样本量\nbayesstats summary, credible                     // 后验均值与 95% 可信区间\nbayes, prior({y:x}, normal(0, 10)) rseed(2026): regress y x   // 自定义先验 → 敏感性分析\n* ===== R =====\nlibrary(brms)\nfit <- brm(y ~ x, data = d, chains = 4, iter = 2000, warmup = 1000, seed = 2026)\nsummary(fit)                                 # 后验均值、95% CI、R-hat、ESS\nplot(fit)                                    # 链轨迹与后验密度\nhypothesis(fit, \"x > 0\")                     # 后验概率 P(x > 0)\n```\n\n**报告要点**：报告先验分布及依据、MCMC 设置（链数/迭代/预热/种子）与收敛指标（R-hat、ESS）；效应量给出后验均值与 95% 可信区间，附先验敏感性分析。\n\n**常见错误**：把可信区间当置信区间解释、不检查收敛就下结论，或先验无依据且不做敏感性分析。\n\n---","source":"skills/references/methods-54.md"}},{"id":"methods:F1","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"Andersen 卫生服务利用行为模型","summary":"**适用**：解释与预测个体卫生服务利用（是否就诊、就诊次数、慢病服务利用），为就医行为与卫生服务研究提供变量选择框架。\n\n**要点**：模型由三类因素构成——倾向特征（predisposing：年龄、性别、教育、健康信念）、使能资源（enabling：收入、医保、机构可及性）、需要因素（need：自评健康、疾病状况），其中需要因素通常对利用的解释力最强。初始模型（1960s）后经多轮扩展，加入卫生系统、环境与健康行为/结局（BMH 版）。论文用法：按三因素组织自变量并分组报告，检验「需要主导」假设。\n\n**代码**（论文应用模板：三因素变量清单 + 回归）：\n\n```stata\n* Andersen 三因素变量清单（就诊行为研究示例）\n* 倾向特征 predisposing：age 年龄、female 性别(0/1)、edu 教育年限\n* 使能资源 enabling：lnincome 家庭收入对数、insured 是否参保(0/1)、dist 距最近机构(km)\n* 需要因素 need：sahealth 自评健康(1-5)、chronic 慢病种数\nlogit visit age female edu lnincome insured dist sahealth chronic, or\n```\n\n**报告要点**：理论框架部分画三因素模型图，逐项说明本研究变量与模型的对应关系；讨论中用「需要因素是否主导、使能因素是否受限」呼应模型预测并推导政策含义。\n\n**常见错误**：三因素","url":"methods.html","source":"data/methods.json","details":{"id":"F1","name":"Andersen 卫生服务利用行为模型","category":"F","content":"**适用**：解释与预测个体卫生服务利用（是否就诊、就诊次数、慢病服务利用），为就医行为与卫生服务研究提供变量选择框架。\n\n**要点**：模型由三类因素构成——倾向特征（predisposing：年龄、性别、教育、健康信念）、使能资源（enabling：收入、医保、机构可及性）、需要因素（need：自评健康、疾病状况），其中需要因素通常对利用的解释力最强。初始模型（1960s）后经多轮扩展，加入卫生系统、环境与健康行为/结局（BMH 版）。论文用法：按三因素组织自变量并分组报告，检验「需要主导」假设。\n\n**代码**（论文应用模板：三因素变量清单 + 回归）：\n\n```stata\n* Andersen 三因素变量清单（就诊行为研究示例）\n* 倾向特征 predisposing：age 年龄、female 性别(0/1)、edu 教育年限\n* 使能资源 enabling：lnincome 家庭收入对数、insured 是否参保(0/1)、dist 距最近机构(km)\n* 需要因素 need：sahealth 自评健康(1-5)、chronic 慢病种数\nlogit visit age female edu lnincome insured dist sahealth chronic, or\n```\n\n**报告要点**：理论框架部分画三因素模型图，逐项说明本研究变量与模型的对应关系；讨论中用「需要因素是否主导、使能因素是否受限」呼应模型预测并推导政策含义。\n\n**常见错误**：三因素不齐却声称「基于 Andersen 模型」；只列变量不说明归属与依据，模型沦为装饰。","source":"skills/references/methods-54.md"}},{"id":"methods:F2","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"Grossman 健康需求模型","summary":"**适用**：把健康视为人力资本，分析健康投资的决定因素及健康与教育、收入、年龄的关系，用于健康不平等与老年健康研究。\n\n**要点**：健康既是消费品（健康本身产生效用）又是投资品（健康时间增加劳动供给）；健康存量随年龄折旧，个体通过医疗服务等投资维持存量，投资量由工资率、教育效率与医疗服务价格决定。论文用法：用模型说明健康方程为何必须控制收入、教育与年龄，并据此提出机制性假设（教育提高健康生产效率）。\n\n**代码**（论文应用模板：需求简化式示意）：\n\n```stata\n* 健康需求简化式：H = f(工资率 w, 教育 E, 年龄 t, 医疗服务价格 p)\n* 预期：教育、工资系数为正（效率与投资回报），年龄为负（折旧），价格为负\nreg health lnwage edu age age2 lnprice\n* 教育→健康的机制检验：加入知识/行为中介后看 edu 系数变化\nreg health edu age\nreg health edu age knowl healthbehavior\n```\n\n**报告要点**：在理论框架部分以「健康存量—投资」逻辑引出变量；讨论中解释教育与年龄的系数方向如何与折旧、效率机制一致。\n\n**常见错误**：与 Andersen 模型混用（前者是需求决定机制，后者是利用分析框架）；只做相关回归不交代机制。","url":"methods.html","source":"data/methods.json","details":{"id":"F2","name":"Grossman 健康需求模型","category":"F","content":"**适用**：把健康视为人力资本，分析健康投资的决定因素及健康与教育、收入、年龄的关系，用于健康不平等与老年健康研究。\n\n**要点**：健康既是消费品（健康本身产生效用）又是投资品（健康时间增加劳动供给）；健康存量随年龄折旧，个体通过医疗服务等投资维持存量，投资量由工资率、教育效率与医疗服务价格决定。论文用法：用模型说明健康方程为何必须控制收入、教育与年龄，并据此提出机制性假设（教育提高健康生产效率）。\n\n**代码**（论文应用模板：需求简化式示意）：\n\n```stata\n* 健康需求简化式：H = f(工资率 w, 教育 E, 年龄 t, 医疗服务价格 p)\n* 预期：教育、工资系数为正（效率与投资回报），年龄为负（折旧），价格为负\nreg health lnwage edu age age2 lnprice\n* 教育→健康的机制检验：加入知识/行为中介后看 edu 系数变化\nreg health edu age\nreg health edu age knowl healthbehavior\n```\n\n**报告要点**：在理论框架部分以「健康存量—投资」逻辑引出变量；讨论中解释教育与年龄的系数方向如何与折旧、效率机制一致。\n\n**常见错误**：与 Andersen 模型混用（前者是需求决定机制，后者是利用分析框架）；只做相关回归不交代机制。","source":"skills/references/methods-54.md"}},{"id":"methods:F3","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"健康信念模型 HBM","summary":"**适用**：解释与干预个体预防性健康行为（疫苗接种、筛查、体检、遵医服药），为问卷维度设计与干预设计提供依据。\n\n**要点**：行为由六个构念驱动——感知易感性、感知严重性、感知收益、感知障碍、行动线索、自我效能（后两者为修订版加入）。论文用法：按维度编制 Likert 量表条目（每维度 ≥ 3 题，取均值入模），以预防行为为因变量做 logistic 回归或结构方程。\n\n**代码**（论文应用模板：维度变量清单）：\n\n```stata\n* HBM 维度量表（Likert 1-5，各维度多题取均值）与预防行为模型\n* perc_sus 感知易感性 | perc_sev 感知严重性 | perc_ben 感知收益 | perc_bar 感知障碍\n* cue 行动线索(0/1) | selfeff 自我效能\nlogit vaccinate perc_sus perc_sev perc_ben perc_bar cue selfeff, or\n```\n\n**报告要点**：方法部分报各维度的信度（Cronbach's α）与效度；讨论按维度解释作用方向，并把「感知障碍最显著」等结果转化为干预靶点。\n\n**常见错误**：每维度只设 1 题且不报信效度；用 HBM 解释治疗行为（HBM 针对预防行为，治疗行为宜用 TPB）。","url":"methods.html","source":"data/methods.json","details":{"id":"F3","name":"健康信念模型 HBM","category":"F","content":"**适用**：解释与干预个体预防性健康行为（疫苗接种、筛查、体检、遵医服药），为问卷维度设计与干预设计提供依据。\n\n**要点**：行为由六个构念驱动——感知易感性、感知严重性、感知收益、感知障碍、行动线索、自我效能（后两者为修订版加入）。论文用法：按维度编制 Likert 量表条目（每维度 ≥ 3 题，取均值入模），以预防行为为因变量做 logistic 回归或结构方程。\n\n**代码**（论文应用模板：维度变量清单）：\n\n```stata\n* HBM 维度量表（Likert 1-5，各维度多题取均值）与预防行为模型\n* perc_sus 感知易感性 | perc_sev 感知严重性 | perc_ben 感知收益 | perc_bar 感知障碍\n* cue 行动线索(0/1) | selfeff 自我效能\nlogit vaccinate perc_sus perc_sev perc_ben perc_bar cue selfeff, or\n```\n\n**报告要点**：方法部分报各维度的信度（Cronbach's α）与效度；讨论按维度解释作用方向，并把「感知障碍最显著」等结果转化为干预靶点。\n\n**常见错误**：每维度只设 1 题且不报信效度；用 HBM 解释治疗行为（HBM 针对预防行为，治疗行为宜用 TPB）。","source":"skills/references/methods-54.md"}},{"id":"methods:F4","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"计划行为理论 TPB","summary":"**适用**：解释由行为意向驱动的健康行为（锻炼、戒烟、手卫生、用药依从），用于问卷编制与行为干预研究。\n\n**要点**：行为意向由态度、主观规范、知觉行为控制（PBC）三者决定；行为则由意向与 PBC 共同预测，且 PBC 可绕过意向直接作用于行为。论文用法：用结构方程模型（SEM）检验 TPB 全路径，报告路径系数与模型拟合指标。\n\n**代码**（论文应用模板：SEM 语法）：\n\n```stata\n* Stata sem：观测题项均值入模（单指标路径模型）\nsem (intention <- att sn pbc) (behavior <- intention pbc), standardized\nestat gof, stats(all)\n```\n\n```r","url":"methods.html","source":"data/methods.json","details":{"id":"F4","name":"计划行为理论 TPB","category":"F","content":"**适用**：解释由行为意向驱动的健康行为（锻炼、戒烟、手卫生、用药依从），用于问卷编制与行为干预研究。\n\n**要点**：行为意向由态度、主观规范、知觉行为控制（PBC）三者决定；行为则由意向与 PBC 共同预测，且 PBC 可绕过意向直接作用于行为。论文用法：用结构方程模型（SEM）检验 TPB 全路径，报告路径系数与模型拟合指标。\n\n**代码**（论文应用模板：SEM 语法）：\n\n```stata\n* Stata sem：观测题项均值入模（单指标路径模型）\nsem (intention <- att sn pbc) (behavior <- intention pbc), standardized\nestat gof, stats(all)\n```\n\n```r","source":"skills/references/methods-54.md"}},{"id":"methods:F5","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"社会生态学模型","summary":"**适用**：识别个体、人际、社区/组织、政策多层影响因素，为多水平干预设计（慢病管理、控烟、体力活动促进）提供框架。\n\n**要点**：行为由嵌套层级共同决定——个体内（知识、信念）、人际（家庭、同伴）、组织/社区（学校、工作场所、社区资源）、政策（法规、环境）。论文用法：按层级组织变量，用多水平模型同时估计个体层与群体层效应，干预建议对应各层级。\n\n**代码**（论文应用模板：分层变量 + 多水平模型）：\n\n```stata\n* 个体层：age sex edu | 人际层：fam_support 家庭支持(0-3) | 社区层：park_density 绿地密度\n* 随机截距两水平模型（个体嵌套于社区）\nmixed behavior age sex edu fam_support park_density || community: , mle\n* 跨层交互：社区环境调节个体因素\nmixed behavior age sex edu fam_support c.fam_support#c.park_density || community: , mle\n```\n\n**报告要点**：理论框架部分画出层级图并标出本研究落在哪几层；讨论按层级解读系数（个体层 vs 群体层）并给出分层干预建议。\n\n**常见错误**：声称用生态学框架却把社区变量当个体变量做 OLS（忽视组内相关、易犯生态谬误）；层级划分与数据结构不匹配。","url":"methods.html","source":"data/methods.json","details":{"id":"F5","name":"社会生态学模型","category":"F","content":"**适用**：识别个体、人际、社区/组织、政策多层影响因素，为多水平干预设计（慢病管理、控烟、体力活动促进）提供框架。\n\n**要点**：行为由嵌套层级共同决定——个体内（知识、信念）、人际（家庭、同伴）、组织/社区（学校、工作场所、社区资源）、政策（法规、环境）。论文用法：按层级组织变量，用多水平模型同时估计个体层与群体层效应，干预建议对应各层级。\n\n**代码**（论文应用模板：分层变量 + 多水平模型）：\n\n```stata\n* 个体层：age sex edu | 人际层：fam_support 家庭支持(0-3) | 社区层：park_density 绿地密度\n* 随机截距两水平模型（个体嵌套于社区）\nmixed behavior age sex edu fam_support park_density || community: , mle\n* 跨层交互：社区环境调节个体因素\nmixed behavior age sex edu fam_support c.fam_support#c.park_density || community: , mle\n```\n\n**报告要点**：理论框架部分画出层级图并标出本研究落在哪几层；讨论按层级解读系数（个体层 vs 群体层）并给出分层干预建议。\n\n**常见错误**：声称用生态学框架却把社区变量当个体变量做 OLS（忽视组内相关、易犯生态谬误）；层级划分与数据结构不匹配。","source":"skills/references/methods-54.md"}},{"id":"methods:F6","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"健康公平与正义理论（Rawls / Sen 可行能力）","summary":"**适用**：为健康公平测量与卫生政策评价提供规范基础，用于健康不平等测量、医保待遇公平性与政策伦理分析。\n\n**要点**：Rawls 差别原则——社会经济不平等安排应使最不利者获益最大（maximin）；Sen 可行能力——评价的核心是「人实际能做到什么（functionings/capabilities）」而非资源占有。论文用法：理论部分明确采纳的正义观，并据此选择公平测量指标（如集中指数、公平缺口）与「有利变化」的判定标准。\n\n**代码**（论文应用模板：集中指数计算）：\n\n```stata\n* 集中指数 CI（便利回归法）：按收入排序后，2*σ²_rank*(h/μ) 对 rank 回归\nsort income\ngen rank = _n / _N\nquietly summarize h\nlocal mu = r(mean)\nquietly summarize rank\ngen lhs = 2 * r(Var) * (h / `mu')\nreg lhs rank\n* _b[rank] 即 CI（正=亲富，负=亲穷）；SSC 版：ssc install conindex; conindex h, rankvar(income)\n```\n\n**报告要点**：理论框架部分说明采纳 Rawls 或 Sen 的理由，并解释它如何决定公平指标选择与结果判定；讨论中回到所采纳的正义观检验结论。\n\n**常见错误**：公平测量指标与所宣称的正义理论脱节；把「公平」与「平等」混为一谈，未交代","url":"methods.html","source":"data/methods.json","details":{"id":"F6","name":"健康公平与正义理论（Rawls / Sen 可行能力）","category":"F","content":"**适用**：为健康公平测量与卫生政策评价提供规范基础，用于健康不平等测量、医保待遇公平性与政策伦理分析。\n\n**要点**：Rawls 差别原则——社会经济不平等安排应使最不利者获益最大（maximin）；Sen 可行能力——评价的核心是「人实际能做到什么（functionings/capabilities）」而非资源占有。论文用法：理论部分明确采纳的正义观，并据此选择公平测量指标（如集中指数、公平缺口）与「有利变化」的判定标准。\n\n**代码**（论文应用模板：集中指数计算）：\n\n```stata\n* 集中指数 CI（便利回归法）：按收入排序后，2*σ²_rank*(h/μ) 对 rank 回归\nsort income\ngen rank = _n / _N\nquietly summarize h\nlocal mu = r(mean)\nquietly summarize rank\ngen lhs = 2 * r(Var) * (h / `mu')\nreg lhs rank\n* _b[rank] 即 CI（正=亲富，负=亲穷）；SSC 版：ssc install conindex; conindex h, rankvar(income)\n```\n\n**报告要点**：理论框架部分说明采纳 Rawls 或 Sen 的理由，并解释它如何决定公平指标选择与结果判定；讨论中回到所采纳的正义观检验结论。\n\n**常见错误**：公平测量指标与所宣称的正义理论脱节；把「公平」与「平等」混为一谈，未交代价值判断。","source":"skills/references/methods-54.md"}},{"id":"methods:F7","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"健康社会决定因素框架（CSDH）","summary":"**适用**：分析结构性/中间决定因素如何沿社会经济梯度造成健康不平等，用于健康不平等机制与「将健康融入所有政策」研究。\n\n**要点**：因果链为「社会经济政治环境 → 社会经济地位（SES）→ 中间决定因素（物质条件、行为、生物、心理社会）→ 健康」，卫生系统本身也是社会决定因素。论文用法：把变量明确归入结构层与中间层，用分解方法（Oaxaca-Blinder、集中指数分解）量化各因素对健康不平等的贡献。\n\n**代码**（论文应用模板：分层变量 + 分解）：\n\n```stata\n* 结构层：ses 教育年限/收入/职业 | 中间层：housing 居住条件、smoke 吸烟、stress 心理压力 | 结局：health\n* Oaxaca-Blinder 分解：高/低 SES 组健康差距分解为「禀赋效应 + 系数效应」\nssc install oaxaca\noaxaca health housing smoke stress, by(ses_group)\n```\n\n**报告要点**：在理论框架中画出 CSDH 因果链并把变量标注到对应层级；讨论中报告各决定因素的贡献排序，指出政策切入点（结构层优先）。\n\n**常见错误**：把 CSDH 当普通回归变量清单，不做分解或机制分析；结构层与中间层混排、因果方向写反。","url":"methods.html","source":"data/methods.json","details":{"id":"F7","name":"健康社会决定因素框架（CSDH）","category":"F","content":"**适用**：分析结构性/中间决定因素如何沿社会经济梯度造成健康不平等，用于健康不平等机制与「将健康融入所有政策」研究。\n\n**要点**：因果链为「社会经济政治环境 → 社会经济地位（SES）→ 中间决定因素（物质条件、行为、生物、心理社会）→ 健康」，卫生系统本身也是社会决定因素。论文用法：把变量明确归入结构层与中间层，用分解方法（Oaxaca-Blinder、集中指数分解）量化各因素对健康不平等的贡献。\n\n**代码**（论文应用模板：分层变量 + 分解）：\n\n```stata\n* 结构层：ses 教育年限/收入/职业 | 中间层：housing 居住条件、smoke 吸烟、stress 心理压力 | 结局：health\n* Oaxaca-Blinder 分解：高/低 SES 组健康差距分解为「禀赋效应 + 系数效应」\nssc install oaxaca\noaxaca health housing smoke stress, by(ses_group)\n```\n\n**报告要点**：在理论框架中画出 CSDH 因果链并把变量标注到对应层级；讨论中报告各决定因素的贡献排序，指出政策切入点（结构层优先）。\n\n**常见错误**：把 CSDH 当普通回归变量清单，不做分解或机制分析；结构层与中间层混排、因果方向写反。","source":"skills/references/methods-54.md"}},{"id":"methods:F8","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"委托代理与供方诱导需求（SID）","summary":"**适用**：解释医患信息不对称下供方诱导过度需求，用于卫生服务利用、过度医疗与医保控费研究。\n\n**要点**：医患信息不对称使医生同时充当患者代理人（决定需求）与服务提供者（获取收益），存在为自身利益诱导需求的动机；经典经验检验是「床位数/医生密度 → 利用量」正相关（Roemer 定律：造好的床位就会被填满）。论文用法：在控制需要与价格后，用供给资源变量的系数作为诱导需求的证据。\n\n**代码**（论文应用模板：简化估计式）：\n\n```stata\n* 供方诱导需求简化估计式：y = β0 + β1·beds + β2·need + β3·price + ε\n* β1 > 0 且在控制需要与价格后仍显著 → 诱导需求的证据\nreg visits beds_per1000 chronic lnprice insurance age\n* 稳健性：换供给指标（医生密度）重复\nreg visits doctors_per1000 chronic lnprice insurance age\n```\n\n**报告要点**：讨论中如实报告识别问题（供给与需求同时决定、地区未观测因素），说明缓解策略（工具变量、自然实验、滞后期）并谨慎表述因果。\n\n**常见错误**：把床位—利用正相关直接等同于诱导需求，忽视人口需要、价格与反向因果；不讨论内生性。","url":"methods.html","source":"data/methods.json","details":{"id":"F8","name":"委托代理与供方诱导需求（SID）","category":"F","content":"**适用**：解释医患信息不对称下供方诱导过度需求，用于卫生服务利用、过度医疗与医保控费研究。\n\n**要点**：医患信息不对称使医生同时充当患者代理人（决定需求）与服务提供者（获取收益），存在为自身利益诱导需求的动机；经典经验检验是「床位数/医生密度 → 利用量」正相关（Roemer 定律：造好的床位就会被填满）。论文用法：在控制需要与价格后，用供给资源变量的系数作为诱导需求的证据。\n\n**代码**（论文应用模板：简化估计式）：\n\n```stata\n* 供方诱导需求简化估计式：y = β0 + β1·beds + β2·need + β3·price + ε\n* β1 > 0 且在控制需要与价格后仍显著 → 诱导需求的证据\nreg visits beds_per1000 chronic lnprice insurance age\n* 稳健性：换供给指标（医生密度）重复\nreg visits doctors_per1000 chronic lnprice insurance age\n```\n\n**报告要点**：讨论中如实报告识别问题（供给与需求同时决定、地区未观测因素），说明缓解策略（工具变量、自然实验、滞后期）并谨慎表述因果。\n\n**常见错误**：把床位—利用正相关直接等同于诱导需求，忽视人口需要、价格与反向因果；不讨论内生性。","source":"skills/references/methods-54.md"}},{"id":"methods:F9","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"市场失灵与政府干预（公共品 / 外部性 / 信息不对称）","summary":"**适用**：论证政府干预卫生领域的必要性与方式选择，用于卫生政策分析、医保制度设计与公共卫生筹资研究。\n\n**要点**：卫生领域存在公共品（传染病防控、健康信息，非竞争非排他）、正外部性（疫苗接种、抗生素合理使用，私人收益低于社会收益）、信息不对称（医患关系诱导需求；保险市场逆选择与道德风险），市场配置失灵为政府干预提供理由。论文用法：把每条政策建议对应到具体失灵类型，形成「失灵—干预—机制」论证链。\n\n**代码**（论文应用模板：失灵—干预论证链提纲）：\n\n```text\n论文应用模板：市场失灵 → 对应卫生服务 → 干预手段（论证链）\n· 公共品：传染病监测、控烟立法（非竞争、非排他）→ 政府提供或强制\n· 正外部性：疫苗接种（私人收益 < 社会收益，私人最优接种量低于社会最优）→ 补贴、免疫规划\n· 信息不对称①：医患（供方诱导需求）→ 支付方式改革、监管、信息公开\n· 信息不对称②：保险市场（逆选择、道德风险）→ 强制参保、共付与起付线设计\n```\n\n**报告要点**：在政策建议部分逐条注明依据的是哪类市场失灵及其证据；讨论政府干预本身可能的失灵（政府失灵）以保持分析平衡。\n\n**常见错误**：只罗列失灵类型不落到具体干预设计；不区分市场失灵与政府失灵，政策建议缺乏对应机制。","url":"methods.html","source":"data/methods.json","details":{"id":"F9","name":"市场失灵与政府干预（公共品 / 外部性 / 信息不对称）","category":"F","content":"**适用**：论证政府干预卫生领域的必要性与方式选择，用于卫生政策分析、医保制度设计与公共卫生筹资研究。\n\n**要点**：卫生领域存在公共品（传染病防控、健康信息，非竞争非排他）、正外部性（疫苗接种、抗生素合理使用，私人收益低于社会收益）、信息不对称（医患关系诱导需求；保险市场逆选择与道德风险），市场配置失灵为政府干预提供理由。论文用法：把每条政策建议对应到具体失灵类型，形成「失灵—干预—机制」论证链。\n\n**代码**（论文应用模板：失灵—干预论证链提纲）：\n\n```text\n论文应用模板：市场失灵 → 对应卫生服务 → 干预手段（论证链）\n· 公共品：传染病监测、控烟立法（非竞争、非排他）→ 政府提供或强制\n· 正外部性：疫苗接种（私人收益 < 社会收益，私人最优接种量低于社会最优）→ 补贴、免疫规划\n· 信息不对称①：医患（供方诱导需求）→ 支付方式改革、监管、信息公开\n· 信息不对称②：保险市场（逆选择、道德风险）→ 强制参保、共付与起付线设计\n```\n\n**报告要点**：在政策建议部分逐条注明依据的是哪类市场失灵及其证据；讨论政府干预本身可能的失灵（政府失灵）以保持分析平衡。\n\n**常见错误**：只罗列失灵类型不落到具体干预设计；不区分市场失灵与政府失灵，政策建议缺乏对应机制。","source":"skills/references/methods-54.md"}},{"id":"methods:F10","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"制度变迁与路径依赖（医改分析）","summary":"**适用**：解释医改政策演进、制度为何难以变革及政策扩散规律，用于卫生体制改革与医保制度变迁研究。\n\n**要点**：制度变迁受历史路径约束——初始制度选择通过规模报酬递增、学习效应、协调效应、适应性预期与利益集团锁定而自我强化；制度突破依赖关键节点或外部冲击。论文用法：以政策时间线证据识别锁定机制与变迁触发点，解释医改反复或试点—推广过程。\n\n**代码**（论文应用模板：制度变迁分析提纲）：\n\n```text\n论文应用模板：医改制度变迁分析提纲\n1) 锁定起点：初始制度安排（如公费/劳保/新农合分轨起点）与关键节点（2009 新医改）\n2) 自我强化机制：规模报酬递增、沉没成本、利益集团、适应性预期\n3) 锁定证据：政策时间线——改革方案反复收敛于既有路径（如碎片化格局持续）\n4) 变迁触发：外部冲击（疫情、财政压力）或内部矛盾积累 → 制度突破/渐进调整\n```\n\n**报告要点**：用时间线表格或图呈现制度沿革，逐条对应锁定机制；讨论说明本案例中路径依赖的强度与突破条件，避免历史决定论。\n\n**常见错误**：把路径依赖当「历史决定论」只叙述不分析机制；时间线证据与机制脱节，未说明变迁触发条件。\n\n---","url":"methods.html","source":"data/methods.json","details":{"id":"F10","name":"制度变迁与路径依赖（医改分析）","category":"F","content":"**适用**：解释医改政策演进、制度为何难以变革及政策扩散规律，用于卫生体制改革与医保制度变迁研究。\n\n**要点**：制度变迁受历史路径约束——初始制度选择通过规模报酬递增、学习效应、协调效应、适应性预期与利益集团锁定而自我强化；制度突破依赖关键节点或外部冲击。论文用法：以政策时间线证据识别锁定机制与变迁触发点，解释医改反复或试点—推广过程。\n\n**代码**（论文应用模板：制度变迁分析提纲）：\n\n```text\n论文应用模板：医改制度变迁分析提纲\n1) 锁定起点：初始制度安排（如公费/劳保/新农合分轨起点）与关键节点（2009 新医改）\n2) 自我强化机制：规模报酬递增、沉没成本、利益集团、适应性预期\n3) 锁定证据：政策时间线——改革方案反复收敛于既有路径（如碎片化格局持续）\n4) 变迁触发：外部冲击（疫情、财政压力）或内部矛盾积累 → 制度突破/渐进调整\n```\n\n**报告要点**：用时间线表格或图呈现制度沿革，逐条对应锁定机制；讨论说明本案例中路径依赖的强度与突破条件，避免历史决定论。\n\n**常见错误**：把路径依赖当「历史决定论」只叙述不分析机制；时间线证据与机制脱节，未说明变迁触发条件。\n\n---","source":"skills/references/methods-54.md"}},{"id":"methods:G1","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"扎根理论（Grounded Theory）","summary":"**适用**：从访谈/观察/文本材料中归纳建构理论（医患体验、政策执行过程、患者旅程）。\n\n**要点**：三级编码——开放式编码（贴标签）、主轴编码（范畴与关系）、选择性编码（核心范畴与故事线）；理论抽样与饱和（新资料不再产生新范畴）；备忘录贯穿全程。Strauss-Corbin 偏结构化程序，Charmaz 偏建构主义。质性分析以**人工编码为主**，软件（NVivo/MAXQDA）与文本挖掘只是辅助；报告遵循 COREQ。\n\n**代码**：R 文本辅助（词频与共现），编码本身仍由人完成：\n\n```r\nlibrary(quanteda)\ncorp <- corpus(data.frame(text = interviews$text, doc_id = interviews$id))\ndfm(corp, remove = stopwords(\"zh\"), remove_punct = TRUE) |>\n  textstat_frequency(n = 30)          # 高频词提示开放编码线索\n```\n\n**报告要点**：编码表（范畴-子范畴-原始引文）、理论模型图、饱和度说明、研究者立场。\n\n**常见错误**：只贴词频不做理论建构；样本量小却宣称饱和；混淆描述与理论。","url":"methods.html","source":"data/methods.json","details":{"id":"G1","name":"扎根理论（Grounded Theory）","category":"G","content":"**适用**：从访谈/观察/文本材料中归纳建构理论（医患体验、政策执行过程、患者旅程）。\n\n**要点**：三级编码——开放式编码（贴标签）、主轴编码（范畴与关系）、选择性编码（核心范畴与故事线）；理论抽样与饱和（新资料不再产生新范畴）；备忘录贯穿全程。Strauss-Corbin 偏结构化程序，Charmaz 偏建构主义。质性分析以**人工编码为主**，软件（NVivo/MAXQDA）与文本挖掘只是辅助；报告遵循 COREQ。\n\n**代码**：R 文本辅助（词频与共现），编码本身仍由人完成：\n\n```r\nlibrary(quanteda)\ncorp <- corpus(data.frame(text = interviews$text, doc_id = interviews$id))\ndfm(corp, remove = stopwords(\"zh\"), remove_punct = TRUE) |>\n  textstat_frequency(n = 30)          # 高频词提示开放编码线索\n```\n\n**报告要点**：编码表（范畴-子范畴-原始引文）、理论模型图、饱和度说明、研究者立场。\n\n**常见错误**：只贴词频不做理论建构；样本量小却宣称饱和；混淆描述与理论。","source":"skills/references/methods-54.md"}},{"id":"methods:G2","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"结构方程模型（SEM）","summary":"**适用**：多个潜变量（如健康素养、自我效能）与其测量条目、结构路径的联合检验。\n\n**要点**：测量模型（CFA，效度：载荷、CR、AVE）与结构模型（路径）两步或联合估计；拟合指标 CFI/TLI ≥ 0.90、RMSEA < 0.08、SRMR < 0.08，修正指数仅用于理论允许的修正；样本量经验：每个自由参数 10–20 例。\n\n**代码**：\n\n```stata\n* Stata：三潜变量 SEM\nsem (HL -> x1 x2 x3) (SE -> y1 y2 y3) (AD -> z1 z2 z3) (HL -> SE) (SE -> AD), method(mlmv)\nestat gof\n```\n\n```r\n# R：lavaan 等价写法\nlibrary(lavaan)\nmodel <- ' HL =~ x1 + x2 + x3\n           SE =~ y1 + y2 + y3\n           AD =~ z1 + z2 + z3\n           SE ~ HL\n           AD ~ SE '\nfit <- sem(model, data = dat)\nfitMeasures(fit, c(\"cfi\",\"tli\",\"rmsea\",\"srmr\"))\n```\n\n**报告要点**：模型图、载荷与路径系数表（标准化）、拟合指标与判断标准、测量不变性（如做群组比较）。\n\n**常见错误**：把探索性结果当验证性模型报告；只看卡方显著性；无理","url":"methods.html","source":"data/methods.json","details":{"id":"G2","name":"结构方程模型（SEM）","category":"G","content":"**适用**：多个潜变量（如健康素养、自我效能）与其测量条目、结构路径的联合检验。\n\n**要点**：测量模型（CFA，效度：载荷、CR、AVE）与结构模型（路径）两步或联合估计；拟合指标 CFI/TLI ≥ 0.90、RMSEA < 0.08、SRMR < 0.08，修正指数仅用于理论允许的修正；样本量经验：每个自由参数 10–20 例。\n\n**代码**：\n\n```stata\n* Stata：三潜变量 SEM\nsem (HL -> x1 x2 x3) (SE -> y1 y2 y3) (AD -> z1 z2 z3) (HL -> SE) (SE -> AD), method(mlmv)\nestat gof\n```\n\n```r\n# R：lavaan 等价写法\nlibrary(lavaan)\nmodel <- ' HL =~ x1 + x2 + x3\n           SE =~ y1 + y2 + y3\n           AD =~ z1 + z2 + z3\n           SE ~ HL\n           AD ~ SE '\nfit <- sem(model, data = dat)\nfitMeasures(fit, c(\"cfi\",\"tli\",\"rmsea\",\"srmr\"))\n```\n\n**报告要点**：模型图、载荷与路径系数表（标准化）、拟合指标与判断标准、测量不变性（如做群组比较）。\n\n**常见错误**：把探索性结果当验证性模型报告；只看卡方显著性；无理论依据乱加修正路径。","source":"skills/references/methods-54.md"}},{"id":"methods:G3","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"多层 / 多重嵌套 SEM（Multilevel SEM）","summary":"**适用**：嵌套数据（患者—科室—医院、居民—社区）的潜变量与结构关系，区分组内与组间效应。\n\n**要点**：先检验 ICC 判断是否需要多层；组内/组间变异分解；样本量经验：组数至少 30–50，组内个体可少；随机截距与随机斜率设定要有理论依据。\n\n**代码**：\n\n```stata\n* Stata：gsem 多层（医院随机截距）\ngsem (HL -> x1 x2 x3) (SE -> y1 y2 y3) (SE <- HL M1[hospital]), vce(cluster hospital)\n```\n\n```r\n# R：lavaan 双层写法\nlibrary(lavaan)\nmodel2 <- '\n  level: 1\n    SEw =~ y1 + y2 + y3\n    SEw ~ HLw\n  level: 2\n    SEb =~ y1 + y2 + y3\n    SEb ~ HLb '\nfit2 <- sem(model2, data = dat, cluster = \"hospital\")\n```\n\n**报告要点**：ICC、组内与组间两层结果分表、层级说明与软件语法对照。\n\n**常见错误**：组数很少却做多层 SEM；把组间结论套到个体；忽略聚类稳健标准误。","url":"methods.html","source":"data/methods.json","details":{"id":"G3","name":"多层 / 多重嵌套 SEM（Multilevel SEM）","category":"G","content":"**适用**：嵌套数据（患者—科室—医院、居民—社区）的潜变量与结构关系，区分组内与组间效应。\n\n**要点**：先检验 ICC 判断是否需要多层；组内/组间变异分解；样本量经验：组数至少 30–50，组内个体可少；随机截距与随机斜率设定要有理论依据。\n\n**代码**：\n\n```stata\n* Stata：gsem 多层（医院随机截距）\ngsem (HL -> x1 x2 x3) (SE -> y1 y2 y3) (SE <- HL M1[hospital]), vce(cluster hospital)\n```\n\n```r\n# R：lavaan 双层写法\nlibrary(lavaan)\nmodel2 <- '\n  level: 1\n    SEw =~ y1 + y2 + y3\n    SEw ~ HLw\n  level: 2\n    SEb =~ y1 + y2 + y3\n    SEb ~ HLb '\nfit2 <- sem(model2, data = dat, cluster = \"hospital\")\n```\n\n**报告要点**：ICC、组内与组间两层结果分表、层级说明与软件语法对照。\n\n**常见错误**：组数很少却做多层 SEM；把组间结论套到个体；忽略聚类稳健标准误。","source":"skills/references/methods-54.md"}},{"id":"methods:G4","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"fsQCA 模糊集定性比较分析","summary":"**适用**：中小样本、多条路径达到同一结果（组态视角），如「基层能力建设成效」的条件组合：资源投入、人员配置、激励机制、信息化。\n\n**要点**：校准（直接法三锚点：完全隶属/交叉点/完全不隶属）；必要条件（一致性 ≥ 0.9）与充分条件（一致性 ≥ 0.75–0.8）；真值表与最小化得到复杂解/简约解/中间解；覆盖率（原始/唯一）；与回归互补：fsQCA 问「哪些组合」，回归问「净效应」。\n\n**代码**：\n\n```r\nlibrary(QCA)\ndat2 <- data.frame(成效 = calibrate(score, c(60, 75, 90)),\n                   资源 = calibrate(res, c(10, 20, 30)),\n                   人员 = calibrate(staff, c(5, 10, 15)),\n                   激励 = calibrate(inc, c(0.2, 0.5, 0.8)),\n                   信息化 = calibrate(it, c(1, 3, 5)))\ntt <- truthTable(dat2, outcome = \"成效\", n.cut = 1, incl.cut = 0.8)\nminimize(tt, include = \"?\")            # 中间解\n```\n\n```stata\n* Stata：SSC 的 fuzzy ","url":"methods.html","source":"data/methods.json","details":{"id":"G4","name":"fsQCA 模糊集定性比较分析","category":"G","content":"**适用**：中小样本、多条路径达到同一结果（组态视角），如「基层能力建设成效」的条件组合：资源投入、人员配置、激励机制、信息化。\n\n**要点**：校准（直接法三锚点：完全隶属/交叉点/完全不隶属）；必要条件（一致性 ≥ 0.9）与充分条件（一致性 ≥ 0.75–0.8）；真值表与最小化得到复杂解/简约解/中间解；覆盖率（原始/唯一）；与回归互补：fsQCA 问「哪些组合」，回归问「净效应」。\n\n**代码**：\n\n```r\nlibrary(QCA)\ndat2 <- data.frame(成效 = calibrate(score, c(60, 75, 90)),\n                   资源 = calibrate(res, c(10, 20, 30)),\n                   人员 = calibrate(staff, c(5, 10, 15)),\n                   激励 = calibrate(inc, c(0.2, 0.5, 0.8)),\n                   信息化 = calibrate(it, c(1, 3, 5)))\ntt <- truthTable(dat2, outcome = \"成效\", n.cut = 1, incl.cut = 0.8)\nminimize(tt, include = \"?\")            # 中间解\n```\n\n```stata\n* Stata：SSC 的 fuzzy 包做校准等（以 help fuzzy 为准）\nssc install fuzzy\nfzcalc zy, low(60) cross(75) high(90)\n```\n\n**报告要点**：校准锚点与理由、真值表、各解的组态表、一致性与覆盖率、稳健性（锚点微调/阈值变动）。\n\n**常见错误**：校准锚点拍脑袋不给理由；把组态解当净效应做因果推断；样本与条件数比例失衡。\n\n> 以上 4 条的完整教程（含完整模拟数据与更多代码）见 **sem-qca-qualitative-skill.md**。\n\n\n---","source":"skills/references/methods-54.md"}},{"id":"methods:A9","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"复杂抽样与调查权重","summary":"**适用**：分层整群调查的总体估计；区分横断面与纵向权重。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [复杂抽样与调查权重专题](longitudinal-public-data-skill.md)。\n\n**报告要点**：抽样层级、PSU、分层、权重版本、目标总体和方差估计。\n\n**常见错误**：直接把全国调查当简单随机样本，或把调查权重与因果权重混用。","url":"methods.html","source":"data/methods.json","details":{"id":"A9","name":"复杂抽样与调查权重","category":"A","content":"**适用**：分层整群调查的总体估计；区分横断面与纵向权重。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [复杂抽样与调查权重专题](longitudinal-public-data-skill.md)。\n\n**报告要点**：抽样层级、PSU、分层、权重版本、目标总体和方差估计。\n\n**常见错误**：直接把全国调查当简单随机样本，或把调查权重与因果权重混用。","source":"guides/longitudinal-public-data-skill.html"}},{"id":"methods:B11","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"一致性评价：Kappa / ICC","summary":"**适用**：名义、等级或连续评分的评审者一致性；与准确性分开。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [一致性评价：Kappa / ICC专题](medical-ai-evaluation-skill.md)。\n\n**报告要点**：评分者结构、量表类型、Kappa/ICC定义、区间及类别分布。\n\n**常见错误**：用相关性代替一致性，或将高一致性等同于高准确性。","url":"methods.html","source":"data/methods.json","details":{"id":"B11","name":"一致性评价：Kappa / ICC","category":"B","content":"**适用**：名义、等级或连续评分的评审者一致性；与准确性分开。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [一致性评价：Kappa / ICC专题](medical-ai-evaluation-skill.md)。\n\n**报告要点**：评分者结构、量表类型、Kappa/ICC定义、区间及类别分布。\n\n**常见错误**：用相关性代替一致性，或将高一致性等同于高准确性。","source":"guides/medical-ai-evaluation-skill.html"}},{"id":"methods:B12","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"竞争风险与累积发生率","summary":"**适用**：死亡等事件阻止目标结局发生时，估计原因别风险或实际累积风险。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [竞争风险与累积发生率专题](longitudinal-public-data-skill.md)。\n\n**报告要点**：事件定义、竞争事件、估计目标、时间点与绝对风险。\n\n**常见错误**：把竞争死亡作为无信息删失，直接用1-KM解释实际发生风险。","url":"methods.html","source":"data/methods.json","details":{"id":"B12","name":"竞争风险与累积发生率","category":"B","content":"**适用**：死亡等事件阻止目标结局发生时，估计原因别风险或实际累积风险。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [竞争风险与累积发生率专题](longitudinal-public-data-skill.md)。\n\n**报告要点**：事件定义、竞争事件、估计目标、时间点与绝对风险。\n\n**常见错误**：把竞争死亡作为无信息删失，直接用1-KM解释实际发生风险。","source":"guides/longitudinal-public-data-skill.html"}},{"id":"methods:D9","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"目标试验模拟 TTE","summary":"**适用**：把观察性数据分析对应到明确的假想试验协议。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [目标试验模拟 TTE专题](target-trial-emulation-skill.md)。\n\n**报告要点**：七要素协议、time zero、estimand、识别假设、诊断及TARGET。\n\n**常见错误**：资格、治疗分配和随访起点错位，或用未来依从性定义基线治疗组。","url":"methods.html","source":"data/methods.json","details":{"id":"D9","name":"目标试验模拟 TTE","category":"D","content":"**适用**：把观察性数据分析对应到明确的假想试验协议。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [目标试验模拟 TTE专题](target-trial-emulation-skill.md)。\n\n**报告要点**：七要素协议、time zero、estimand、识别假设、诊断及TARGET。\n\n**常见错误**：资格、治疗分配和随访起点错位，或用未来依从性定义基线治疗组。","source":"guides/target-trial-emulation-skill.html"}},{"id":"methods:D10","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"IPW与边际结构模型 MSM","summary":"**适用**：基线或时间变化治疗的加权因果估计。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [IPW与边际结构模型 MSM专题](advanced-causal-inference-skill.md)。\n\n**报告要点**：概率模型、稳定化、截尾、平衡、有效样本量与删失机制。\n\n**常见错误**：忽略极端权重、正值性或受既往治疗影响的时间变化混杂。","url":"methods.html","source":"data/methods.json","details":{"id":"D10","name":"IPW与边际结构模型 MSM","category":"D","content":"**适用**：基线或时间变化治疗的加权因果估计。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [IPW与边际结构模型 MSM专题](advanced-causal-inference-skill.md)。\n\n**报告要点**：概率模型、稳定化、截尾、平衡、有效样本量与删失机制。\n\n**常见错误**：忽略极端权重、正值性或受既往治疗影响的时间变化混杂。","source":"guides/advanced-causal-inference-skill.html"}},{"id":"methods:D11","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"g-formula与双稳健估计","summary":"**适用**：按指定干预标准化结局，或结合治疗和结局模型估计目标效应。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [g-formula与双稳健估计专题](advanced-causal-inference-skill.md)。\n\n**报告要点**：目标人群、干预策略、模型设定、标准化流程和推断方法。\n\n**常见错误**：把双稳健误解为对未测量混杂也稳健；不核对两模型的假设。","url":"methods.html","source":"data/methods.json","details":{"id":"D11","name":"g-formula与双稳健估计","category":"D","content":"**适用**：按指定干预标准化结局，或结合治疗和结局模型估计目标效应。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [g-formula与双稳健估计专题](advanced-causal-inference-skill.md)。\n\n**报告要点**：目标人群、干预策略、模型设定、标准化流程和推断方法。\n\n**常见错误**：把双稳健误解为对未测量混杂也稳健；不核对两模型的假设。","source":"guides/advanced-causal-inference-skill.html"}},{"id":"methods:D12","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"双重机器学习 DML","summary":"**适用**：高维或非线性混杂下，利用正交得分与交叉拟合估计因果参数。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [双重机器学习 DML专题](advanced-causal-inference-skill.md)。\n\n**报告要点**：得分/模型、估计目标、折分单位、学习器、调参与聚类推断。\n\n**常见错误**：把高预测准确率视为因果识别，或在测试折调参导致信息泄漏。","url":"methods.html","source":"data/methods.json","details":{"id":"D12","name":"双重机器学习 DML","category":"D","content":"**适用**：高维或非线性混杂下，利用正交得分与交叉拟合估计因果参数。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [双重机器学习 DML专题](advanced-causal-inference-skill.md)。\n\n**报告要点**：得分/模型、估计目标、折分单位、学习器、调参与聚类推断。\n\n**常见错误**：把高预测准确率视为因果识别，或在测试折调参导致信息泄漏。","source":"guides/advanced-causal-inference-skill.html"}},{"id":"methods:D13","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"合成双重差分 SDID","summary":"**适用**：政策面板中同时构造单位权重与时间权重。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [合成双重差分 SDID专题](advanced-causal-inference-skill.md)。\n\n**报告要点**：供体池、处理日期、政策前拟合、权重、溢出及少簇推断。\n\n**常见错误**：忽略供体受政策影响，或把交错处理直接套入同时处理的块状实现。","url":"methods.html","source":"data/methods.json","details":{"id":"D13","name":"合成双重差分 SDID","category":"D","content":"**适用**：政策面板中同时构造单位权重与时间权重。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [合成双重差分 SDID专题](advanced-causal-inference-skill.md)。\n\n**报告要点**：供体池、处理日期、政策前拟合、权重、溢出及少簇推断。\n\n**常见错误**：忽略供体受政策影响，或把交错处理直接套入同时处理的块状实现。","source":"guides/advanced-causal-inference-skill.html"}},{"id":"methods:D14","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"定量偏倚与敏感性分析","summary":"**适用**：评估未测量混杂、误分类和选择机制对主要结论的影响。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [定量偏倚与敏感性分析专题](advanced-causal-inference-skill.md)。\n\n**报告要点**：偏倚机制、参数来源、情景范围、效应变化及剩余局限。\n\n**常见错误**：把敏感性摘要当作不存在偏倚的证据，或随意选择参数范围。","url":"methods.html","source":"data/methods.json","details":{"id":"D14","name":"定量偏倚与敏感性分析","category":"D","content":"**适用**：评估未测量混杂、误分类和选择机制对主要结论的影响。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [定量偏倚与敏感性分析专题](advanced-causal-inference-skill.md)。\n\n**报告要点**：偏倚机制、参数来源、情景范围、效应变化及剩余局限。\n\n**常见错误**：把敏感性摘要当作不存在偏倚的证据，或随意选择参数范围。","source":"guides/advanced-causal-inference-skill.html"}},{"id":"methods:E7","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"纵向轨迹与潜类别","summary":"**适用**：识别重复测量数据中的不同变化模式。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [纵向轨迹与潜类别专题](longitudinal-public-data-skill.md)。\n\n**报告要点**：时间编码、模型/类数选择、类别大小、后验概率及失访处理。\n\n**常见错误**：把统计类别当自然人群，忽略后验分类不确定性和类数稳定性。","url":"methods.html","source":"data/methods.json","details":{"id":"E7","name":"纵向轨迹与潜类别","category":"E","content":"**适用**：识别重复测量数据中的不同变化模式。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [纵向轨迹与潜类别专题](longitudinal-public-data-skill.md)。\n\n**报告要点**：时间编码、模型/类数选择、类别大小、后验概率及失访处理。\n\n**常见错误**：把统计类别当自然人群，忽略后验分类不确定性和类数稳定性。","source":"guides/longitudinal-public-data-skill.html"}},{"id":"methods:E8","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"预测验证、校准与净获益","summary":"**适用**：从内部/外部验证判断模型判别、风险准确性和决策价值。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [预测验证、校准与净获益专题](medical-ai-evaluation-skill.md)。\n\n**报告要点**：独立验证设计、校准截距/斜率、区间、阈值及净获益比较。\n\n**常见错误**：仅凭训练集AUC或SHAP图宣称模型临床可用。","url":"methods.html","source":"data/methods.json","details":{"id":"E8","name":"预测验证、校准与净获益","category":"E","content":"**适用**：从内部/外部验证判断模型判别、风险准确性和决策价值。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [预测验证、校准与净获益专题](medical-ai-evaluation-skill.md)。\n\n**报告要点**：独立验证设计、校准截距/斜率、区间、阈值及净获益比较。\n\n**常见错误**：仅凭训练集AUC或SHAP图宣称模型临床可用。","source":"guides/medical-ai-evaluation-skill.html"}},{"id":"methods:F11","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"实施科学：CFIR / RE-AIM","summary":"**适用**：解释干预为何难落地，并评价触达、实施与维持。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [实施科学：CFIR / RE-AIM专题](implementation-equity-skill.md)。\n\n**报告要点**：框架版本、构念定义、分母、层级、时间窗与弱势群体触达。\n\n**常见错误**：堆叠框架名称而没有构念测量，把实施结局与健康结局混为一谈。","url":"methods.html","source":"data/methods.json","details":{"id":"F11","name":"实施科学：CFIR / RE-AIM","category":"F","content":"**适用**：解释干预为何难落地，并评价触达、实施与维持。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [实施科学：CFIR / RE-AIM专题](implementation-equity-skill.md)。\n\n**报告要点**：框架版本、构念定义、分母、层级、时间窗与弱势群体触达。\n\n**常见错误**：堆叠框架名称而没有构念测量，把实施结局与健康结局混为一谈。","source":"guides/implementation-equity-skill.html"}},{"id":"methods:H1","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"系统评价与证据分级","summary":"**适用**：对明确研究问题系统检索、筛选、评价并综合证据。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [系统评价与证据分级专题](research-workflow-skill.md)。\n\n**报告要点**：方案、检索式/日期、双人流程、排除原因、偏倚与确定性。\n\n**常见错误**：只有检索和文章汇总，没有预设标准、偏倚评价或排除记录。","url":"methods.html","source":"data/methods.json","details":{"id":"H1","name":"系统评价与证据分级","category":"H","content":"**适用**：对明确研究问题系统检索、筛选、评价并综合证据。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [系统评价与证据分级专题](research-workflow-skill.md)。\n\n**报告要点**：方案、检索式/日期、双人流程、排除原因、偏倚与确定性。\n\n**常见错误**：只有检索和文章汇总，没有预设标准、偏倚评价或排除记录。","source":"guides/research-workflow-skill.html"}},{"id":"methods:H2","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"成对 Meta 分析","summary":"**适用**：对可合并的同类比较与结局估计综合效应。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [成对 Meta 分析专题](meta-analysis-skill.md)。\n\n**报告要点**：效应方向、模型、τ²、区间、研究设计与异质性解释。\n\n**常见错误**：混合不相容效应尺度，按I²阈值机械选择模型。","url":"methods.html","source":"data/methods.json","details":{"id":"H2","name":"成对 Meta 分析","category":"H","content":"**适用**：对可合并的同类比较与结局估计综合效应。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [成对 Meta 分析专题](meta-analysis-skill.md)。\n\n**报告要点**：效应方向、模型、τ²、区间、研究设计与异质性解释。\n\n**常见错误**：混合不相容效应尺度，按I²阈值机械选择模型。","source":"guides/meta-analysis-skill.html"}},{"id":"methods:H3","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"网状 Meta 分析 NMA","summary":"**适用**：在连通且可比的研究网络中比较三个及以上干预。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [网状 Meta 分析 NMA专题](network-meta-analysis-skill.md)。\n\n**报告要点**：节点、网络、多臂相关、直接间接证据、诊断与CINeMA。\n\n**常见错误**：缺少传递性论证，仅凭排序宣布最佳干预。","url":"methods.html","source":"data/methods.json","details":{"id":"H3","name":"网状 Meta 分析 NMA","category":"H","content":"**适用**：在连通且可比的研究网络中比较三个及以上干预。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [网状 Meta 分析 NMA专题](network-meta-analysis-skill.md)。\n\n**报告要点**：节点、网络、多臂相关、直接间接证据、诊断与CINeMA。\n\n**常见错误**：缺少传递性论证，仅凭排序宣布最佳干预。","source":"guides/network-meta-analysis-skill.html"}},{"id":"methods:H4","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"meta-meta / 伞状综述","summary":"**适用**：以系统综述为单位绘制证据版图并解释结论差异。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [meta-meta / 伞状综述专题](umbrella-review-skill.md)。\n\n**报告要点**：综述家族、研究重叠矩阵、CCA、方法质量与重叠处理。\n\n**常见错误**：忽略原始研究重复，直接将多个Meta合并效应再次独立加权。","url":"methods.html","source":"data/methods.json","details":{"id":"H4","name":"meta-meta / 伞状综述","category":"H","content":"**适用**：以系统综述为单位绘制证据版图并解释结论差异。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [meta-meta / 伞状综述专题](umbrella-review-skill.md)。\n\n**报告要点**：综述家族、研究重叠矩阵、CCA、方法质量与重叠处理。\n\n**常见错误**：忽略原始研究重复，直接将多个Meta合并效应再次独立加权。","source":"guides/umbrella-review-skill.html"}},{"id":"methods:H5","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"范围综述 Scoping review","summary":"**适用**：梳理概念、研究类型、测量方法和证据缺口。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [范围综述 Scoping review专题](research-workflow-skill.md)。\n\n**报告要点**：问题框架、检索范围、筛选、资料制图与PRISMA-ScR。\n\n**常见错误**：把非系统的文献罗列称范围综述，或据研究数量断言干预有效。","url":"methods.html","source":"data/methods.json","details":{"id":"H5","name":"范围综述 Scoping review","category":"H","content":"**适用**：梳理概念、研究类型、测量方法和证据缺口。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [范围综述 Scoping review专题](research-workflow-skill.md)。\n\n**报告要点**：问题框架、检索范围、筛选、资料制图与PRISMA-ScR。\n\n**常见错误**：把非系统的文献罗列称范围综述，或据研究数量断言干预有效。","source":"guides/research-workflow-skill.html"}},{"id":"methods:I1","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"蒙特卡洛方法学模拟","summary":"**适用**：在已知真值下评估估计方法的偏倚、覆盖与效能。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [蒙特卡洛方法学模拟专题](monte-carlo-simulation-skill.md)。\n\n**报告要点**：ADEMP、场景、种子、重复次数依据、性能及MC误差。\n\n**常见错误**：不报告MCSE与失败率，或只选择对方法有利的数据生成机制。","url":"methods.html","source":"data/methods.json","details":{"id":"I1","name":"蒙特卡洛方法学模拟","category":"I","content":"**适用**：在已知真值下评估估计方法的偏倚、覆盖与效能。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [蒙特卡洛方法学模拟专题](monte-carlo-simulation-skill.md)。\n\n**报告要点**：ADEMP、场景、种子、重复次数依据、性能及MC误差。\n\n**常见错误**：不报告MCSE与失败率，或只选择对方法有利的数据生成机制。","source":"guides/monte-carlo-simulation-skill.html"}},{"id":"methods:I2","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"概率敏感性分析 PSA","summary":"**适用**：传播模型参数不确定性，估计成本效果决策的不确定性。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [概率敏感性分析 PSA专题](monte-carlo-simulation-skill.md)。\n\n**报告要点**：联合分布、参数依据、相关性、阈值、INMB与CEAC。\n\n**常见错误**：破坏参数相关性，把成本效果概率当临床有效概率。","url":"methods.html","source":"data/methods.json","details":{"id":"I2","name":"概率敏感性分析 PSA","category":"I","content":"**适用**：传播模型参数不确定性，估计成本效果决策的不确定性。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [概率敏感性分析 PSA专题](monte-carlo-simulation-skill.md)。\n\n**报告要点**：联合分布、参数依据、相关性、阈值、INMB与CEAC。\n\n**常见错误**：破坏参数相关性，把成本效果概率当临床有效概率。","source":"guides/monte-carlo-simulation-skill.html"}},{"id":"methods:I3","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"个体微观模拟","summary":"**适用**：模拟具有异质风险和历史依赖的个体路径。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [个体微观模拟专题](microsimulation-studio.md)。\n\n**报告要点**：个体属性、事件风险、路径记忆、校准、验证与随机数。\n\n**常见错误**：混淆个体随机变异与参数不确定性，忽略模拟噪声收敛。\n\n**实现**：base R四状态教学脚本已运行并通过29项检查，Stata未执行；见完整技能与支持脚本。","url":"methods.html","source":"data/methods.json","details":{"id":"I3","name":"个体微观模拟","category":"I","content":"**适用**：模拟具有异质风险和历史依赖的个体路径。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [个体微观模拟专题](microsimulation-studio.md)。\n\n**报告要点**：个体属性、事件风险、路径记忆、校准、验证与随机数。\n\n**常见错误**：混淆个体随机变异与参数不确定性，忽略模拟噪声收敛。\n\n**实现**：base R四状态教学脚本已运行并通过29项检查，Stata未执行；见完整技能与支持脚本。","source":"guides/microsimulation-studio.html"}},{"id":"methods:I4","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"离散事件模拟 DES","summary":"**适用**：分析到达、排队、床位与转诊等资源受限系统。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [离散事件模拟 DES专题](simulation-models-skill.md)。\n\n**报告要点**：实体、事件、队列纪律、分布、容量、重复与验证。\n\n**常见错误**：忽略到达率、容量、暖机期或重复运行，仅展示单次输出。","url":"methods.html","source":"data/methods.json","details":{"id":"I4","name":"离散事件模拟 DES","category":"I","content":"**适用**：分析到达、排队、床位与转诊等资源受限系统。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [离散事件模拟 DES专题](simulation-models-skill.md)。\n\n**报告要点**：实体、事件、队列纪律、分布、容量、重复与验证。\n\n**常见错误**：忽略到达率、容量、暖机期或重复运行，仅展示单次输出。","source":"guides/simulation-models-skill.html"}},{"id":"methods:I5","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"系统动力学 SD","summary":"**适用**：刻画存量、流量、反馈与延迟驱动的系统变化。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [系统动力学 SD专题](simulation-models-skill.md)。\n\n**报告要点**：边界、存量流量方程、延迟、参数、校准与情景。\n\n**常见错误**：仅画因果回路图便声称完成定量模拟，或忽视量纲一致。","url":"methods.html","source":"data/methods.json","details":{"id":"I5","name":"系统动力学 SD","category":"I","content":"**适用**：刻画存量、流量、反馈与延迟驱动的系统变化。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [系统动力学 SD专题](simulation-models-skill.md)。\n\n**报告要点**：边界、存量流量方程、延迟、参数、校准与情景。\n\n**常见错误**：仅画因果回路图便声称完成定量模拟，或忽视量纲一致。","source":"guides/simulation-models-skill.html"}},{"id":"methods:I6","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"多主体模型 ABM","summary":"**适用**：通过异质主体互动和适应解释系统涌现。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [多主体模型 ABM专题](simulation-models-skill.md)。\n\n**报告要点**：主体、环境、决策与互动、调度、参数来源及验证。\n\n**常见错误**：把AI助手称为仿真主体，或用无依据行为规则预设政策效果。","url":"methods.html","source":"data/methods.json","details":{"id":"I6","name":"多主体模型 ABM","category":"I","content":"**适用**：通过异质主体互动和适应解释系统涌现。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [多主体模型 ABM专题](simulation-models-skill.md)。\n\n**报告要点**：主体、环境、决策与互动、调度、参数来源及验证。\n\n**常见错误**：把AI助手称为仿真主体，或用无依据行为规则预设政策效果。","source":"guides/simulation-models-skill.html"}},{"id":"methods:J1","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"三维政策框架与 PMC","summary":"**适用**：按可复核codebook整理政策工具、对象、目标和内容覆盖。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [三维政策框架与 PMC专题](policy-text-analysis-skill.md)。\n\n**报告要点**：政策范围、单位、指标依据、编码规则、权重与原文定位。\n\n**常见错误**：把文件得分直接解释为政策执行或健康改善。","url":"methods.html","source":"data/methods.json","details":{"id":"J1","name":"三维政策框架与 PMC","category":"J","content":"**适用**：按可复核codebook整理政策工具、对象、目标和内容覆盖。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [三维政策框架与 PMC专题](policy-text-analysis-skill.md)。\n\n**报告要点**：政策范围、单位、指标依据、编码规则、权重与原文定位。\n\n**常见错误**：把文件得分直接解释为政策执行或健康改善。","source":"guides/policy-text-analysis-skill.html"}},{"id":"methods:J2","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"LDA / BERTopic 主题分析","summary":"**适用**：探索政策语料中的主题及其变化。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [LDA / BERTopic 主题分析专题](policy-text-analysis-skill.md)。\n\n**报告要点**：语料、预处理、词典/嵌入、参数、主题稳定性及代表文本。\n\n**常见错误**：只看主题词命名，不回读原文或检验参数/主题数稳定性。","url":"methods.html","source":"data/methods.json","details":{"id":"J2","name":"LDA / BERTopic 主题分析","category":"J","content":"**适用**：探索政策语料中的主题及其变化。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [LDA / BERTopic 主题分析专题](policy-text-analysis-skill.md)。\n\n**报告要点**：语料、预处理、词典/嵌入、参数、主题稳定性及代表文本。\n\n**常见错误**：只看主题词命名，不回读原文或检验参数/主题数稳定性。","source":"guides/policy-text-analysis-skill.html"}},{"id":"methods:J3","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"LLM辅助编码与关系抽取","summary":"**适用**：把政策或病历条款转成有证据定位的结构化变量。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [LLM辅助编码与关系抽取专题](policy-text-analysis-skill.md)。\n\n**报告要点**：codebook、人工仲裁、模型/prompt版本、测试、错误与修订率。\n\n**常见错误**：无人工金标准、无独立测试，用模型自报信心代替评价。","url":"methods.html","source":"data/methods.json","details":{"id":"J3","name":"LLM辅助编码与关系抽取","category":"J","content":"**适用**：把政策或病历条款转成有证据定位的结构化变量。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [LLM辅助编码与关系抽取专题](policy-text-analysis-skill.md)。\n\n**报告要点**：codebook、人工仲裁、模型/prompt版本、测试、错误与修订率。\n\n**常见错误**：无人工金标准、无独立测试，用模型自报信心代替评价。","source":"guides/policy-text-analysis-skill.html"}},{"id":"methods:J4","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"医疗大模型配对评价","summary":"**适用**：比较模型与人工的准确性、安全性、时间和实际决策表现。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [医疗大模型配对评价专题](medical-ai-evaluation-skill.md)。\n\n**报告要点**：病例与参考标准、盲评、配对/聚类、区间、失败及亚组。\n\n**常见错误**：把同一病例多轮回答当独立样本，忽略拒答或严重错误。","url":"methods.html","source":"data/methods.json","details":{"id":"J4","name":"医疗大模型配对评价","category":"J","content":"**适用**：比较模型与人工的准确性、安全性、时间和实际决策表现。\n\n**逻辑与操作**：明确研究单位与目标，按专题完成设计、估计与验证。详细流程和示例见 [医疗大模型配对评价专题](medical-ai-evaluation-skill.md)。\n\n**报告要点**：病例与参考标准、盲评、配对/聚类、区间、失败及亚组。\n\n**常见错误**：把同一病例多轮回答当独立样本，忽略拒答或严重错误。","source":"guides/medical-ai-evaluation-skill.html"}},{"id":"methods:B13","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"广义线性模型 GLM","summary":"**适用**：依据结局支持集与均值-方差关系选择分布和链接。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [广义线性模型 GLM专题](econometric-modeling-skill.md)。\n\n**报告要点**：分布、链接、支持集、方差、诊断、边际预测与区间。\n\n**常见错误**：对含零费用直接用Gamma，或把链接尺度系数当原尺度差异。","url":"methods.html","source":"data/methods.json","details":{"id":"B13","name":"广义线性模型 GLM","category":"B","content":"**适用**：依据结局支持集与均值-方差关系选择分布和链接。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [广义线性模型 GLM专题](econometric-modeling-skill.md)。\n\n**报告要点**：分布、链接、支持集、方差、诊断、边际预测与区间。\n\n**常见错误**：对含零费用直接用Gamma，或把链接尺度系数当原尺度差异。","source":"guides/econometric-modeling-skill.html"}},{"id":"methods:B14","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"费用两部模型","summary":"**适用**：分别估计发生费用概率与正费用均值，再合成人群费用。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [费用两部模型专题](econometric-modeling-skill.md)。\n\n**报告要点**：两部分样本/模型、合成预测、区间及因果假设。\n\n**常见错误**：只报告正费用模型，忽略使用概率和整个估计流程的不确定性。","url":"methods.html","source":"data/methods.json","details":{"id":"B14","name":"费用两部模型","category":"B","content":"**适用**：分别估计发生费用概率与正费用均值，再合成人群费用。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [费用两部模型专题](econometric-modeling-skill.md)。\n\n**报告要点**：两部分样本/模型、合成预测、区间及因果假设。\n\n**常见错误**：只报告正费用模型，忽略使用概率和整个估计流程的不确定性。","source":"guides/econometric-modeling-skill.html"}},{"id":"methods:B15","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"面板 FE / RE / CRE","summary":"**适用**：区分组内变化、组间差异及个体效应与协变量的关系。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [面板 FE / RE / CRE专题](econometric-modeling-skill.md)。\n\n**报告要点**：单位/时间效应、组内变异、假设、聚类层级与动态偏误。\n\n**常见错误**：把FE当作全部内生性的解决方案，或仅凭Hausman选择模型。","url":"methods.html","source":"data/methods.json","details":{"id":"B15","name":"面板 FE / RE / CRE","category":"B","content":"**适用**：区分组内变化、组间差异及个体效应与协变量的关系。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [面板 FE / RE / CRE专题](econometric-modeling-skill.md)。\n\n**报告要点**：单位/时间效应、组内变异、假设、聚类层级与动态偏误。\n\n**常见错误**：把FE当作全部内生性的解决方案，或仅凭Hausman选择模型。","source":"guides/econometric-modeling-skill.html"}},{"id":"methods:B16","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"分位数回归","summary":"**适用**：研究条件分布不同位置的费用或健康结局关联。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [分位数回归专题](econometric-modeling-skill.md)。\n\n**报告要点**：条件/无条件目标、分位点、区间和模型支持。\n\n**常见错误**：把条件分位数系数直接解释为同一群个体的因果效应。","url":"methods.html","source":"data/methods.json","details":{"id":"B16","name":"分位数回归","category":"B","content":"**适用**：研究条件分布不同位置的费用或健康结局关联。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [分位数回归专题](econometric-modeling-skill.md)。\n\n**报告要点**：条件/无条件目标、分位点、区间和模型支持。\n\n**常见错误**：把条件分位数系数直接解释为同一群个体的因果效应。","source":"guides/econometric-modeling-skill.html"}},{"id":"methods:B17","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"零膨胀与 hurdle 计数模型","summary":"**适用**：依据零产生机制区分结构性零与计数过程。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [零膨胀与 hurdle 计数模型专题](econometric-modeling-skill.md)。\n\n**报告要点**：零机制、两部分模型、过度离散、拟合和边际效应。\n\n**常见错误**：仅因零很多就选择零膨胀，不核对两过程机制与可识别性。","url":"methods.html","source":"data/methods.json","details":{"id":"B17","name":"零膨胀与 hurdle 计数模型","category":"B","content":"**适用**：依据零产生机制区分结构性零与计数过程。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [零膨胀与 hurdle 计数模型专题](econometric-modeling-skill.md)。\n\n**报告要点**：零机制、两部分模型、过度离散、拟合和边际效应。\n\n**常见错误**：仅因零很多就选择零膨胀，不核对两过程机制与可识别性。","source":"guides/econometric-modeling-skill.html"}},{"id":"methods:B18","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"空间计量与溢出","summary":"**适用**：分析地区间依赖并在合适模型中解释直接/间接效应。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [空间计量与溢出专题](econometric-modeling-skill.md)。\n\n**报告要点**：空间权重、尺度、依赖结构、直接/间接效应与敏感性。\n\n**常见错误**：把空间自相关直接当作政策溢出因果证据。","url":"methods.html","source":"data/methods.json","details":{"id":"B18","name":"空间计量与溢出","category":"B","content":"**适用**：分析地区间依赖并在合适模型中解释直接/间接效应。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [空间计量与溢出专题](econometric-modeling-skill.md)。\n\n**报告要点**：空间权重、尺度、依赖结构、直接/间接效应与敏感性。\n\n**常见错误**：把空间自相关直接当作政策溢出因果证据。","source":"guides/econometric-modeling-skill.html"}},{"id":"methods:D15","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"交错 DID / 分批处理","summary":"**适用**：按首次政策批次估计ATT(g,t)并聚合动态与总体效应。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [交错 DID / 分批处理专题](staggered-did-skill.md)。\n\n**报告要点**：处理批次、对照、预期、支持、聚合权重、置信带与聚类。\n\n**常见错误**：异质效应下直接套传统TWFE，或把已处理单位当合格对照。","url":"methods.html","source":"data/methods.json","details":{"id":"D15","name":"交错 DID / 分批处理","category":"D","content":"**适用**：按首次政策批次估计ATT(g,t)并聚合动态与总体效应。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [交错 DID / 分批处理专题](staggered-did-skill.md)。\n\n**报告要点**：处理批次、对照、预期、支持、聚合权重、置信带与聚类。\n\n**常见错误**：异质效应下直接套传统TWFE，或把已处理单位当合格对照。","source":"guides/staggered-did-skill.html"}},{"id":"methods:D16","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"熵平衡与重叠权重","summary":"**适用**：通过矩约束或强调重叠构建可比样本，明确新的目标总体。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [熵平衡与重叠权重专题](propensity-score-skill.md)。\n\n**报告要点**：目标、约束/权重公式、重叠、有效样本量与推断。\n\n**常见错误**：只看平衡不看权重集中，或把ATO结果仍称原总体ATE。","url":"methods.html","source":"data/methods.json","details":{"id":"D16","name":"熵平衡与重叠权重","category":"D","content":"**适用**：通过矩约束或强调重叠构建可比样本，明确新的目标总体。\n\n**逻辑与操作**：先定义目标与数据单位，再核对假设、执行诊断并解释结果。完整步骤、代码和边界见 [熵平衡与重叠权重专题](propensity-score-skill.md)。\n\n**报告要点**：目标、约束/权重公式、重叠、有效样本量与推断。\n\n**常见错误**：只看平衡不看权重集中，或把ATO结果仍称原总体ATE。","source":"guides/propensity-score-skill.html"}},{"id":"methods:A10","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"模拟与标准化病人 SP / USP","summary":"**适用**：用训练过的扮演者在统一情境下测量服务过程，区分教学考核与未预先告知的质量研究。\n\n**准备**：先明确场景、剧本、评分依据、伦理安全、机构抽样、重复访问与识别记录。\n\n**代码**与完整说明：\n\n---\nname: standardized-patient\ndescription: 模拟/标准化病人SP与未预先告知标准化病人USP：教学考核、医疗服务质量测量、剧本与评分表、训练一致性、伦理安全、抽样与机构聚类分析，配R/Stata教学代码。\n---\n\n# 模拟与标准化病人：把服务过程变成可比较的证据\n\n标准化病人（standardized patient, SP）通常由经过训练的人按剧本稳定呈现病史、行为和反应；模拟病人（simulated patient）在文献中有时与之互用。这里强调可重复呈现和测量一致性。它不是 Markov 微观模拟，也不是让大模型生成“真实患者”。\n\n## 1. 先选研究用途\n\n| 用途 | 主要问题 | 能测量什么 | 不能直接声称什么 |\n|---|---|---|---|\n| 教学、沟通训练与OSCE | 学员在统一情境下如何表现 | 问诊、沟通、决策和程序表现；按站点评分 | 考场表现等于长期真实临床表现 |\n| 告知式服务评估 | 机构在知道评估存在时怎样服务 | 统一情境的质量与流程 | 不存在观察反应 |\n| 未预先告知标准化病人 USP | 常规服务中的实际行为如何 | 适当处置、询问检查、转诊、费用及沟通 | 覆盖所有疾病","url":"methods.html","source":"data/methods.json","details":{"id":"A10","name":"模拟与标准化病人 SP / USP","category":"A","content":"**适用**：用训练过的扮演者在统一情境下测量服务过程，区分教学考核与未预先告知的质量研究。\n\n**准备**：先明确场景、剧本、评分依据、伦理安全、机构抽样、重复访问与识别记录。\n\n**代码**与完整说明：\n\n---\nname: standardized-patient\ndescription: 模拟/标准化病人SP与未预先告知标准化病人USP：教学考核、医疗服务质量测量、剧本与评分表、训练一致性、伦理安全、抽样与机构聚类分析，配R/Stata教学代码。\n---\n\n# 模拟与标准化病人：把服务过程变成可比较的证据\n\n标准化病人（standardized patient, SP）通常由经过训练的人按剧本稳定呈现病史、行为和反应；模拟病人（simulated patient）在文献中有时与之互用。这里强调可重复呈现和测量一致性。它不是 Markov 微观模拟，也不是让大模型生成“真实患者”。\n\n## 1. 先选研究用途\n\n| 用途 | 主要问题 | 能测量什么 | 不能直接声称什么 |\n|---|---|---|---|\n| 教学、沟通训练与OSCE | 学员在统一情境下如何表现 | 问诊、沟通、决策和程序表现；按站点评分 | 考场表现等于长期真实临床表现 |\n| 告知式服务评估 | 机构在知道评估存在时怎样服务 | 统一情境的质量与流程 | 不存在观察反应 |\n| 未预先告知标准化病人 USP | 常规服务中的实际行为如何 | 适当处置、询问检查、转诊、费用及沟通 | 覆盖所有疾病、重症体征和后续健康结局 |\n| 标准化病例/临床情境问卷 | 医务人员“知道该怎么做” | 知识或陈述决策 | 与USP测得的实际行为完全相同 |\n\nASPE 标准主要面向人类模拟教学与评估；USP 质量研究还需研究方案、抽样、识别与安全管理。先确定对象、场景、主要结局、比较与时间窗，再选是否适合 SP。[S1–S3]\n\n## 2. 剧本与评分表怎样制作\n\n选择可安全、可信地呈现且与研究问题相关的场景。明确开场陈述、只有被问到才提供的信息、允许的标准回答、情绪与行为、不可模拟的体征，以及何时停止访问。临床处置评分应由相应专业人员依据适用指南制定，注明指南日期与评分规则；本站不提供自行施行检查或治疗的脚本。\n\n评分表逐项定义“应询问/应检查/应处置/不适当操作”、信息来源、缺失、不适用和分母。主要结局应预先规定，例如符合预定处置标准的访问比例；不能看完结果才挑最显著条目。复杂综合分数需说明权重和测量依据，不能自动视为已验证量表。\n\n先做小规模可行性试演：对同一剧本重复训练，检查扮演者间与扮演者内一致性、评分者一致性及回忆遗漏。二分类可考虑一致率与κ，连续分数按设计考虑ICC；同时查看分布，因为高一致率与低κ可能并存。\n\n## 3. 抽样、分配与访视安排\n\n1. 写清机构抽样框、纳排、地区与服务类型。便利样本不代表全国质量。\n2. 若评价干预，明确随机化或政策分配单位；机构层分配不能把每次访问当作独立随机单位。\n3. 记录医生、机构、扮演者、剧本、访问时间与重复访问关系。角色和案例尽量在比较组平衡；同一医生重复访问及跨机构SP可形成多层或交叉结构。\n4. 事先做功效规划：主要结局、预期差异、机构数、每机构访问数、ICC、病例构成与不可完成比例。增加同一机构访问数不能无限替代增加机构数。\n5. 在许可范围内尽快填写结构化访问记录；若使用音频等材料，必须与批准方案、访问授权和保存期限一致。\n\n## 4. 伦理与安全是设计条件\n\n未预先告知不等于不需伦理审查。医务人员同意的取得方式或豁免、事后说明、隐私与声誉保护、对正常服务的干扰，须由相应伦理审查与机构安排明确。授权或豁免只能由适用程序认定，不能由本工具自动决定。[S1–S3]\n\n预设不得接受的侵入性检查、真实用药与危险情境及退出流程；出现超出模拟范围的情况应中止并按批准的安全流程处理。保护SP的身体与情绪安全，安排训练、支持和复盘。公开结果以适当聚合形式呈现，不发布可识别的个人访问记录。\n\n## 5. 数据字典与质量核查\n\n[下载SP字段模板](../data/standardized-patient-data-dictionary.csv)。模板是空白字段说明，不含患者或医务人员记录。\n\n核心字段：匿名机构/医生/访问/扮演者/案例ID，组别与实际分配，访问时间，各评分项目及分母，主要处置结局，费用，是否被识别，剧本偏离，不完整访问及缺失原因。\n\n保留访视失败和识别记录。识别可能与机构、剧本或干预有关；仅删除“被识别”样本可能引入选择偏倚。主分析与敏感性分析的对象、分母和排除标准均提前规定，并报告各阶段数量。[S2]\n\n## 6. 分析如何排布\n\n- 先描述抽样、完成率、案例分布、识别比例和评分缺失。\n- 二元结局报告比例与区间，并明确风险差、风险比或OR；不把OR直接解释成风险比。\n- 机构随机研究按分配单位处理相关性。脚本/演员固定或随机、平衡程度与目标总体决定采用机构汇总、多层或交叉模型。\n- 横断面USP比较主要支持描述与关联；只有满足随机或准实验识别条件才能作相应因果解释。\n- 观察真实患者资料与SP资料回答不同问题：病例标准化有利于比较，却不代表实际患者疾病谱与长期结局。[S2–S3]\n\n下方例子为36个随机分组机构，每机构3个案例×4位扮演者，共432次模拟访问。案例和扮演者在机构间完全平衡，采用等机构权重的组间比较；推断限于这组案例和扮演者。R脚本已在R 4.6.1运行；Stata为未执行教学模板。模拟结果不保证显著，也不是任何真实政策效果。\n\n## 7. 与实施科学怎样联用\n\n例如评估基层指南落实：同一临床服务对应两种实施支持策略，机构分组，USP评估处置过程质量，另用实施数据衡量采纳、忠实度、成本与维持。SP评分属于服务过程质量，不能替代全部实施结局或患者健康结局。明确盲法、识别风险、策略污染及机构聚类，再判断是否适合 hybrid 设计。[实施科学路线](implementation-science-skill.md)\n\n## 8. 最终交付清单\n\n研究问题与估计目标、剧本版本、评分依据、抽样与功效方案、伦理与安全文件、训练及一致性记录、字段字典、访问流程图、分析计划、可复现代码和限制说明。\n\n可复制提问：**“我想用标准化病人比较基层服务质量。请帮我定义主要结局、剧本、抽样、机构相关性、识别记录和R分析方案。”**\n\n## 核对来源\n\n- S1. Lewis KL 等，2017，ASPE Standards of Best Practice。[官方全文](https://www.aspeducators.org/assets/docs/ASPE%20SOBP%202.7.20.pdf) · [DOI](https://doi.org/10.1186/s41077-017-0043-4)。使用范围：训练、安全、情境与质量保证。\n- S2. King JJC 等，2019，How to do (or not to do)… using the standardized patient method to measure clinical quality of care in LMIC health facilities。[PubMed](https://pubmed.ncbi.nlm.nih.gov/31424494/) · [DOI](https://doi.org/10.1093/heapol/czz078)。使用范围：USP方法与测量边界。\n- S3. Assessing the quality of primary healthcare in seven Chinese provinces with unannounced standardised patients: protocol of a cross-sectional survey，2019。[BMJ Open](https://bmjopen.bmj.com/content/9/2/e023997)。这是研究方案，不能当作研究结果。\n\n题名、DOI、期刊与作者核对日期：2026-10-24。[来源登记](../data/sp-implementation-sources.json)。本站为原创方法导读，不复制论文全文。\n\n<!-- BEGIN TEACHING CODE -->\n\n## 教学代码：可以单独下载，也可展开复制\n\n### 机构层级分析 · R\n\n[下载脚本](scripts/standardized_patient_demo.R)\n\n```r\n# Original teaching simulation. No patient data and no clinical recommendations.\n# Balanced cases/actors at every clinic; inference is conditional on this set.\nset.seed(20261024)\nclinics <- data.frame(clinic_id=1:36, arm=sample(rep(0:1, each=18)))\nvisits <- merge(clinics, expand.grid(case_id=1:3, actor_id=1:4))\nclinic_effect <- rnorm(36, 0, 0.45)\np <- plogis(-0.55 + 0.5*visits$arm + clinic_effect[visits$clinic_id] +\n             c(-0.2,0,0.2)[visits$case_id])\nvisits$correct <- rbinom(nrow(visits), 1, p)\nvisits$detected <- rbinom(nrow(visits), 1, 0.07)\nstopifnot(nrow(visits)==432, all(table(visits$clinic_id)==12))\nclinic_scores <- aggregate(correct ~ clinic_id + arm, visits, mean)\n# Clinic allocation means visits are not independent trial units.\ncomparison <- t.test(clinic_scores$correct[clinic_scores$arm==1],\n                     clinic_scores$correct[clinic_scores$arm==0])\nrd <- unname(diff(rev(comparison$estimate)))\nstopifnot(abs(rd - (mean(clinic_scores$correct[clinic_scores$arm==1]) -\n                   mean(clinic_scores$correct[clinic_scores$arm==0]))) < 1e-12)\nprint(data.frame(estimand='Equal-clinic mean risk difference (arm 1 minus 0)',\n                 estimate=rd, lower=comparison$conf.int[1], upper=comparison$conf.int[2]))\n# Detection is reported, not silently deleted; exclusions may select on treatment.\nprint(aggregate(detected ~ arm, visits, mean))\nprint(aggregate(correct ~ arm + case_id, visits, mean))\n# For observational samples these contrasts are descriptive, not causal effects.\n# Unbalanced scripts/actors, few clusters, or cross-classified random actors need\n# a prespecified adjustment model and suitable uncertainty estimation.\nstopifnot(all(visits$correct %in% 0:1), all(is.finite(comparison$conf.int)))\ncat('PASS: 432 visits; 36 randomized clinics; balanced scripts; finite clinic-level interval\\n')\n```\n\n### 机构层级分析 · STATA\n\n[下载脚本](scripts/standardized_patient_demo.do)\n\n```stata\n* Original teaching simulation; not run in Stata in this release.\nclear all\nset seed 20261024\nset obs 36\ngen clinic_id = _n\ngen allocation_order = runiform()\nsort allocation_order\ngen arm = (_n > 18)\ngen clinic_effect = rnormal(0, .45)\nexpand 12\nbysort clinic_id: gen visit = _n\ngen case_id = mod(visit-1,3)+1\ngen actor_id = ceil(visit/3)\ngen p = invlogit(-.55 + .5*arm + clinic_effect + .2*(case_id-2))\ngen correct = (runiform() < p)\ngen detected = (runiform() < .07)\nassert inlist(correct,0,1)\nassert _N == 432\ntabstat detected, by(arm) stat(mean n)\n* Equal clinic weighting; case and actor assignments are balanced by construction.\npreserve\ncollapse (mean) correct, by(clinic_id arm)\nassert _N == 36\nregress correct i.arm, vce(robust)\nlincom 1.arm\nrestore\n* The individual-visit model is an alternative, not an independent replication.\nregress correct i.arm i.case_id i.actor_id, vce(cluster clinic_id)\nlincom 1.arm\n* Few clusters, unbalanced case mixes and observational allocation require\n* separate design-based decisions. Do not delete detected visits without a plan.\n```\n","source":"guides/standardized-patient-skill.html"}},{"id":"methods:F12","collection":"methods","collection_title":"方法完整说明","kind":"方法","title":"实施科学：从框架到研究设计","summary":"**适用**：区分服务干预与实施策略，把障碍、策略、实施结局、健康结局和持续使用连接到具体设计。\n\n**准备**：先明确服务证据、策略执行者、主要结局、层级、分母与时间窗；框架不能代替识别设计。\n\n**代码**与完整说明：\n\n---\nname: implementation-science\ndescription: 实施科学完整路线：临床/服务干预与实施策略分离，CFIR、RE-AIM、Proctor实施结局、ERIC、hybrid 1/2/3、混合方法、分母与层级、R/Stata教学分析和StaRI报告。\n---\n\n# 实施科学：让有证据的服务在真实场景中被采用和持续使用\n\n先区分**要落实的服务/干预**（innovation/intervention）与**帮助它落实的实施策略**（strategy）。例如，“规范的慢病随访服务”与“为实施该服务提供培训、反馈和促进支持”是两条不同的线。研究应同时明确谁采用、如何交付、为何受阻、产生何种结果。[I1–I4]\n\n## 1. 把研究问题写成可执行句子\n\n在【场景与机构】中，为【人群】落实【已有证据的服务】时，针对【已识别障碍】，由【执行者】实施【策略及剂量】，相对于【比较条件】，在【时间窗】内改善【主要实施结局】，并观察【服务或健康结局】。\n\n先评估服务本身的证据与可实施性，不把“有效性未知”隐藏成单纯推广问题。研究不是将多个框架名词拼接；框架服务于具体问题、构念和数据。\n\n## 2. 框架按任务分工\n\n| 任务 | 候选工具 ","url":"methods.html","source":"data/methods.json","details":{"id":"F12","name":"实施科学：从框架到研究设计","category":"F","content":"**适用**：区分服务干预与实施策略，把障碍、策略、实施结局、健康结局和持续使用连接到具体设计。\n\n**准备**：先明确服务证据、策略执行者、主要结局、层级、分母与时间窗；框架不能代替识别设计。\n\n**代码**与完整说明：\n\n---\nname: implementation-science\ndescription: 实施科学完整路线：临床/服务干预与实施策略分离，CFIR、RE-AIM、Proctor实施结局、ERIC、hybrid 1/2/3、混合方法、分母与层级、R/Stata教学分析和StaRI报告。\n---\n\n# 实施科学：让有证据的服务在真实场景中被采用和持续使用\n\n先区分**要落实的服务/干预**（innovation/intervention）与**帮助它落实的实施策略**（strategy）。例如，“规范的慢病随访服务”与“为实施该服务提供培训、反馈和促进支持”是两条不同的线。研究应同时明确谁采用、如何交付、为何受阻、产生何种结果。[I1–I4]\n\n## 1. 把研究问题写成可执行句子\n\n在【场景与机构】中，为【人群】落实【已有证据的服务】时，针对【已识别障碍】，由【执行者】实施【策略及剂量】，相对于【比较条件】，在【时间窗】内改善【主要实施结局】，并观察【服务或健康结局】。\n\n先评估服务本身的证据与可实施性，不把“有效性未知”隐藏成单纯推广问题。研究不是将多个框架名词拼接；框架服务于具体问题、构念和数据。\n\n## 2. 框架按任务分工\n\n| 任务 | 候选工具 | 用法与边界 |\n|---|---|---|\n| 解释障碍与促进因素 | 更新版CFIR（2022）；2025使用指南 | 按问题选构念，建立访谈/观察与分析对应；不是全部条目强制打分 |\n| 规划和评价真实世界覆盖 | RE-AIM | 分开触达、效果、采纳、实施、维持；逐维指定层级、分母与时间 |\n| 定义实施成效 | Proctor实施结局 | 区分实施、服务与健康结局；不把患者满意度当所有实施成功 |\n| 具体说明实施支持措施 | ERIC策略术语 | 明确谁对谁做什么、何时、多频繁及预期机制；不是看到障碍就机械配策略 |\n| 组织结果报告 | StaRI | 服务干预与实施策略分两条线报告；另按具体研究设计核对报告规范 |\n\nCFIR常用于解释决定因素，RE-AIM常用于规划与评价；两者可互补，但不是同一套量表。构念名称本身不会产生因果识别。[I1–I3、I6–I8]\n\n## 3. 结局、分母与时间窗\n\nProctor等区分可接受性、采纳、适切性、可行性、忠实度、实施成本、渗透/整合和可持续性。分别定义测量对象与操作化指标；临床效果和服务系统结果单独记录。[I4]\n\n| 指标例子 | 分子 | 分母 | 注意 |\n|---|---|---|---|\n| 触达 | 实际接受服务的符合条件者 | 目标范围内全部符合条件者 | 说明招募、拒绝和排除；还要看代表性 |\n| 机构采纳 | 开始采用服务的机构 | 纳入目标的符合条件机构 | 机构层和工作人员层不要混算 |\n| 忠实度 | 按规定完成核心环节的服务次数 | 应执行这些环节的服务次数 | 区分适应性调整与偏离；不适用不是未完成 |\n| 维持 | 规定随访时点仍运行的机构 | 按方案定义的初始或已采纳机构 | 明确时点和分母，不能临时替换 |\n| 单位实施成本 | 明确视角下的实施资源成本 | 对应触达者/采纳机构数 | 分开服务成本与实施支持成本，注明币种和价格年 |\n\n分母为0时显示缺失并说明原因，不能直接记为0%。机构平均比例与“总分子/总分母”是不同估计目标；两者不能混报。可接受性等潜变量如使用量表，需核对授权、译本、测量性质及本场景适用性，不能把自编问题标成已验证量表。\n\n## 4. 选择设计与hybrid类型\n\n| 类型 | 主要关注 | 同时关注 |\n|---|---|---|\n| Hybrid 1 | 检验服务干预有效性 | 探索实施条件与障碍 |\n| Hybrid 2 | 服务干预与实施策略的双重研究目标 | 两类目标的样本量、结局和比较均须说明 |\n| Hybrid 3 | 检验实施策略对实施结局的作用 | 观察服务/健康效果 |\n\n类型描述研究目标的侧重，不能替代具体的随机、准实验或观察设计。根据已有服务证据、场景差异、污染和可行性选择个体/整群随机、分阶段推行或适当准实验；阶梯楔形设计仍要处理日历时间和机构相关性，不能等同简单前后比较。[I5]\n\n样本量以主要目标和分配层级计算，考虑ICC、机构大小差异、双重主要结局与缺失；不能把个体数当成独立机构数。\n\n## 5. 实施策略与机制表\n\n一行一个策略：执行者 → 具体行动 → 对象 → 频率/时长 → 对应障碍 → 预期机制 → 近端实施结局 → 远端服务/健康结局。区分计划剂量与实际执行，记录适应性修改的时间、理由和决定者。[I6]\n\n访谈可按选定CFIR构念设计，同时保留开放编码与反例。建立联合展示：构念、定性证据、量化指标、相符/不符、替代解释。不能用访谈提及次数直接估计人群比例，也不能仅凭显著路径判定机制成立。\n\n## 6. 数据与分析\n\n[下载实施研究字段模板](../data/implementation-science-data-dictionary.csv)。至少包括机构/工作人员/服务对象层级ID，策略分配，服务定义，实施阶段，时间，分母，采纳与执行记录，适应性修改，成本及相关群体特征。公开模板不含任何个体记录。\n\n下方教学例子把40个机构随机分配至两种实施策略，对同一种临床服务计算采纳、触达、执行忠实度和单位实施成本。主示例比较等机构权重的触达率；重复时间点需另建纵向模型。\n\n忠实度在实际接受服务者中计算，条件于一个分组后的变量；组间忠实度差不能自动解释为随机策略对所有符合条件者的因果效应。R脚本已运行并检查分母与缺失逻辑；Stata未执行。模拟数据只演示计算，不提供效果证据。\n\n## 7. 公平与标准化病人联用\n\n按预先定义的群体查看谁未被触达、谁承担更大负担、机构是否代表目标场景。平均改善不排除差距扩大。[公平性补充](implementation-equity-skill.md)\n\n[标准化病人](standardized-patient-skill.md)可提供统一情境的服务过程评价，帮助比较策略实施前后或不同组的实际处置；仍需单独收集实施策略执行、采纳和成本，不能把USP处置合格率直接改名为全部实施成功。\n\n## 8. 按StaRI交付\n\n分别报告服务干预、实施策略及其依据；交代情境、适应性修改、设计、比较、主要分母、过程、实施/健康结局和局限。交付研究问题表、框架—构念映射、逻辑模型、策略操作表、测量字典、混合方法联合展示、分析计划与报告核对表。[I7]\n\n可复制提问：**“我想提高基层指南采纳。请区分服务干预与实施策略，用CFIR选择障碍、RE-AIM定义分母，并比较hybrid 1/2/3的适用条件。”**\n\n## 核对来源\n\n- I1. Damschroder等，2022，更新版CFIR。[原文](https://link.springer.com/article/10.1186/s13012-022-01245-0)。\n- I2. CFIR User Guide，2025，五步使用指南。[原文](https://link.springer.com/article/10.1186/s13012-025-01450-7)。\n- I3. RE-AIM框架团队。[官方五维说明](https://re-aim.org/learn/what-is-re-aim/)。\n- I4. Proctor等，2011，实施结局概念区分。[原文](https://doi.org/10.1007/s10488-010-0319-7)。\n- I5. Curran等，2012，Effectiveness-implementation Hybrid Designs。[原文](https://pmc.ncbi.nlm.nih.gov/articles/PMC3731143/)。\n- I6. Powell等，2015，ERIC策略术语整理。[原文](https://link.springer.com/article/10.1186/s13012-015-0209-1)。\n- I7. Pinnock等，2017，StaRI。[EQUATOR登记与清单](https://www.equator-network.org/reporting-guidelines/stari-statement/)。\n- I8. [CFIR官方资源](https://cfirguide.org/)。\n\n核对日期：2026-10-24。[完整题名、DOI、作者及核验范围](../data/sp-implementation-sources.json)。本文为本站原创教学整理，未复制论文全文。\n\n<!-- BEGIN TEACHING CODE -->\n\n## 教学代码：可以单独下载，也可展开复制\n\n### 机构层级分析 · R\n\n[下载脚本](scripts/implementation_science_demo.R)\n\n```r\n# Original teaching simulation: same clinical service, two implementation strategies.\nset.seed(20261025)\nd <- data.frame(site_id=1:40, strategy=sample(rep(0:1,each=20)))\nd$eligible <- sample(50:100, 40, replace=TRUE)\nd$adopted <- rbinom(40, 1, plogis(-0.1 + 0.6*d$strategy))\nd$reached <- ifelse(d$adopted==1,\n                    rbinom(40, d$eligible, plogis(-0.3 + 0.3*d$strategy)), 0)\nd$completed <- rbinom(40, d$reached, 0.8)\nsafe_ratio <- function(num,den) ifelse(den>0,num/den,NA_real_)\nd$reach <- safe_ratio(d$reached,d$eligible)\nd$fidelity <- safe_ratio(d$completed,d$reached)\nd$implementation_cost <- 500 + 15*d$reached\nsummary_by_arm <- do.call(rbind,lapply(split(d,d$strategy), function(x)\n data.frame(strategy=x$strategy[1], sites=nrow(x),\n adoption=mean(x$adopted), reach_person_weighted=sum(x$reached)/sum(x$eligible),\n reach_site_weighted=mean(x$reach), fidelity_among_reached=safe_ratio(sum(x$completed),sum(x$reached)),\n cost_per_reached=safe_ratio(sum(x$implementation_cost),sum(x$reached)))))\nprint(summary_by_arm)\n# Primary example: strategy effect on equal-site reach, at one prespecified time.\nfit <- t.test(d$reach[d$strategy==1],d$reach[d$strategy==0])\nprint(data.frame(reach_difference=unname(fit$estimate[1]-fit$estimate[2]),\n                 lower=fit$conf.int[1],upper=fit$conf.int[2]))\n# Fidelity conditions on reach. Its between-arm contrast is not automatically\n# a causal strategy effect because reaching the service is post-allocation.\nstopifnot(all(d$completed<=d$reached),all(d$reached<=d$eligible),\n          is.na(safe_ratio(0,0)),all(is.na(d$fidelity[d$reached==0])),\n          all(is.finite(fit$conf.int)))\ncat('PASS: denominators, missing zero-denominator ratios, 40 randomized sites, finite interval\\n')\n```\n\n### 机构层级分析 · STATA\n\n[下载脚本](scripts/implementation_science_demo.do)\n\n```stata\n* Teaching template. Not run in Stata in this release.\nclear all\nset seed 20261025\nset obs 40\ngen site_id = _n\ngen allocation_order = runiform()\nsort allocation_order\ngen strategy = (_n > 20)\ngen eligible = 50 + floor(51*runiform())\ngen adopted = runiform() < invlogit(-.1+.6*strategy)\ngen reached = rbinomial(eligible, invlogit(-.3+.3*strategy)) if adopted==1\nreplace reached = 0 if adopted==0\ngen completed = rbinomial(reached,.8)\ngen reach = reached/eligible if eligible>0\ngen fidelity = completed/reached if reached>0\ngen implementation_cost = 500+15*reached\nassert completed<=reached & reached<=eligible\nassert missing(fidelity) if reached==0\ntabstat adopted reach fidelity, by(strategy) stat(mean n)\n* Site-weighted reach contrast. Zero reach remains in the primary analysis.\nregress reach i.strategy, vce(robust)\nlincom 1.strategy\n* Person-weighted summaries use pooled numerators AND denominators.\npreserve\ncollapse (sum) reached eligible completed implementation_cost, by(strategy)\ngen pooled_reach = reached/eligible\ngen pooled_fidelity = completed/reached if reached>0\ngen cost_per_reached = implementation_cost/reached if reached>0\nlist, noobs\nrestore\n* These are different estimands; do not equate a conditional fidelity contrast\n* with a randomized strategy effect on everyone originally eligible.\n```\n","source":"guides/implementation-science-skill.html"}},{"id":"studio-methods:active-health-hiap-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"主动健康与健康融入万策（Health in All Policies，总纲旗舰）","summary":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"active-health-hiap-skill","title":"主动健康与健康融入万策（Health in All Policies，总纲旗舰）","tags":"主动健康与健康融入万策（Health in All Policies，总纲旗舰） active-health-hiap-skill","use":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"skills/active-health-hiap-skill.md","codes":[{"lang":"r","heading":"板块 1：理论框架（怎么写进论文与标书）","code":"library(quanteda)\ndocs <- readtext::readtext(\"policy_texts/*.txt\")          # 政策文件目录\ncorp <- corpus(docs)\ntoks <- tokens(corp, remove_punct = TRUE)\ndfmat <- dfm(toks)\nkw <- c(\"健康\", \"预防\", \"公平\", \"环境\", \"医疗\", \"保障\")\ndfm_select(dfmat, pattern = kw) |> textstat_frequency()    # 政策文本中的健康话语频次"},{"lang":"stata","heading":"板块 2：健康影响评估（HIA）方法","code":"* Stata：PAF 与可避免负担的情景计算\nlocal p  0.35            // 基线暴露率\nlocal RR 1.45            // 暴露-反应 RR（示例，来自 Meta 分析）\nlocal paf = (`p'*(`RR'-1))/(`p'*(`RR'-1)+1)\ndi \"PAF = \" %5.3f `paf'\nlocal B 12000            // 基线年死亡数（示例）\ndi \"基线归因死亡 = \" %9.0f (`B'*`paf')\n* 干预使暴露率降至 0.25\nlocal p2 0.25\nlocal paf2 = (`p2'*(`RR'-1))/(`p2'*(`RR'-1)+1)\ndi \"干预后可避免死亡 = \" %9.0f (`B'*(`paf'-`paf2'))\n\n* 公平性：集中指数（协方差法）\negen r = rank(y)         // y 为健康结局，按经济状况排序后的秩\nsum y\ngen lhs = 2*y*r/r(mean)\nquietly reg lhs r\ndi \"CI = \" %5.3f _b[r]"},{"lang":"r","heading":"板块 2：健康影响评估（HIA）方法","code":"# R：同一计算 + 集中指数 + 情景比较\npaf <- function(p, RR) p*(RR-1)/(p*(RR-1)+1)\nB <- 12000\nbase  <- B * paf(0.35, 1.45)\nafter <- B * paf(0.25, 1.45)\navoid <- base - after\ncat(sprintf(\"基线归因死亡 %.0f；干预后可避免死亡 %.0f\\n\", base, avoid))\n\n# 集中指数\nci <- function(y, x) 2 * cov(y, rank(x)) / mean(y)\nci(health_y, income_x)"},{"lang":"r","heading":"板块 3：数据源（健康融入万策的证据底座）","code":"library(readxl)\ndf <- read_excel(\"GBD_results.xlsx\")     # 从 GBD Results Tool 导出的 CSV/XLSX\nagg <- aggregate(deaths ~ year + location, data = df, sum)\n# 与政策时点对齐：政策前后负担变化（配合 ITS/DID skill 做因果评估）"}],"status":"教学模板 / 原有代码摘录；本轮未在 R 或 Stata 执行，需核对依赖与变量"}},{"id":"studio-methods:binary-studio","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"二元结局与边际效应","summary":"区分优势比、风险比与风险差；报告有解释性的绝对概率。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"binary-studio","title":"二元结局与边际效应","tags":"二元 binary logistic logit margins","use":"区分优势比、风险比与风险差；报告有解释性的绝对概率。","url":"skills/binary-studio.md","codes":[{"lang":"r","heading":"R 模板","code":"# df包含二元y、exposure、age\nfit <- glm(y ~ exposure + age, family=binomial(), data=df)\nsummary(fit)\nexp(cbind(OR=coef(fit), confint(fit)))\ndf0 <- transform(df,exposure=0); df1 <- transform(df,exposure=1)\nmean(predict(fit,df1,type=\"response\")-predict(fit,df0,type=\"response\"))\n# 上式为模型标准化差异，因果解释需额外识别假设；区间另用适当bootstrap"},{"lang":"stata","heading":"STATA 模板","code":"logit y i.exposure c.age, vce(robust)\nmargins exposure\nmargins, dydx(exposure)\n* 优势比不等于风险比；设计、混杂控制和聚类层级需先确定"}],"status":"教学模板 / 原有代码摘录；本轮未在 R 或 Stata 执行，需核对依赖与变量"}},{"id":"studio-methods:dce-design-assistant-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"DCE 设计助手（离散选择实验设计与分析，Stata / R）","summary":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"dce-design-assistant-skill","title":"DCE 设计助手（离散选择实验设计与分析，Stata / R）","tags":"DCE 设计助手（离散选择实验设计与分析，Stata / R） dce-design-assistant-skill","use":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"skills/dce-design-assistant-skill.md","codes":[{"lang":"stata","heading":"软件准备","code":"* Stata（联网安装一次）\nssc install mixlogit, replace\nssc install lclogit2, replace\nssc install dcreate,  replace"},{"lang":"r","heading":"软件准备","code":"# R\ninstall.packages(c(\"DoE.base\", \"AlgDesign\", \"support.CEs\", \"mlogit\", \"gmnl\"))"},{"lang":"stata","heading":"第 1 步：确定属性与水平","code":"* Stata：生成 2^4 全因子（16 行），用于观察所有组合\nclear\nset obs 16\ngen a = mod(_n-1, 2)              // 0/1\ngen b = mod(int((_n-1)/2), 2)\ngen c = mod(int((_n-1)/4), 2)\ngen d = mod(int((_n-1)/8), 2)\nlist"},{"lang":"r","heading":"第 1 步：确定属性与水平","code":"# R：属性水平表与全因子规模\nlevels <- list(费用 = c(10, 30, 50),   # 元/次\n               候诊 = c(1, 3, 7),      # 天\n               连续性 = c(0, 1),       # 0=不同医生 1=同一医生\n               目录 = c(0, 1))         # 0=基础目录 1=扩展目录\nsapply(levels, length)                 # 各属性水平数\nfull <- expand.grid(levels)            # 全因子组合\nnrow(full)                             # 3*3*2*2 = 36"},{"lang":"stata","heading":"第 2 步：正交设计与 D-效率设计","code":"* Stata：2^(4-1) 分辨率 IV 正交主效应设计（8 行，生成元 d = a*b*c 模 2）\nclear\nset obs 16\ngen a = mod(_n-1, 2)\ngen b = mod(int((_n-1)/2), 2)\ngen c = mod(int((_n-1)/4), 2)\ngen d = mod(int((_n-1)/8), 2)\nkeep if mod(a+b+c+d, 2) == 0\nlist, sep(0)\n\n* Stata：3^4 的 L9 正交设计（9 行）\nclear\nset obs 27\ngen a = mod(_n-1, 3)\ngen b = mod(int((_n-1)/3), 3)\ngen c = mod(int((_n-1)/9), 3)\nkeep if mod(a+b+c, 3) == 0\n\n* Stata：D-效率设计（dcreate，SSC）。示例：3 个属性、每选择集 2 个方案、12 个选择集\nmatrix b0 = J(1, 3, 0)                    // 先验参数（默认 0）\ndcreate i.x1 i.x2 i.x3, nalt(2) nset(12) bmat(b0) seed(2026)\n* 注：dcreate 要求因子变量写法；详细选项以 help dcreate 为准"},{"lang":"r","heading":"第 2 步：正交设计与 D-效率设计","code":"# R：正交主效应设计（DoE.base）\nlibrary(DoE.base)\ndes24 <- oa.design(nlevels = c(2, 2, 2, 2))    # 2^4 → 8 行 L8\ndes33 <- oa.design(nlevels = c(3, 3, 3, 3))    # 3^4 → 9 行 L9\nas.data.frame(des24)\n\n# R：D-效率设计（AlgDesign）\nlibrary(AlgDesign)\nfull36 <- expand.grid(x1 = c(0, 1, 2), x2 = c(0, 1, 2), x3 = c(0, 1), x4 = c(0, 1))\ndopt <- optFederov(~ ., data = full36, nTrials = 16, criterion = \"D\")\ndopt$design                                  # 选中的 16 个 profile\ndopt$D                                       # D-效率值"},{"lang":"stata","heading":"第 3 步：检查设计质量","code":"* Stata：水平平衡 + 两两相关\ntab1 a b c d\npwcorr a b c d"},{"lang":"r","heading":"第 3 步：检查设计质量","code":"# R：水平平衡、相关矩阵与设计效率\napply(des24, 2, table)                       # 水平平衡\nround(cor(as.data.frame(des24)), 3)          # 正交性：非对角应 ≈ 0\neval.design(~ ., dopt$design)                # AlgDesign：方差膨胀、D/G 效率"},{"lang":"r","heading":"第 4 步：构造选择集与分块","code":"# R：随机配对、分块\nlibrary(dplyr)\nset.seed(2026)\nprof  <- as.data.frame(des24) |> sample_frac(1)      # 随机打乱\npairs <- matrix(seq_len(nrow(prof)), ncol = 2, byrow = TRUE)\nnset  <- nrow(pairs)                                  # 选择集数\nnblock <- 2; per <- 2                                 # 2 个块、每块 2 题\nblock  <- rep(1:nblock, each = per, length.out = nset)\nout <- data.frame(choice_set = rep(1:nset, each = 2),\n                  block = rep(block, each = 2),\n                  alt = rep(1:2, times = nset),\n                  prof[as.vector(t(pairs)), ])\n# opt-out 在问卷中额外提供“都不选”；数据里记 alt == 3、各属性 = 0"},{"lang":"stata","heading":"第 4 步：构造选择集与分块","code":"* Stata：为每个选择集追加 opt-out 行（alt == 3，属性全为 0）\nlocal nset 4\nforvalues s = 1/`nset' {\n    set obs `=_N+1'\n    replace choice_set = `s' if _n == _N\n    replace alt = 3 if _n == _N\n    foreach v in a b c d {\n        replace `v' = 0 if _n == _N\n    }\n}"},{"lang":"stata","heading":"第 5 步：问卷与预测试","code":"* Stata：预测试数据体检——受访者选择 opt-out 的次数分布\negen n_optout = total(choice == 1 & alt == 3), by(id)\ntab n_optout\n* 提示：某受访者所有题都选 opt-out，说明属性或任务设计可能有问题"},{"lang":"r","heading":"第 5 步：问卷与预测试","code":"# R：同一检查\nlibrary(dplyr)\ndat |> group_by(id) |>\n  summarise(n_optout = sum(choice == 1 & alt == 3),\n            n_A      = sum(choice == 1 & alt == 1),\n            n_total  = n_distinct(choice_set))"},{"lang":"stata","heading":"第 6 步：样本量与 long 数据整理","code":"* Stata：示例 long 数据（1 名受访者、2 个选择集、各 3 个方案）\nclear\ninput id choice_set alt choice a b c d\n1 1 1 1 0 0 1 1\n1 1 2 0 1 1 0 0\n1 1 3 0 0 0 0 0\n1 2 1 0 1 0 1 0\n1 2 2 1 0 1 0 1\n1 2 3 0 0 0 0 0\nend\n\n* 宽表 → 长表（宽表每行一个选择集，选项变量带后缀 _1/_2/_3）\nreshape long choice_ a_ b_ c_ d_, i(id choice_set) j(alt)\nrename (choice_ a_ b_ c_ d_) (choice a b c d)\n\n* effects coding 示例：三水平属性 a（0/1/2 → -1/0/1）\nrecode a (0 = -1) (1 = 0) (2 = 1)"},{"lang":"r","heading":"第 6 步：样本量与 long 数据整理","code":"# R：dfidx 构造选择数据（mlogit 用）\nlibrary(mlogit)\nmd <- dfidx(dat, idx = c(\"id\", \"alt\"), choice = \"choice\")"},{"lang":"stata","heading":"7.1 条件 logit（`clogit`）","code":"clogit choice a b c d, group(choice_set) vce(cluster id)\nest store clogit\n\n* WTP：d 为费用属性时的属性 b 的 WTP（delta 方法，含 95% CI）\nnlcom wtp_b: -_b[b] / _b[d]\n\n* IIA 直觉检验：删除 opt-out 后系数应大体稳定\nclogit choice a b c d if alt != 3, group(choice_set) vce(cluster id)"},{"lang":"r","heading":"7.1 条件 logit（`clogit`）","code":"# R：clogit（survival）与 mlogit 等价实现\nlibrary(survival)\nclogit(choice ~ a + b + c + d + strata(choice_set), data = dat)\n\nlibrary(mlogit)\nm1 <- mlogit(choice ~ a + b + c + d | 0, data = md)\nsummary(m1)"},{"lang":"stata","heading":"7.2 混合 logit（`mixlogit`）","code":"* 随机系数：a、b、c 正态，d 固定\nmixlogit choice a b c d, group(choice_set) id(id) rand(a b c) nrep(500)\nest store mixl\nfitstat                                     // AIC/BIC，与 clogit 比较\n\n* 对数正态费用 + 相关随机参数（ln(1) = rand() 中第 1 个随机参数取对数正态）\nmixlogit choice a b c d, group(choice_set) id(id) rand(a b c) ln(1) corr nrep(500)\n\n* WTP：mixlwtp（SSC）或 delta 方法；具体语法以 help mixlwtp 为准"},{"lang":"r","heading":"7.2 混合 logit（`mixlogit`）","code":"# R：混合 logit（mlogit rpar）\nm2 <- mlogit(choice ~ a + b + c + d | 0, data = md,\n             rpar = c(a = \"n\", b = \"n\", c = \"n\"),\n             R = 500, halton = NA, panel = TRUE)\nsummary(m2)"},{"lang":"stata","heading":"7.3 潜类别 logit（latent class，`lclogit2`）","code":"lclogit2 choice a b c d, group(choice_set) id(id) nclasses(2) seed(2026)\nlclogit2, nclasses(3) seed(2026)            // 比较 BIC/AIC 选类别数\nest store lc3\nlclogitprob, cpprob(3)                      // 各类别平均成员概率\nestat lcmean, post                          // 各类别协变量均值（刻画类别特征）\n\n* 类成员协变量模型（membership 方程放入 age、income）\nlclogitml choice a b c d, group(choice_set) id(id) nclasses(3) membership(age income)\n\n* WTP：lcwtp（lclogit2 附带），语法以 help lcwtp 为准；\n* 或手工取各类别系数比值并配合 delta 方法"},{"lang":"r","heading":"7.3 潜类别 logit（latent class，`lclogit2`）","code":"# R：潜类别 logit（gmnl）\nlibrary(gmnl)\nlc <- gmnl(choice ~ a + b + c + d | 0 | 0 | 0 | 1, data = md,\n           model = \"lc\", panel = TRUE, Q = 3)\nsummary(lc)"},{"lang":"stata","heading":"第 8 步：结果解读与报告","code":"* WTP 与置信区间（clogit，delta 方法）\nnlcom wtp_b: -_b[b] / _b[d], post\nestimates store wtp_b\n\n* 三模型并列输出\nestimates table clogit mixl lc3, b(%9.3f) se stats(N aic bic)"},{"lang":"r","heading":"第 8 步：结果解读与报告","code":"# R：delta 方法 WTP（car::deltaMethod）\nlibrary(car)\ndeltaMethod(coef(m1), \"-b/d\")               # 按实际系数名替换 b、d"}],"status":"教学模板 / 原有代码摘录；本轮未在 R 或 Stata 执行，需核对依赖与变量"}},{"id":"studio-methods:ddd-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"三重差分助手（DDD 政策效果评估，Stata / R）","summary":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"ddd-skill","title":"三重差分助手（DDD 政策效果评估，Stata / R）","tags":"三重差分助手（DDD 政策效果评估，Stata / R） ddd-skill","use":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"skills/ddd-skill.md","codes":[{"lang":"stata","heading":"软件准备","code":"* Stata（联网安装一次）\nssc install reghdfe, replace\n* 若 ftools 未被自动装上：ssc install ftools, replace"},{"lang":"r","heading":"软件准备","code":"# R\ninstall.packages(\"fixest\")"},{"lang":"stata","heading":"第 0 步：构造模拟面板数据（贯穿全流程）","code":"* ============ 第 0 步：模拟面板数据（贯穿全流程） ============\nclear all\nset seed 2026\n\n* 60 家医院：id 1-30 公立（treated = 1），31-60 民营（treated = 0）\nset obs 60\ngen id = _n\ngen treated = (id <= 30)          // 处理维度：1 = 公立医院（受集采约束）\ngen u_i = rnormal(0, 4)           // 医院层面随机效应（制造院内相关，聚类依据）\n\n* 每家医院 2 类药品：group = 1 集采品种，0 非集采品种\nexpand 2\nbysort id: gen group = _n - 1\n\n* 每个（医院 × 品种）2 期：post = 0 政策前，1 政策后\nexpand 2\nbysort id group: gen post = _n - 1\ngen time = post                   // 供 reghdfe absorb() 使用\n\n* ---------- 真实数据生成过程（DGP，内置真实 DDD 效应 δ = -8） ----------\n* y = γ0 + γ1·treated + γ2·post + γ3·group\n*     + γ12·treated×post + γ13·treated×group + γ23·post×group\n*     + δ·treated×post×group + u_i + ε\nlocal g0 100                        // 基组均值（民营 × 非集采 × 政策前）\nlocal g1 10                         // 公立医院整体费用水平更高\nlocal g2 -5                         // 全体时间趋势（普遍控费）\nlocal g3 20                         // 集采品种用量大、费用更高\nlocal g12 -6                        // 偏误 1：公立医院特有趋势（DRG / 取消加成等）\nlocal g13 5                         // 公立医院对集采品种的固定偏好\nlocal g23 -4                        // 偏误 2：集采品种特有趋势（专利到期等）\nlocal delta -8                      // 真实 DDD 效应：集采使公立医院集采品种额外下降 8 万元/月\n\ngen y = `g0' + `g1'*treated + `g2'*post + `g3'*group + ///\n        `g12'*treated*post + `g13'*treated*group + `g23'*post*group + ///\n        `delta'*treated*post*group + u_i + rnormal(0, 2)\n\n* ---------- 稳健性结局（第 4 步用） ----------\n* y_qty：集采品种采购数量（相关结局，真实效应较小 δ = -3）\ngen y_qty = 60 + 8*treated - 4*post + 18*group - 3*treated*post + ///\n            4*treated*group - 2*post*group - 3*treated*post*group + ///\n            u_i + rnormal(0, 3)\n* y_dev：与药品无关的耗材费用（安慰剂结局，真实 δ = 0）\ngen y_dev = 40 + 5*treated - 2*post + 2*group + 1*treated*post - ///\n            1*treated*group + 0.5*post*group + 0*treated*post*group + ///\n            0.5*u_i + rnormal(0, 5)\n\n* 数据体检：8 组均值表（Stata 17+；16 及更早用 bysort treated post group: summarize y）\ntable treated post group, stat(mean y) stat(sd y) nototals\n* 期望组均值（噪声平均掉后）：民营 非集采 前 100 / 后 95；民营 集采 前 120 / 后 111；\n*                             公立 非集采 前 110 / 后 99；公立 集采 前 135 / 后 112"},{"lang":"r","heading":"第 0 步：构造模拟面板数据（贯穿全流程）","code":"# ============ 第 0 步：模拟面板数据（贯穿全流程） ============\nset.seed(2026)\nN <- 60                                     # 60 家医院\ndat <- expand.grid(id = 1:N, group = c(0, 1), post = c(0, 1))\ndat$treated <- as.integer(dat$id <= 30)     # 处理维度：1 = 公立医院\ndat$u_i     <- rnorm(N, 0, 4)[dat$id]       # 医院层面随机效应（聚类依据）\ndat$time    <- dat$post                     # 供 fixest 吸收固定效应\n\n# 真实参数（内置真实 DDD 效应 delta = -8）\ng0 <- 100; g1 <- 10; g2 <- -5; g3 <- 20\ng12 <- -6; g13 <- 5; g23 <- -4; delta <- -8\n\ndat$y <- with(dat, g0 + g1*treated + g2*post + g3*group +\n                   g12*treated*post + g13*treated*group + g23*post*group +\n                   delta*treated*post*group + u_i + rnorm(nrow(dat), 0, 2))\n\n# 稳健性结局：y_qty（相关结局，真实 δ = -3）；y_dev（安慰剂结局，真实 δ = 0）\ndat$y_qty <- with(dat, 60 + 8*treated - 4*post + 18*group - 3*treated*post +\n                      4*treated*group - 2*post*group - 3*treated*post*group +\n                      u_i + rnorm(nrow(dat), 0, 3))\ndat$y_dev <- with(dat, 40 + 5*treated - 2*post + 2*group + 1*treated*post -\n                      1*treated*group + 0.5*post*group + 0*treated*post*group +\n                      0.5*u_i + rnorm(nrow(dat), 0, 5))\n\n# 8 组均值表（应与下方期望值接近）\naggregate(y ~ treated + post + group, data = dat, FUN = mean)"},{"lang":"stata","heading":"第 1 步：从 DID 到 DDD：动机","code":"* 图形直觉：四组均值线（先平均到 8 个单元格）\npreserve\ncollapse (mean) y, by(treated post group)\ntwoway (connected y post if treated==1 & group==1, sort) ///\n       (connected y post if treated==1 & group==0, sort) ///\n       (connected y post if treated==0 & group==1, sort) ///\n       (connected y post if treated==0 & group==0, sort), ///\n       legend(order(1 \"公立·集采\" 2 \"公立·非集采\" 3 \"民营·集采\" 4 \"民营·非集采\")) ///\n       xtitle(\"post（0 政策前 / 1 政策后）\") ytitle(\"月均药品费用（万元）\")\nrestore"},{"lang":"r","heading":"第 1 步：从 DID 到 DDD：动机","code":"# 图形直觉：四组均值线（两条 DID 线的斜率差之差 = DDD）\ncell <- aggregate(y ~ treated + post + group, data = dat, FUN = mean)\nplot(NA, xlim = c(0, 1), ylim = range(cell$y),\n     xlab = \"post（0 政策前 / 1 政策后）\", ylab = \"月均药品费用 y（万元）\",\n     main = \"四组均值线：DDD = 实线斜率差 - 虚线斜率差\")\nlines(y ~ post, data = cell, subset = treated == 1 & group == 1, type = \"b\", col = \"red\",  lty = 1)\nlines(y ~ post, data = cell, subset = treated == 1 & group == 0, type = \"b\", col = \"blue\", lty = 1)\nlines(y ~ post, data = cell, subset = treated == 0 & group == 1, type = \"b\", col = \"red\",  lty = 2)\nlines(y ~ post, data = cell, subset = treated == 0 & group == 0, type = \"b\", col = \"blue\", lty = 2)\nlegend(\"topright\", c(\"公立·集采\", \"公立·非集采\", \"民营·集采\", \"民营·非集采\"),\n       col = c(\"red\", \"blue\", \"red\", \"blue\"), lty = c(1, 1, 2, 2), bty = \"n\")"},{"lang":"stata","heading":"第 2 步：模型设定与估计（Stata / R 对应）","code":"* ---------- 模型 A：pooled OLS + 医院聚类稳健标准误 ----------\nreg y i.treated##i.post##i.group, vce(cluster id)\nest store ddd_ols\n\n* ---------- 模型 B：医院 + 时间固定效应（reghdfe） ----------\nreghdfe y i.treated##i.post##i.group, absorb(id time) vce(cluster id)\nest store ddd_fe\n* 说明：treated、post 主效应被吸收（与 id / time 固定效应共线，输出中标注）；\n*       两两交互与三重交互仍可估，δ 与模型 A 相同"},{"lang":"r","heading":"第 2 步：模型设定与估计（Stata / R 对应）","code":"library(fixest)\n\n# 模型 A：pooled OLS + 医院聚类稳健标准误（不吸收固定效应）\nm_ols <- feols(y ~ treated*post*group, cluster = ~id, data = dat)\nsummary(m_ols)\n\n# 模型 B：医院 + 时间固定效应（treated、post 因共线被自动移除）\nm_fe <- feols(y ~ treated*post*group | id + time, cluster = ~id, data = dat)\nsummary(m_fe)\n\n# 三重交互系数 δ（fixest 中交互用 \":\" 连接，对应 Stata 的 #）\ncoef(m_fe)[\"treated:post:group\"]"},{"lang":"stata","heading":"第 3 步：核心系数 δ 的解读与分解验证","code":"* ---------- 3.1 两个 2×2 DID（品种类型 DID，在公立/民营切片上） ----------\nreg y i.post##i.group if treated == 1, vce(cluster id)\nlocal did1 = _b[1.post#1.group]          // 期望 ≈ -12（= γ23 + δ）\nreg y i.post##i.group if treated == 0, vce(cluster id)\nlocal did2 = _b[1.post#1.group]          // 期望 ≈ -4（= γ23）\n\n* ---------- 3.2 分解：DDD = DID1 - DID2 ----------\ndisplay \"DID1(品种|公立) = \" %5.2f `did1'\ndisplay \"DID2(品种|民营) = \" %5.2f `did2'\ndisplay \"DDD = DID1 - DID2 = \" %5.2f (`did1' - `did2')\n\n* ---------- 3.3 镜像分解（医院类型 DID，在集采/非集采切片上） ----------\nreg y i.treated##i.post if group == 1, vce(cluster id)\nlocal did1b = _b[1.treated#1.post]       // 期望 ≈ -14（= γ12 + δ）\nreg y i.treated##i.post if group == 0, vce(cluster id)\nlocal did2b = _b[1.treated#1.post]       // 期望 ≈ -6（= γ12）\ndisplay \"镜像分解：DID(医院|集采) - DID(医院|非集采) = \" %5.2f (`did1b' - `did2b')\n\n* ---------- 3.4 与主回归三重交互系数对照（期望 ≈ -8） ----------\nreg y i.treated##i.post##i.group, vce(cluster id)\ndisplay \"主回归三重交互系数 δ = \" %5.2f _b[1.treated#1.post#1.group]"},{"lang":"r","heading":"第 3 步：核心系数 δ 的解读与分解验证","code":"# 3.1 两个 2×2 DID（品种类型 DID 在公立/民营切片上）\ndid1 <- coef(feols(y ~ post*group | id, cluster = ~id, data = dat[dat$treated == 1, ]))[\"post:group\"]\ndid2 <- coef(feols(y ~ post*group | id, cluster = ~id, data = dat[dat$treated == 0, ]))[\"post:group\"]\nc(DID1 = unname(did1), DID2 = unname(did2), DDD = unname(did1 - did2))\n# 期望：DID1 ≈ -12，DID2 ≈ -4，DDD ≈ -8 = δ\n\n# 3.2 镜像分解（医院类型 DID 在集采/非集采切片上），期望 ≈ -8\nd1 <- coef(feols(y ~ treated*post | id, cluster = ~id, data = dat[dat$group == 1, ]))[\"treated:post\"]\nd2 <- coef(feols(y ~ treated*post | id, cluster = ~id, data = dat[dat$group == 0, ]))[\"treated:post\"]\nunname(d1 - d2)\n\n# 3.3 与主回归三重交互系数对照（期望 ≈ -8）\ncoef(m_fe)[\"treated:post:group\"]"},{"lang":"stata","heading":"第 4 步：平行趋势与稳健性（识别假设、动态检验、安慰剂、替换结局）","code":"* ---------- 动态检验思路（示意，需多期数据；本模拟两期无法执行） ----------\n* 假设 time = -2..2，政策时点 = 0，基准期 = -1：\n* reghdfe y ib(-1).time##i.treated##i.group, absorb(id) vce(cluster id)\n* 关注各期 1.time#1.treated#1.group 系数（政策前应 ≈ 0）\n* 画图：ssc install coefplot; coefplot, keep(*.time#1.treated#1.group) vertical yline(0)\n* 具体 keep 匹配规则以 help coefplot 为准\n\n* ---------- 安慰剂 1：伪第三维度（Stata permute，200 次） ----------\n* 随机置换 group，破坏品种维度结构；真实 δ = -8 应落在置换分布左尾之外\npermute group delta = _b[1.treated#1.post#1.group], ///\n        reps(200) seed(2026): reg y i.treated##i.post##i.group, vce(cluster id)\n\n* ---------- 安慰剂 2：伪时点（示意，需 time = 0..3 的多期数据） ----------\n* gen post_fake = (time == 1)        // 把政策提前到 t=1，只用真政策前数据\n* reg y i.treated##i.post_fake##i.group if time < 2, vce(cluster id)  // 期望 δ ≈ 0\n\n* ---------- 替换结局 ----------\nreghdfe y_qty i.treated##i.post##i.group, absorb(id time) vce(cluster id)  // 相关结局：期望 δ ≈ -3\nreghdfe y_dev i.treated##i.post##i.group, absorb(id time) vce(cluster id)  // 安慰剂结局：期望 δ ≈ 0\n\n* ---------- 聚类层级敏感性：多向聚类（reghdfe 支持） ----------\nreghdfe y i.treated##i.post##i.group, absorb(id time) vce(cluster id time)"},{"lang":"r","heading":"第 4 步：平行趋势与稳健性（识别假设、动态检验、安慰剂、替换结局）","code":"# ---------- 安慰剂 1（R 版）：伪第三维度，医院内随机交换品种标签，200 次 ----------\nset.seed(2026)\nperm_delta <- replicate(200, {\n  d <- dat\n  d$group_p <- ave(d$group, d$id, FUN = sample)      # 每家医院内部重排，保留医院结构\n  coef(feols(y ~ treated*post*group_p | id + time, data = d))[\"treated:post:group_p\"]\n})\nhist(perm_delta, main = \"伪第三维度：δ 的置换分布（200 次）\")\nabline(v = -8, col = \"red\", lwd = 2)                 # 真实 δ = -8 应落在左尾之外\nmean(perm_delta <= -8)                               # 置换单侧 p 值（期望接近 0）\n\n# ---------- 替换结局 ----------\ncoef(feols(y_qty ~ treated*post*group | id + time, cluster = ~id, data = dat))[\"treated:post:group\"]  # 期望 ≈ -3\ncoef(feols(y_dev ~ treated*post*group | id + time, cluster = ~id, data = dat))[\"treated:post:group\"]  # 期望 ≈ 0\n\n# 伪时点（示意，需 ≥3 期数据）：把政策时点前移后用真政策前数据估计 δ，期望 ≈ 0；\n# 写法同上，仅替换 post 变量与子样本（以 fixest 帮助文档为准）"},{"lang":"stata","heading":"第 6 步：报告清单","code":"* 主回归表：逐步（仅主效应 → +两两交互 → +三重交互）\nreg y i.treated i.post i.group, vce(cluster id)\nest store m1\nreg y i.treated i.post i.group i.treated#i.post i.treated#i.group i.post#i.group, vce(cluster id)\nest store m2\nreg y i.treated##i.post##i.group, vce(cluster id)\nest store m3\nestimates table m1 m2 m3, b(%9.2f) se stats(N)\n* 报告时另附 95% CI（reg 输出自带）与聚类数（60 家医院）"},{"lang":"r","heading":"第 6 步：报告清单","code":"# 主回归表：逐步（fixest 的 etable；模型 SE 已在估计时按 id 聚类）\nm1 <- feols(y ~ treated + post + group, cluster = ~id, data = dat)\nm2 <- feols(y ~ treated*post + treated*group + post*group, cluster = ~id, data = dat)\nm3 <- feols(y ~ treated*post*group, cluster = ~id, data = dat)\netable(m1, m2, m3)          # 选项以 ?etable 为准"}],"status":"教学模板 / 原有代码摘录；本轮未在 R 或 Stata 执行，需核对依赖与变量"}},{"id":"studio-methods:did-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"DID 双重差分助手（卫生政策效果评估，Stata / R）","summary":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"did-skill","title":"DID 双重差分助手（卫生政策效果评估，Stata / R）","tags":"DID 双重差分助手（卫生政策效果评估，Stata / R） did-skill","use":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"skills/did-skill.md","codes":[{"lang":"stata","heading":"软件准备","code":"* Stata（联网安装一次）\nssc install csdid,    replace    // 交错处理 DID（Callaway-Sant'Anna）\nssc install drdid,    replace    // 双重稳健 DID（csdid 的依赖）\nssc install reghdfe,  replace    // 高维固定效应（事件研究回归用）\nssc install ftools,   replace    // reghdfe 的依赖\nssc install coefplot, replace    // 系数图\nssc install estout,   replace    // 结果表\nssc install ritest,   replace    // 随机化推断（安慰剂检验备选）\nssc install psmatch2, replace    // 倾向得分匹配"},{"lang":"r","heading":"软件准备","code":"# R\ninstall.packages(c(\"fixest\", \"did\", \"MatchIt\", \"ggplot2\", \"cobalt\", \"modelsummary\"))"},{"lang":"stata","heading":"第 0 步：生成贯穿全文的模拟面板数据","code":"* ============================================================\n* 模拟面板：60 地区 × 4 期；30 个处理组交错入组；真实 ATT = -150\n* ============================================================\nclear all\nset seed 2026\nset obs 60\ngen id = _n\n* 处理时点 g（0 = 永不受理）\ngen g = 0\nreplace g = 2 if inrange(id, 1, 10)\nreplace g = 3 if inrange(id, 11, 20)\nreplace g = 4 if inrange(id, 21, 30)\ngen treated = (g > 0)\n* 地区特征（时不变）：固定效应、自身趋势、可观测协变量\ngen alpha = rnormal(900, 50)        // 地区平均次均费用水平\ngen beta  = rnormal(-8, 4)          // 地区自身趋势（元/期）\ngen x1 = rnormal(0.3*treated, 1)    // 协变量 1：与入选相关\ngen x2 = rnormal(0, 1)              // 协变量 2\n* 4 期面板\nexpand 4\nbysort id: gen t = _n\ngen lambda = -30 * (t - 1)          // 共同时间趋势\ngen D = (t >= g & g > 0)            // 处理指示\ngen y   = alpha + beta*(t-1) + lambda + (-150)*D + rnormal(0, 40)           // 次均药品费用\ngen adm = 0.20 - 0.01*(t-1) + (alpha-900)/3000 + (-0.02)*D + rnormal(0, 0.04)  // 住院率\nxtset id t\nsave \"did_demo.dta\", replace\n* 数据体检\nxtdes                               // 面板结构：60 个地区 × 4 期、无缺失\ntab g                               // 队列规模：g=2/3/4 各 10，g=0 共 30\ntabstat y if t == 1, by(treated) stat(n mean sd)   // 干预前基线比较"},{"lang":"r","heading":"第 0 步：生成贯穿全文的模拟面板数据","code":"# ============================================================\n# 模拟面板：60 地区 × 4 期；30 个处理组交错入组；真实 ATT = -150\n# ============================================================\nset.seed(2026)\nN <- 60; Tmax <- 4\nid  <- 1:N\ng   <- c(rep(2, 10), rep(3, 10), rep(4, 10), rep(0, 30))   # 处理时点；0 = 永不受理\ntreated <- as.integer(g > 0)\nalpha <- rnorm(N, 900, 50)          # 地区固定效应\nbeta  <- rnorm(N, -8, 4)            # 地区自身趋势\nx1 <- rnorm(N, 0.3 * treated, 1)    # 协变量 1：与入选相关\nx2 <- rnorm(N, 0, 1)                # 协变量 2\n\ndat <- expand.grid(id = id, t = 1:Tmax)\ndat$g <- g[dat$id]; dat$treated <- treated[dat$id]\ndat$alpha <- alpha[dat$id]; dat$beta <- beta[dat$id]\ndat$x1 <- x1[dat$id]; dat$x2 <- x2[dat$id]\ndat$D <- as.integer(dat$t >= dat$g & dat$g > 0)\ndat$y <- dat$alpha + dat$beta * (dat$t - 1) - 30 * (dat$t - 1) +\n         (-150) * dat$D + rnorm(N * Tmax, 0, 40)                       # 次均药品费用\ndat$adm <- 0.20 - 0.01 * (dat$t - 1) + (dat$alpha - 900) / 3000 +\n           (-0.02) * dat$D + rnorm(N * Tmax, 0, 0.04)                  # 住院率\n\ntable(dat$g)                        # 队列规模\naggregate(y ~ treated + t, data = dat, FUN = mean)     # 各期均值（干预前应接近）"},{"lang":"stata","heading":"第 1 步：研究设计与识别假设","code":"* 干预前基线平衡表（t=1 期）\ntabstat y adm x1 x2 if t == 1, by(treated) stat(n mean sd)\n* 各期结局均值图（第一道识别直觉：干预前两组路径应平行）\negen ybar = mean(y), by(treated t)\ntwoway (line ybar t if treated == 1) (line ybar t if treated == 0), ///\n    legend(order(1 \"处理组\" 2 \"对照组\")) xline(2.5, lp(dash)) ///\n    title(\"各期次均药品费用：处理组 vs 对照组（干预前应平行）\")\ndrop ybar"},{"lang":"r","heading":"第 1 步：研究设计与识别假设","code":"# 干预前基线平衡表\naggregate(cbind(y, adm, x1, x2) ~ treated, data = subset(dat, t == 1), FUN = mean)\n# 各期均值图\nmeans <- aggregate(y ~ treated + t, data = dat, FUN = mean)\nwith(means, {\n    plot(t, y, type = \"n\", xlab = \"t\", ylab = \"次均药品费用\")\n    lines(t[treated == 1], y[treated == 1], col = \"red\",  lwd = 2)\n    lines(t[treated == 0], y[treated == 0], col = \"blue\", lwd = 2)\n    abline(v = 2.5, lty = 2)\n    legend(\"topright\", c(\"处理组\", \"对照组\"), col = c(\"red\", \"blue\"), lwd = 2)\n})"},{"lang":"stata","heading":"第 2 步：经典 2×2 DID","code":"* 两期子样本：t=1 干预前、t=2 干预后；处理组 = t=2 期入组队列\npreserve\nkeep if inlist(t, 1, 2)\ngen post = (t == 2)\ngen treated2 = (g == 2)                // 10 个处理组；g=3/4 在 t<=2 尚未处理，作为对照\n* 经典 2×2（OLS + 交互项）\nreg y i.treated2##i.post, vce(cluster id)\nest store m_2x2\n* 系数解释：1.treated2 = 基线差异；1.post = 共同时间变化；\n*           1.treated2#1.post = ATT ≈ -150（集采使次均药品费用下降约 150 元）\n\n* 等价：双向固定效应（系数相同）\nreg y i.treated2#i.post i.id i.t, vce(cluster id)\n\n* 官方命令（Stata 17+）：didregress（自动 FE 与 ATET 报告）\ndidregress (y) (treated2), group(id) time(t) vce(cluster id)\n* 注：didregress 假设处理组单一入组时点；交错处理请用第 4 步的 csdid\nrestore"},{"lang":"r","heading":"第 2 步：经典 2×2 DID","code":"library(fixest)\nd2 <- subset(dat, t %in% 1:2)\nd2$post <- as.integer(d2$t == 2)\nd2$treated2 <- as.integer(d2$g == 2)\n\n# 双向固定效应 DID：组/期主效应被 FE 吸收，交互项 = ATT\nm_2x2 <- feols(y ~ treated2 * post | id + t, data = d2, cluster = ~id)\nsummary(m_2x2)                       # treated2:post ≈ -150\n\n# 等价：不含 FE 的经典 2×2（两期时系数相同，方便逐项解释）\nm_ols <- feols(y ~ treated2 * post, data = d2, cluster = ~id)\nsummary(m_ols)\n# 系数：treated2 = 基线差异；post = 共同时间变化；treated2:post = ATT"},{"lang":"stata","heading":"第 3 步：平行趋势检验","code":"* 事件研究（手工交互虚拟变量，基准 = 干预前一期 rel = -1）\npreserve\nkeep if inlist(g, 0, 3)\ngen treated3 = (g == 3)\ngen rel = t - 3                         // rel = -2,-1,0,1\ngen ev_m2 = (rel == -2 & treated3 == 1)\ngen ev_m1 = (rel == -1 & treated3 == 1) // 基准（回归中省略）\ngen ev_0  = (rel ==  0 & treated3 == 1)\ngen ev_1  = (rel ==  1 & treated3 == 1)\nreghdfe y ev_m2 ev_0 ev_1, absorb(id t) vce(cluster id)\ntest ev_m2 = 0                          // 正式检验：干预前系数 = 0\ncoefplot, keep(ev_*) vertical yline(0, lcolor(red)) xline(1.5, lcolor(gs8) lp(dash))\n* 图：ev_m2（干预前）应 ≈ 0；ev_0、ev_1（干预后）应 ≈ -150\n* 注：本例受 4 期数据限制只有 1 个前趋势系数；真实研究应有 3 期以上干预前数据，\n*     并联合检验全部前趋势系数（test ev_m2 ev_m3 ... = 0）\n\n* 官方替代（Stata 17+）：didregress + 预趋势检验\ndidregress (y) (treated3), group(id) time(t) vce(cluster id)\nestat trendplots                        // 各期处理-对照差异图（含置信区间）\nestat ptrends                           // 平行趋势联合检验（H0：干预前各期系数均为 0）\nrestore"},{"lang":"r","heading":"第 3 步：平行趋势检验","code":"# 事件研究（fixest 的 i() 交互；基准 rel = -1）\nlibrary(fixest)\nes <- subset(dat, g %in% c(0, 3))\nes$treated3 <- as.integer(es$g == 3)\nes$rel <- es$t - 3\nm_es <- feols(y ~ i(rel, treated3, ref = -1) | id + t, data = es, cluster = ~id)\nsummary(m_es)\niplot(m_es)                             # 事件研究图：干预前系数应 ≈ 0\n# 正式检验：干预前系数（本例为 rel = -2）\ncoef(m_es)                              # 系数名形如 rel::-2:treated3（以实际输出为准）\nwald(m_es, \"rel::-2:treated3\")          # 名称以 coef(m_es) 输出为准\n# 亦可用 coefplot(m_es)（fixest 自带）；选项以 help(coefplot.fixest) 为准\n# 交错处理时可用 did::pre.test()（对 CS 估计做干预前联合检验，以 help(pre.test) 为准）"},{"lang":"stata","heading":"第 4 步：多期 / 交错处理 DID（Callaway & Sant'Anna, 2021）","code":"* Stata 18+：官方 csdid；Stata 17 及更早：ssc install csdid, replace + ssc install drdid, replace\n* 全样本：30 个处理组在 t=2/3/4 交错入组（g=2/3/4），30 个永不受理（g=0）\ncsdid y, ivar(id) time(t) gvar(g)\n* 聚合（官方 18：estat simple/event/group/calendar；SSC 版：csdid_estat simple ...）\nestat simple                  // 总体 ATT ≈ -150\nestat event                   // 事件时间聚合（供第 5 步作图）\nestat group                   // 按队列聚合\n* 选项（method、对照组选择、权重自举等）以 help csdid 为准\n\n* 官方替代（Stata 18+）：xthdidregress，处理时点由 D 自动识别\nxthdidregress ra (y) (D), group(id) time(t)\n* 聚合与作图：estat aggregation / estat atetplot；语法以 help xthdidregress postestimation 为准"},{"lang":"r","heading":"第 4 步：多期 / 交错处理 DID（Callaway & Sant'Anna, 2021）","code":"# Callaway-Sant'Anna（官方 R 实现）\nlibrary(did)\nout <- att_gt(yname = \"y\", tname = \"t\", idname = \"id\", gname = \"g\",\n              data = dat, control_group = \"notyettreated\")\nsummary(out)                            # ATT(g,t) 表：队列 g × 时期 t\nagg_simple <- aggte(out, type = \"simple\")    # 总体 ATT ≈ -150\nsummary(agg_simple)\nagg_group  <- aggte(out, type = \"group\")     # 按队列\nagg_cal    <- aggte(out, type = \"calendar\")  # 按日历期\n# 对照组设定、估计方法（est_method = \"dr\"/\"reg\"/\"ipw\"）等选项以 help(att_gt) 为准\n\n# 替代：Sun & Abraham（fixest）\nfeols(y ~ sunab(g, t) | id + t, data = dat, cluster = ~id)"},{"lang":"stata","heading":"第 5 步：事件研究图","code":"* 接第 4 步 csdid\nestat event                   // 官方 18；SSC 版为 csdid_estat event\ncsdid_plot                    // 事件研究图（官方 18 与 SSC 版均有）\n* 若版本支持，亦可用 marginsplot：estat event 后 marginsplot\n* （以 help csdid postestimation / help csdid_plot 为准）"},{"lang":"r","heading":"第 5 步：事件研究图","code":"# 接第 4 步\nagg_dyn <- aggte(out, type = \"dynamic\", min_e = -3, max_e = 2)\nsummary(agg_dyn)                    # 事件研究系数表（含各期置信区间）\nggdid(agg_dyn)                      # 事件研究图（需 ggplot2）\nggdid(agg_dyn, ylim = c(-300, 60), xlab = \"相对处理期\",\n      title = \"动态处理效应：集采对次均药品费用\")\n# ggdid 的图形选项以 help(ggdid) 为准"},{"lang":"stata","heading":"第 6 步：稳健性检验","code":"* ---------- 6.1 伪处理组（随机重抽处理组身份，500 次） ----------\npreserve\nkeep if inlist(t, 1, 2)\ngen post = (t == 2)\ntempname pf\ntempfile pfres\npostfile `pf' b_did using `pfres', replace\nset seed 2026\nforvalues r = 1/500 {\n    * 在地区层面抽一个伪处理身份（同一地区两期身份一致）\n    bysort id: gen rnd_id = runiform() if _n == 1\n    bysort id (rnd_id): replace rnd_id = rnd_id[1]\n    egen prank = rank(rnd_id), unique\n    gen treatedF = (prank <= 10)          // 随机 10 个伪处理地区\n    quietly reg y i.treatedF##i.post, vce(cluster id)\n    post `pf' (_b[1.treatedF#1.post])\n    drop rnd_id prank treatedF\n}\npostclose `pf'\nuse `pfres', clear\nsummarize b_did, detail                  // 伪系数分布：中心应 ≈ 0\ncount if b_did <= -150                   // 伪系数 ≤ 真实系数（-150）的次数\ndi (r(N) + 1) / 501                      // 经验 p 值（单侧）\nhistogram b_did, normal xline(-150, lcolor(red))\n* 备选：ritest（SSC）做随机化推断，支持聚类层级（选项以 help ritest 为准）\nrestore\n\n* ---------- 6.2 伪时点（预期效应检验） ----------\n* 用 g=3 队列的干预前两期（t=1,2），人为假设 t=2 已干预\npreserve\nkeep if inlist(g, 0, 3) & inlist(t, 1, 2)\ngen post = (t == 2)\ngen treated3 = (g == 3)\nreg y i.treated3##i.post, vce(cluster id)     // 预期系数 ≈ 0\nrestore\n\n* ---------- 6.3 剔除部分样本 ----------\n* (a) 剔除 y 上下 1% 极端值后重估 2×2\npreserve\nkeep if inlist(t, 1, 2)\ngen post = (t == 2)\ngen treated2 = (g == 2)\nquietly summarize y, detail\ndrop if y < r(p1) | y > r(p99)\nreg y i.treated2##i.post, vce(cluster id)\nrestore\n* (b) 对照只用永不受理组（排除尚未处理队列），多期 FE 估计\npreserve\nkeep if inlist(g, 0, 2)\ngen post = (t >= 2)\ngen treated2 = (g == 2)\nreghdfe y i.treated2#i.post, absorb(id t) vce(cluster id)\nrestore\n\n* ---------- 6.4 PSM-DID ----------\npreserve\nkeep if inlist(t, 1, 2)\ngen post = (t == 2)\ngen treated2 = (g == 2)\n* 方案 A（官方 teffects）：横截面 ATT 演示（把两期分别视为观测）\nteffects psmatch (y) (treated2 x1 x2), atet\n* 方案 B（psmatch2 + DID，SSC；实务常用）：按干预前协变量匹配后做 DID\npsmatch2 treated2 x1 x2, out(y) logit common\npstest x1 x2, both                        // 匹配后平衡性：标准化差异应 < 0.1\nreg y i.treated2##i.post [pw = _weight], vce(cluster id)\nest store m_psm\n* 注：psmatch2 生成的 _weight 为匹配权重；以 help psmatch2 为准\nrestore\n\n* ---------- 6.5 更换结局与聚类层级 ----------\npreserve\nkeep if inlist(t, 1, 2)\ngen post = (t == 2)\ngen treated2 = (g == 2)\nreg adm i.treated2##i.post, vce(cluster id)      // 结局换为住院率：预期 ≈ -0.02\nest store m_adm\n* 聚类层级敏感性：地区 → 更保守的层级（示例：每 3 个地区并为一组）\negen group3 = group(ceil(id/3))\nreg y i.treated2##i.post, vce(cluster group3)    // 聚类数变少，标准误通常变大\nrestore"},{"lang":"r","heading":"第 6 步：稳健性检验","code":"# 接第 2 步的 d2（t ∈ {1,2}；post、treated2 已定义）\nlibrary(fixest)\n\n# ---------- 6.1 伪处理组（500 次随机重抽） ----------\nset.seed(2026)\nids <- unique(d2$id)\nb_null <- replicate(500, {\n    fake <- sample(ids, 10)                      # 随机 10 个伪处理地区\n    d2$treatedF <- as.integer(d2$id %in% fake)\n    m <- feols(y ~ treatedF * post | id + t, data = d2, cluster = ~id)\n    coef(m)[\"treatedF:post\"]                     # 系数名以 coef(m) 输出为准\n})\npval <- (sum(b_null <= -150) + 1) / 501          # 经验 p 值（单侧）\nhist(b_null, breaks = 30, main = \"伪处理组 DID 系数分布\")\nabline(v = -150, col = \"red\", lwd = 2)\n\n# ---------- 6.2 伪时点 ----------\nd_pre <- subset(dat, g %in% c(0, 3) & t %in% 1:2)\nd_pre$post <- as.integer(d_pre$t == 2)\nd_pre$treated3 <- as.integer(d_pre$g == 3)\nfeols(y ~ treated3 * post | id + t, data = d_pre, cluster = ~id)   # 预期 ≈ 0\n\n# ---------- 6.3 剔除极端值 ----------\nq <- quantile(d2$y, c(0.01, 0.99))\nd2t <- subset(d2, y > q[1] & y < q[2])\nfeols(y ~ treated2 * post | id + t, data = d2t, cluster = ~id)\n\n# ---------- 6.4 PSM-DID ----------\nlibrary(MatchIt)\nd2pre <- subset(d2, t == 1)\nm.out <- matchit(treated2 ~ x1 + x2, data = d2pre, method = \"nearest\", ratio = 1)\nsummary(m.out)                       # 匹配后标准化均值差应 < 0.1\n# library(cobalt); love.plot(m.out)  # 平衡性图\nmd  <- match.data(m.out)             # 匹配样本（含 weights）\nd2m <- subset(d2, id %in% unique(md$id))\nfeols(y ~ treated2 * post | id + t, data = d2m, cluster = ~id)\n\n# ---------- 6.5 更换结局与聚类层级 ----------\nfeols(adm ~ treated2 * post | id + t, data = d2, cluster = ~id)     # 住院率 ≈ -0.02\nd2$group3 <- ceiling(d2$id / 3)\nfeols(y ~ treated2 * post | id + t, data = d2, cluster = ~group3)   # 更保守聚类"},{"lang":"stata","heading":"第 7 步：报告清单","code":"* 结果表（esttab，SSC；官方 etable 亦可）\nesttab m_2x2 m_psm m_adm, b(%9.2f) se(%9.2f) star(* 0.10 ** 0.05 *** 0.01) ///\n    mtitles(\"2x2 DID\" \"PSM-DID\" \"结局=住院率\") ///\n    stats(N N_clust, labels(\"观测数\" \"聚类数\"))\n* CS 聚合结果用 estat simple / estat event 直接导出；\n* esttab 与 etable 选项以 help esttab / help etable 为准"},{"lang":"r","heading":"第 7 步：报告清单","code":"# 结果表（fixest::etable 或 modelsummary）\netable(list(m_2x2, m_psm, m_adm), cluster = ~id, fitstat = ~n)\n# library(modelsummary)\n# modelsummary(list(\"2x2\" = m_2x2, \"PSM-DID\" = m_psm, \"住院率\" = m_adm), stars = TRUE)"}],"status":"教学模板 / 原有代码摘录；本轮未在 R 或 Stata 执行，需核对依赖与变量"}},{"id":"studio-methods:event-study-studio","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"交错 DID 与多时点事件研究","summary":"区分分组和时间效应；常规双向固定效应并不适用于所有异质交错处理。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"event-study-studio","title":"交错 DID 与多时点事件研究","tags":"DID 事件研究 多时点 csdid att_gt","use":"区分分组和时间效应；常规双向固定效应并不适用于所有异质交错处理。","url":"skills/event-study-studio.md","codes":[{"lang":"r","heading":"R 模板","code":"# 依赖 did；g 为首次处理年，未处理者为 0\nlibrary(did)\nfit <- att_gt(yname=\"y\", tname=\"year\", idname=\"id\", gname=\"g\",\n              data=df, control_group=\"notyettreated\", panel=TRUE,\n              clustervars=\"id\", bstrap=TRUE)\nes <- aggte(fit, type=\"dynamic\")\nsummary(es); ggdid(es)\n# 另检查提前反应、样本组成、同期政策与平行趋势；更高政策层级需相应聚类"},{"lang":"stata","heading":"STATA 模板","code":"* 需 csdid 与 drdid；g=首次处理年份，未处理为0\ncsdid y, ivar(id) time(year) gvar(g) notyet method(dripw)\nestat event\ncsdid_plot\n* 聚类层级、控制组与协变量需按具体政策分配结构设置"}],"status":"教学模板 / 原有代码摘录；本轮未在 R 或 Stata 执行，需核对依赖与变量"}},{"id":"studio-methods:health-economics-tutorial-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"卫生经济学评价教程（CEA/CUA 总纲，Stata / R）","summary":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"health-economics-tutorial-skill","title":"卫生经济学评价教程（CEA/CUA 总纲，Stata / R）","tags":"卫生经济学评价教程（CEA/CUA 总纲，Stata / R） health-economics-tutorial-skill","use":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"skills/health-economics-tutorial-skill.md","codes":[{"lang":"stata","heading":"软件准备","code":"* Stata（本教程主要用官方命令；结果表导出可选用 estout，putexcel 为官方命令）\nssc install estout, replace"},{"lang":"r","heading":"软件准备","code":"# R\ninstall.packages(\"boot\")      # bootstrap 置信区间；其余均为基础 R"},{"lang":"stata","heading":"第 1 步：提出研究问题与评价类型选择","code":"* Stata：评价类型选择示意（把结局类型写入 local 后运行）\nlocal outcome \"qaly\"    // 可选：equivalent / natural / qaly / monetary\nif \"`outcome'\" == \"equivalent\" {\n    display \"结局已证明等效 → 用 CMA（最小成本分析）\"\n}\nelse if \"`outcome'\" == \"natural\" {\n    display \"结局为自然单位（血压、生命年等）→ 用 CEA（成本-效果分析）\"\n}\nelse if \"`outcome'\" == \"qaly\" {\n    display \"结局为偏好加权指标（QALY/DALY）→ 用 CUA（成本-效用分析）\"\n}\nelse {\n    display \"结局已货币化或需跨领域比较 → 用 CBA（成本-效益分析）\"\n}"},{"lang":"r","heading":"第 1 步：提出研究问题与评价类型选择","code":"# R：评价类型选择决策函数\npick_type <- function(outcome = c(\"equivalent\", \"natural\", \"qaly\", \"monetary\")) {\n  outcome <- match.arg(outcome)\n  switch(outcome,\n    equivalent = \"CMA：最小成本分析（结局等效时只比较成本）\",\n    natural    = \"CEA：成本-效果分析（自然单位结局）\",\n    qaly       = \"CUA：成本-效用分析（QALY/DALY 等偏好加权结局）\",\n    monetary   = \"CBA：成本-效益分析（成本与效果均货币化）\")\n}\npick_type(\"qaly\")     # 本算例用 QALY → CUA"},{"lang":"stata","heading":"第 2 步：研究视角与时间范围","code":"* Stata：成本条目按研究视角纳入的示意见表（1 = 纳入，0 = 不纳入）\n* 注：中文值需 Stata 17+（UTF-8）\nclear\ninput byte payer byte system byte society str50 item\n1 1 1 \"药品费用（医保报销部分）\"\n1 1 1 \"检查与住院（医保报销部分）\"\n0 1 1 \"政府财政补助与公共卫生投入\"\n0 1 1 \"患者自付医疗费用\"\n0 0 1 \"交通、陪护与食宿（患者家庭）\"\n0 0 1 \"因病损失的生产力\"\nend\nlabel var payer   \"医保支付方视角\"\nlabel var system  \"卫生体系视角\"\nlabel var society \"社会视角\"\nlist, sep(0) noobs"},{"lang":"r","heading":"第 2 步：研究视角与时间范围","code":"# R：不同研究视角纳入的成本条目（示例矩阵）\nitems <- data.frame(\n  条目 = c(\"药品费用（医保报销部分）\", \"检查与住院（医保报销部分）\", \"政府财政补助与公共卫生投入\",\n           \"患者自付医疗费用\", \"交通、陪护与食宿（患者家庭）\", \"因病损失的生产力\"),\n  医保支付方 = c(\"√\", \"√\", \"—\", \"—\", \"—\", \"—\"),\n  卫生体系   = c(\"√\", \"√\", \"√\", \"√\", \"—\", \"—\"),\n  社会视角   = c(\"√\", \"√\", \"√\", \"√\", \"√\", \"√\"))\nitems"},{"lang":"stata","heading":"第 3 步：成本测算","code":"* Stata：直接医疗成本示例数据（元/人/年；与贯穿算例一致：对照均值 6040、干预均值 9180）\nclear\ninput byte group int cost\n0 6000\n0 5500\n0 6800\n0 5900\n0 6200\n0 6100\n0 5700\n0 6400\n0 5800\n0 6000\n1 9000\n1 8800\n1 9500\n1 9200\n1 8900\n1 9600\n1 9100\n1 9300\n1 9000\n1 9400\nend\nlabel define grp 0 \"对照\" 1 \"干预\"\nlabel values group grp\n\n* 均数与 95% CI（正态近似）\nmean cost, over(group)\nci means cost if group == 0\nci means cost if group == 1\n\n* bootstrap：两组成本之差的 95% 百分位区间与偏差校正（BC）区间\ncapture program drop diff_cost\nprogram define diff_cost, rclass\n    quietly summarize cost if group == 0\n    local m0 = r(mean)\n    quietly summarize cost if group == 1\n    local m1 = r(mean)\n    return scalar diff = `m1' - `m0'\nend\nbootstrap r(diff), reps(1000) seed(2026) nodots: diff_cost\nestat bootstrap, percentile bc"},{"lang":"r","heading":"第 3 步：成本测算","code":"# R：直接医疗成本示例数据（贯穿算例：对照组均值 6040、干预组均值 9180）\ncost0 <- c(6000, 5500, 6800, 5900, 6200, 6100, 5700, 6400, 5800, 6000)\ncost1 <- c(9000, 8800, 9500, 9200, 8900, 9600, 9100, 9300, 9000, 9400)\nc(mean0 = mean(cost0), mean1 = mean(cost1))\nt.test(cost0)$conf.int          # 对照组 95% CI（正态近似）\nt.test(cost1)$conf.int          # 干预组 95% CI\n\n# bootstrap：两组成本之差的 95% 百分位 CI\nlibrary(boot)\nd <- data.frame(cost = c(cost0, cost1), group = rep(c(0, 1), each = 10))\ndiff_mean <- function(data, i) {\n  g <- data[i, ]\n  mean(g$cost[g$group == 1]) - mean(g$cost[g$group == 0])\n}\nset.seed(2026)\nb <- boot(d, diff_mean, R = 1000)\nboot.ci(b, type = \"perc\")       # 差值点估计 ≈ 9180 - 6040 = 3140 元"},{"lang":"stata","heading":"第 4 步：效果与效用指标","code":"* Stata：QALY 与增量 QALY（效用 × 生存年数，未贴现）\nscalar u0 = 0.70       // 对照年效用\nscalar u1 = 0.78       // 干预年效用\nscalar years = 10\ndisplay \"对照 QALY = \" u0 * years          // 7.0\ndisplay \"干预 QALY = \" u1 * years          // 7.8\ndisplay \"ΔQALY     = \" (u1 - u0) * years   // 0.8"},{"lang":"r","heading":"第 4 步：效果与效用指标","code":"# R：QALY 与增量 QALY（未贴现）\nu0 <- 0.70; u1 <- 0.78; years <- 10\nQ0 <- u0 * years        # 7.0\nQ1 <- u1 * years        # 7.8\ndQ <- Q1 - Q0           # 0.8 QALY\nlist(QALY_control = Q0, QALY_treat = Q1, dQALY = dQ)\n\n# R：DALY 简算示意：DALY = YLL + YLD\nYLL <- 10               # 早亡损失的生命年（示例）\nYLD <- 2.5              # 伤残状态生存年 × 伤残权重（示例）\nYLL + YLD               # 12.5 DALY\n\n# 提示：EQ-5D 状态（如 21121）须用已发表的中国积分体系系数换算效用值，\n# 勿自造系数；换算公式形如 U = 1 −（常数项 + 各维度离差项 + 交互/极端项）"},{"lang":"stata","heading":"第 5 步：贴现","code":"* Stata：贴现因子与 10 年年金贴现因子（r = 5%）\nscalar r = 0.05\nscalar df = 0\nforvalues t = 1/10 {\n    scalar df = df + 1 / (1 + r)^`t'\n}\ndisplay \"10 年、5% 的年金贴现因子 = \" %9.4f df      // 7.7217\n\n* 贴现后的增量成本与增量效果\nscalar dC = (12000 - 8000) * df\nscalar dE = (0.78 - 0.70) * df\ndisplay \"贴现后 ΔC = \" %9.2f dC                      // 30886.93 元\ndisplay \"贴现后 ΔE = \" %9.4f dE                      // 0.6177 QALY\n\n* 贴现率敏感性（0–8%）\nforeach rr in 0 0.03 0.05 0.08 {\n    scalar df = 0\n    forvalues t = 1/10 {\n        scalar df = df + 1 / (1 + `rr')^`t'\n    }\n    display \"r = \" `rr' \"：10 年年金贴现因子 = \" %9.4f df\n}"},{"lang":"r","heading":"第 5 步：贴现","code":"# R：贴现与贴现率敏感性\nr <- 0.05; t <- 1:10\ndisc <- 1 / (1 + r)^t\nsum(disc)                                   # 7.7217（10 年、5% 年金贴现因子）\ndC <- (12000 - 8000) * sum(disc)            # 30886.9（贴现后增量成本，元）\ndE <- (0.78 - 0.70) * sum(disc)             # 0.6177（贴现后增量 QALY）\n\n# 贴现率敏感性（0–8%）\nsapply(c(0, 0.03, 0.05, 0.08), function(rr) sum(1 / (1 + rr)^(1:10)))\n# 本算例年增量恒定 → 贴现率不改变 ICER（= 4000 / 0.08 = 50000）；\n# 成本与效果时间分布不同时，ICER 会随贴现率变化（见第 7 步）"},{"lang":"stata","heading":"第 6 步：增量分析与 ICER","code":"* Stata：ICER、净收益与四象限判断\nscalar dC = 30886.9\nscalar dE = 0.6177\nscalar lambda = 100000\ndisplay \"ICER = \" %9.1f dC / dE                    // ≈ 50003 元/QALY\ndisplay \"NB(λ=100000) = \" %9.1f lambda * dE - dC   // ≈ 30887 元\n\nif dC > 0 & dE > 0 {\n    display \"东北象限：更贵更有效 → 比较 ICER 与 λ\"\n    display cond(dC/dE < lambda, \"ICER < λ → 具有成本-效果\", \"ICER ≥ λ → 不具有成本-效果\")\n}\nelse if dC < 0 & dE > 0 {\n    display \"东南象限：更便宜更有效 → 优势策略，直接推荐\"\n}\nelse if dC > 0 & dE < 0 {\n    display \"西北象限：更贵更差 → 劣势策略，拒绝\"\n}\nelse {\n    display \"西南象限：更便宜更差 → 权衡节省与效果损失\"\n}"},{"lang":"r","heading":"第 6 步：增量分析与 ICER","code":"# R：ICER、净收益与四象限判断\ndC <- 30886.9; dE <- 0.6177\nlambda <- 100000\nICER <- dC / dE                                    # ≈ 50003 元/QALY\nNB   <- lambda * dE - dC                            # ≈ 30887 元（净货币收益）\nquadrant <- if (dC > 0 && dE > 0) \"NE：更贵更有效（与 λ 比较）\" else\n            if (dC < 0 && dE > 0) \"SE：更便宜更有效（优势）\" else\n            if (dC > 0 && dE < 0) \"NW：更贵更差（劣势）\" else\n            \"SW：更便宜更差（权衡）\"\nverdict <- ifelse(ICER < lambda, \"ICER < λ，具有成本-效果\", \"ICER ≥ λ，不具有成本-效果\")\nlist(ICER = ICER, NB = NB, quadrant = quadrant, verdict = verdict)"},{"lang":"stata","heading":"第 7 步：不确定性分析","code":"* Stata：单因素敏感性分析（基线 ICER ≈ 50003 元/QALY）\nscalar dC = 30886.9\nscalar dE = 0.6177\ndisplay \"基线 ICER            = \" %9.1f dC / dE\ndisplay \"ΔE +20% 的 ICER      = \" %9.1f dC / (dE * 1.2)\ndisplay \"ΔE −20% 的 ICER      = \" %9.1f dC / (dE * 0.8)\ndisplay \"ΔC +20% 的 ICER      = \" %9.1f (dC * 1.2) / dE\ndisplay \"ΔC −20% 的 ICER      = \" %9.1f (dC * 0.8) / dE\n\n* 干预组年效用取 0.73 / 0.83（其余不变；贴现后 ΔE = Δu × 7.7217）\ndisplay \"干预效用 0.73 → ICER = \" %9.1f dC / (0.03 * 7.7217)\ndisplay \"干预效用 0.83 → ICER = \" %9.1f dC / (0.13 * 7.7217)\n* 解读：ICER 范围约 30769–133333 元/QALY，跨过 λ = 100000 →\n* 结论对效用参数敏感，报告中须突出说明"},{"lang":"r","heading":"第 7 步：不确定性分析","code":"# R：概率敏感性分析（PSA）示意——直接从 ΔC、ΔE 的分布抽样；\n# 正式研究应从参数层面抽样（成本 gamma、概率 beta、RR lognormal），\n# 完整实现见《Markov 模型助手》skill。\nset.seed(2026)\nn <- 1000\ndC <- rnorm(n, mean = 30886.9, sd = 3200)     # 增量成本（元）\ndE <- rnorm(n, mean = 0.6177, sd = 0.05)      # 增量 QALY\n\n# 成本-效果平面散点图（λ = 100000 元/QALY 的斜率线）\nplot(dE, dC, xlab = \"ΔE（QALY）\", ylab = \"ΔC（元）\", pch = 20,\n     cex = 0.5, col = rgb(0, 0, 0, 0.3), main = \"PSA 成本-效果平面散点图\")\nabline(h = 0, v = 0, lty = 2)\nabline(a = 0, b = 100000, col = \"red\")        # 斜率 = λ\n\n# 各象限模拟点占比\nquad <- ifelse(dE > 0, ifelse(dC > 0, \"NE\", \"SE\"),\n               ifelse(dC > 0, \"NW\", \"SW\"))\ntable(quad) / n\n\n# 成本-效果可接受曲线（CEAC）：P(NMB(λ) > 0)\nlam  <- seq(0, 300000, by = 5000)\nceac <- sapply(lam, function(x) mean(x * dE - dC > 0))\nplot(lam, ceac, type = \"l\", xlab = \"支付意愿阈值 λ（元/QALY）\",\n     ylab = \"具有成本-效果的概率\", ylim = c(0, 1))\nabline(h = 0.5, lty = 3)"},{"lang":"stata","heading":"第 8 步：报告规范（CHEERS 2022）","code":"* Stata：把主要结果写入 Excel（putexcel，Stata 14+）\nputexcel set results.xlsx, replace\nputexcel A1 = \"指标\" B1 = \"数值\"\nputexcel A2 = \"贴现后 ΔC（元）\"        B2 = 30886.9\nputexcel A3 = \"贴现后 ΔE（QALY）\"      B3 = 0.6177\nputexcel A4 = \"ICER（元/QALY）\"        B4 = 50003\nputexcel A5 = \"阈值 λ（元/QALY）\"      B5 = 100000\nputexcel A6 = \"净收益 NB（元）\"        B6 = 30887"},{"lang":"r","heading":"第 8 步：报告规范（CHEERS 2022）","code":"# R：导出主要结果表\nres <- data.frame(\n  指标 = c(\"贴现后 ΔC（元）\", \"贴现后 ΔE（QALY）\", \"ICER（元/QALY）\",\n           \"阈值 λ（元/QALY）\", \"净收益 NB（元）\"),\n  数值 = c(30886.9, 0.6177, 50003, 100000, 30887))\nres\nwrite.csv(res, \"results.csv\", row.names = FALSE)"},{"lang":"r","heading":"第 9 步：研究工具箱指引（本仓库配套 Skill）","code":"# R：本仓库配套 Skill 一览（可复制到 README / 网页）\ntoolbox <- data.frame(\n  Skill = c(\"DCE 设计助手\", \"Markov 模型助手\", \"机器学习\", \"DID 双重差分\"),\n  什么时候用 = c(\n    \"测量偏好、效用值与支付意愿（EQ-5D 的补充或替代）\",\n    \"慢性病动态建模、时间外推与完整 PSA/CEAC\",\n    \"真实世界大数据下的费用预测与风险分层\",\n    \"用面板数据评估医保与支付改革政策的因果效果\"))\ntoolbox"}],"status":"教学模板 / 原有代码摘录；本轮未在 R 或 Stata 执行，需核对依赖与变量"}},{"id":"studio-methods:its-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"间断时间序列助手（ITS 政策效果评估，Stata / R）","summary":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"its-skill","title":"间断时间序列助手（ITS 政策效果评估，Stata / R）","tags":"间断时间序列助手（ITS 政策效果评估，Stata / R） its-skill","use":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"skills/its-skill.md","codes":[{"lang":"stata","heading":"软件准备","code":"* Stata（联网安装一次）\nssc install actest, replace\nssc install itsa,   replace\nssc install estout, replace"},{"lang":"r","heading":"软件准备","code":"# R\ninstall.packages(c(\"lmtest\", \"sandwich\", \"car\", \"ggplot2\", \"texreg\"))"},{"lang":"stata","heading":"第 1 步：适用条件与研究设计（含数据要求与模拟数据）","code":"* =====================================================================\n* 模拟数据：48 个月 = 干预前 24（第 1–24 月）+ 干预后 24（第 25–48 月）\n* 场景：某院 DRG 付费改革（第 25 个月实施）对次均住院费用（元）的影响\n* 内置真实干预效应：\n*   β0 = 12000（干预时点基线，元）  β1 = -50（干预前趋势，元/月）\n*   β2 = -600 （即时水平变化，元）  β3 = -30（斜率变化，元/月）\n* 误差：AR(1)，rho = 0.3，扰动标准差 200 元\n* =====================================================================\nclear all\nset seed 2026\nset obs 48\ngen time        = _n\ngen time_c      = time - 25               // 中心化：干预时点 = 0\ngen level       = time >= 25              // 干预指示：第 25 个月起 = 1\ngen trend_after = time_c * level          // 干预后斜率增量（干预前恒为 0）\n\n* AR(1) 误差\ngen e = rnormal(0, 200)\nforvalues t = 2/48 {\n    replace e = 0.3 * e[_n-1] + rnormal(0, 200) if time == `t'\n}\n\n* 结局：次均住院费用（元）\ngen y = 12000 - 50*time_c - 600*level - 30*trend_after + e\n\ntsset time\ntwoway (line y time), xline(24.5, lpattern(dash)) ///\n    title(\"模拟月度序列：次均住院费用\") ytitle(\"元\") xtitle(\"月份\")"},{"lang":"r","heading":"第 1 步：适用条件与研究设计（含数据要求与模拟数据）","code":"# =====================================================================\n# 模拟数据（与 Stata 版同参数；随机数实现不同，不影响示例）\n# =====================================================================\nset.seed(2026)\nn           <- 48\ntime        <- 1:n\ntime_c      <- time - 25                 # 中心化：干预时点 = 0\nlevel       <- as.numeric(time >= 25)    # 干预指示：第 25 个月起 = 1\ntrend_after <- time_c * level            # 干预后斜率增量\n\n# AR(1) 误差：rho = 0.3，扰动 sd = 200\ne <- as.numeric(arima.sim(model = list(ar = 0.3), n = n, sd = 200))\n\n# 结局：次均住院费用（元），内置真实效应\ny <- 12000 - 50*time_c - 600*level - 30*trend_after + e\n\ndat <- data.frame(time, time_c, level, trend_after, y)\nplot(y ~ time, data = dat, type = \"o\", pch = 16, cex = 0.7,\n     main = \"模拟月度序列：次均住院费用\", xlab = \"月份\", ylab = \"元\")\nabline(v = 24.5, lty = 2, col = \"gray50\")"},{"lang":"stata","heading":"第 2 步：分段回归模型","code":"* 中心化版本（推荐）：β2 直接 = 干预时点即时效应\nregress y time_c level trend_after\n* 预计：_b[level] ≈ -600、_b[trend_after] ≈ -30（样本有噪声）\n\n* 对照：未中心化版本（time 与 time*level 交互）\ngen time_level = time * level\nregress y time level time_level\n* 此时 level 系数不再等于干预时点效应（是 time=0 处的跃变），须用组合计算"},{"lang":"r","heading":"第 2 步：分段回归模型","code":"# 中心化版本（推荐）\nm0 <- lm(y ~ time_c + level + trend_after, data = dat)\nsummary(m0)\n# 预计：level ≈ -600、trend_after ≈ -30（样本有噪声）\n\n# 组合效应（第 3 步换 HAC 协方差后正式报告）\nlibrary(car)\nlinearHypothesis(m0, \"time_c + trend_after = 0\")   # 干预后总斜率（β1+β3）\ndeltaMethod(m0, \"level + 23*trend_after\")          # 期末（time_c=23）总效应"},{"lang":"stata","heading":"第 3 步：自相关处理","code":"* ① 残差诊断\nregress y time_c level trend_after\npredict r, residuals\ntsline r, yline(0) title(\"残差时序图\")\nac r, lags(12) title(\"残差 ACF\")\n\nestat dwatson                    // Durbin-Watson（一阶）\nactest, lags(4)                  // Cumby-Huizinga 一般检验（SSC；选项以 help actest 为准）\nwntestq r, lags(4)               // Ljung-Box Q 检验\n\n* 平稳性检查（结局本身）\ndfuller y, trend\n\n* ② Newey-West / HAC 标准误\nnewey y time_c level trend_after, lag(1)\n* 默认滞后 = floor(4*(T/100)^(2/9))（T=48 → 3）；建议对 lag(1)–lag(4) 做敏感性\n\n* ③ 备选：Prais-Winsten AR(1)（选项以 help prais 为准）\nprais y time_c level trend_after, rhotype(tscorr)"},{"lang":"r","heading":"第 3 步：自相关处理","code":"# ① 残差诊断\nm0 <- lm(y ~ time_c + level + trend_after, data = dat)\nplot(resid(m0), type = \"o\", pch = 16, cex = 0.7,\n     xlab = \"月份\", ylab = \"残差\"); abline(h = 0)\nacf(resid(m0), lag.max = 12, main = \"残差 ACF\")\n\nlibrary(lmtest)\ndwtest(m0)                    # Durbin-Watson（一阶）\nbgtest(m0, order = 4)         # Breusch-Godfrey LM（高阶）\n\n# ② Newey-West / HAC 标准误\nlibrary(sandwich)\nsummary(m0)                                                    # 普通 SE（参考）\ncoeftest(m0, vcov = NeweyWest(m0, lag = 1, prewhite = FALSE))  # HAC SE，lag 固定为 1\ncoeftest(m0, vcov = NeweyWest(m0))                             # 默认：自动带宽 + prewhite\n# 注：NeweyWest 默认 prewhite = TRUE 且自动选带宽；与 Stata lag(1) 对照时\n#     用 lag = 1, prewhite = FALSE（以 help(NeweyWest) 为准）\n\n# ③ 备选：AR(1) 误差模型（nlme）\nlibrary(nlme)\ng1 <- gls(y ~ time_c + level + trend_after, data = dat,\n          correlation = corAR1(form = ~ time))\nsummary(g1)"},{"lang":"stata","heading":"第 4 步：模型估计：Stata 与 R 实现（对照）","code":"* ============ 4.1 手动分段回归 + Newey-West ============\nnewey y time_c level trend_after, lag(1) force\n* 注：任务书简写 newey y time level trend, lag(1) force；此处 time 为干预时点\n*     中心化的 time_c；force 允许未 tsset 的数据仍按时间序列估计（已 tsset 可省略）\nestimates store its_manual\n\n* 组合效应（HAC 标准误下的线性组合检验）\nlincom time_c + trend_after              // 干预后总斜率（β1+β3）\nlincom level + 23*trend_after            // 期末（第 48 月）总效应（β2+23·β3）\n\n* ============ 4.2 itsa 命令（SSC，Ariel Linden）============\n* ssc install itsa, replace\nitsa y, single treatperiod(25) posttrend\n* 注：任务书示例写法 itsa y, single treat(25) 中的选项名，实际对应\n*     treatperiod(25)，以 help itsa 为准；itsa 默认 OLS + Newey-West 标准误。\n* 带滞后因变量的动态版本：itsa y, single treatperiod(25) lag(1) posttrend\n* 出图：itsa y, single treatperiod(25) posttrend figure"},{"lang":"r","heading":"第 4 步：模型估计：Stata 与 R 实现（对照）","code":"# ============ R：lm + NeweyWest，与 Stata 对照 ============\nm <- lm(y ~ time_c + level + trend_after, data = dat)\n\nsummary(m)                                                    # 普通 SE（参考）\ntab_hac <- coeftest(m, vcov = NeweyWest(m, lag = 1, prewhite = FALSE))\nprint(tab_hac)                                                # HAC SE\ncoefci(tab_hac)                                               # 基于 HAC 的 95% CI\n\n# 组合效应（HAC 协方差下的线性组合与 delta 方法）\nlinearHypothesis(m, \"time_c + trend_after = 0\",\n                 vcov. = NeweyWest(m, lag = 1, prewhite = FALSE))   # 干预后总斜率\ndeltaMethod(m, \"level + 23*trend_after\",\n            vcov. = NeweyWest(m, lag = 1, prewhite = FALSE))         # 期末总效应"},{"lang":"stata","heading":"第 5 步：图形呈现（原始序列 + 拟合 + 反事实）","code":"newey y time_c level trend_after, lag(1)\npredict yhat, xb\ngen yhat_cf = _b[_cons] + _b[time_c]*time_c       // 反事实：延续干预前趋势\n\ntwoway (scatter y time, msize(small) mcolor(gs8)) ///\n       (line yhat time, lcolor(navy) lwidth(medthick)) ///\n       (line yhat_cf time if time >= 25, lcolor(red) lpattern(dash) lwidth(medthick)) ///\n       , xline(24.5, lcolor(gs10) lpattern(dash)) ///\n         xtitle(\"月份\") ytitle(\"次均费用（元）\") ///\n         title(\"ITS：DRG 改革对次均住院费用的影响\") ///\n         legend(order(1 \"观测值\" 2 \"模型拟合\" 3 \"反事实（无干预）\") position(6) rows(1))"},{"lang":"r","heading":"第 5 步：图形呈现（原始序列 + 拟合 + 反事实）","code":"# R（基础包）\nm <- lm(y ~ time_c + level + trend_after, data = dat)\ndat$fit <- fitted(m)\nb <- coef(m)\ndat$cf  <- b[\"(Intercept)\"] + b[\"time_c\"] * dat$time_c     # 反事实\n\nplot(y ~ time, data = dat, type = \"o\", pch = 16, cex = 0.7, col = \"gray50\",\n     main = \"ITS：DRG 改革对次均住院费用的影响\",\n     xlab = \"月份\", ylab = \"次均费用（元）\")\nabline(v = 24.5, lty = 3, col = \"gray40\")\nlines(dat$time, dat$fit, col = \"navy\", lwd = 2)\nlines(dat$time[dat$time >= 25], dat$cf[dat$time >= 25], col = \"red\", lwd = 2, lty = 2)\nlegend(\"bottomleft\", legend = c(\"观测值\", \"模型拟合\", \"反事实（无干预）\"),\n       col = c(\"gray50\", \"navy\", \"red\"), lty = c(1, 1, 2),\n       lwd = c(1, 2, 2), bty = \"n\")\n\n# ggplot2 等价写法（ggplot2 ≥ 3.4 用 linewidth，低版本用 size）\nlibrary(ggplot2)\nggplot(dat, aes(time, y)) +\n  geom_point(colour = \"grey50\") +\n  geom_vline(xintercept = 24.5, linetype = 3) +\n  geom_line(aes(y = fit), colour = \"navy\", linewidth = 1) +\n  geom_line(data = subset(dat, time >= 25), aes(y = cf),\n            colour = \"red\", linetype = 2, linewidth = 1) +\n  labs(x = \"月份\", y = \"次均费用（元）\",\n       title = \"ITS：DRG 改革对次均住院费用的影响\") +\n  theme_minimal()"},{"lang":"stata","heading":"第 6 步：稳健性与检验","code":"* ---- 6.1 安慰剂时点：假设干预发生在第 13 个月（实际无干预）----\ngen time_c13 = time - 13\ngen level13  = time >= 13\ngen trend13  = time_c13 * level13\nnewey y time_c13 level13 trend13, lag(1)\n* 期望：level13、trend13 系数接近 0 且不显著\n\n* ---- 6.2 季节性：加入月度虚拟变量（参考月 = 1 月）----\ngen month = mod(time - 1, 12) + 1\ntab month, gen(md)                          // 生成 md1–md12\nnewey y time_c level trend_after md2-md12, lag(1)\n* 期望：β2、β3 与主模型接近（以实际结果为准）\n\n* ---- 6.3 控制组 ITS（CITS）：双地区面板示意 ----\n* 面板长表：id（地区）、time、y；treat = 1（干预地区，第 25 个月起受干预）\n* 多组 itsa（语法与选项以 help itsa 为准）：\n*     xtset id time\n*     itsa y, treatperiod(25) treatid(treat) posttrend\n* 手动 DID 型交互模型（示意）：\ngen t_c   = time - 25\ngen lev   = time >= 25\ngen tr_a  = t_c * lev\ngen g_lev = treat * lev        // 水平变化的组间差（政策即时效应，DID）\ngen g_tr  = treat * tr_a       // 斜率变化的组间差（政策趋势效应，DID）\nnewey y t_c treat lev tr_a g_lev g_tr, lag(1)\n* 关键系数：g_lev（相对对照组的即时水平变化）、g_tr（相对对照组的斜率变化）\n\n* ---- 6.4 异常值敏感性：1%/99% 缩尾 ----\nquietly summarize y, detail\ngen y_w = min(max(y, r(p1)), r(p99))\nnewey y_w time_c level trend_after, lag(1)"},{"lang":"r","heading":"第 6 步：稳健性与检验","code":"# ---- 6.1 安慰剂时点：假设干预发生在第 13 个月 ----\ndat$time_c13 <- dat$time - 13\ndat$level13  <- as.numeric(dat$time >= 13)\ndat$trend13  <- dat$time_c13 * dat$level13\nm_placebo <- lm(y ~ time_c13 + level13 + trend13, data = dat)\ncoeftest(m_placebo, vcov = NeweyWest(m_placebo, lag = 1, prewhite = FALSE))\n# 期望：level13、trend13 接近 0 且不显著\n\n# ---- 6.2 季节性：加入月度虚拟变量 ----\ndat$month <- factor(((dat$time - 1) %% 12) + 1)\nm_season <- lm(y ~ time_c + level + trend_after + month, data = dat)\ncoeftest(m_season, vcov = NeweyWest(m_season, lag = 1, prewhite = FALSE))\n# 更平滑的季节建模可用正弦/余弦对（Fourier terms），以数据周期为准\n\n# ---- 6.3 控制组 ITS（CITS）：面板数据交互项 ----\n# 面板长表 panel：id、time、y、treat（1 = 干预地区）\npanel$t_c  <- panel$time - 25\npanel$lev  <- as.numeric(panel$time >= 25)\npanel$tr_a <- panel$t_c * panel$lev\nm_cits <- lm(y ~ t_c + treat + lev + tr_a + treat:lev + treat:tr_a, data = panel)\ncoeftest(m_cits, vcov = NeweyWest(m_cits, lag = 1, prewhite = FALSE))\n# 政策效应 = treat:lev（即时）与 treat:tr_a（斜率）的系数\n\n# ---- 6.4 异常值敏感性：1%/99% 缩尾 ----\nq <- quantile(dat$y, c(0.01, 0.99))\ndat$y_w <- pmin(pmax(dat$y, q[1]), q[2])\nm_w <- lm(y_w ~ time_c + level + trend_after, data = dat)\ncoeftest(m_w, vcov = NeweyWest(m_w, lag = 1, prewhite = FALSE))"},{"lang":"stata","heading":"第 7 步：报告清单","code":"* 主表：HAC 标准误（esttab，SSC estout）\nnewey y time_c level trend_after, lag(1)\nestimates store its_main\nesttab its_main, b(%9.2f) se(%9.2f) p(%9.3f) stats(N r2, fmt(%9.0f %9.3f)) ///\n    title(\"表 1：ITS 分段回归（Newey-West 标准误，lag(1)）\") ///\n    addnotes(\"干预时点：第 25 个月；time_c 为干预时点中心化的月份。\")"},{"lang":"r","heading":"第 7 步：报告清单","code":"# 主表：用 HAC 标准误覆盖（texreg；以包文档为准）\nlibrary(texreg)\nhac_se <- sqrt(diag(NeweyWest(m, lag = 1, prewhite = FALSE)))\nscreenreg(m, override.se = hac_se,\n          custom.model.names = \"ITS 分段回归（HAC 标准误）\")"}],"status":"教学模板 / 原有代码摘录；本轮未在 R 或 Stata 执行，需核对依赖与变量"}},{"id":"studio-methods:longitudinal-studio","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"纵向轨迹、混合模型与多时点作图","summary":"重复测量应处理个体内相关；轨迹图、混合效应和 GEE 的目标不同。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"longitudinal-studio","title":"纵向轨迹、混合模型与多时点作图","tags":"轨迹 longitudinal mixed effects GEE 时间序列","use":"重复测量应处理个体内相关；轨迹图、混合效应和 GEE 的目标不同。","url":"skills/longitudinal-studio.md","codes":[{"lang":"r","heading":"R 模板","code":"# 依赖 lme4 / ggplot2；df包含id time y group\nlibrary(lme4); library(ggplot2)\nfit <- lmer(y ~ time * group + (1 + time | id), data=df)\nsummary(fit)\nggplot(df, aes(time,y,group=id,colour=factor(group))) +\n geom_line(alpha=.10) +\n stat_summary(aes(group=group),fun=mean,geom=\"line\",linewidth=1.2) +\n theme_classic()\n# 检查非线性、缺失机制及随机效应结构；均值轨迹不等同潜在类别"},{"lang":"stata","heading":"STATA 模板","code":"* id time y group；先检查时间尺度与重复记录\nisid id time\nxtset id time\nmixed y c.time##i.group || id: time, covariance(unstructured)\nmargins group, at(time=(0 1 2 3))\nmarginsplot\n* 非正态结局可考虑GEE：xtgee y c.time##i.group, family(binomial) link(logit) corr(exchangeable) vce(robust)"}],"status":"教学模板 / 原有代码摘录；本轮未在 R 或 Stata 执行，需核对依赖与变量"}},{"id":"studio-methods:machine-learning-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"机器学习助手（卫生经济与社会医学预测建模，Stata / R）","summary":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"machine-learning-skill","title":"机器学习助手（卫生经济与社会医学预测建模，Stata / R）","tags":"机器学习助手（卫生经济与社会医学预测建模，Stata / R） machine-learning-skill","use":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"skills/machine-learning-skill.md","codes":[{"lang":"stata","heading":"软件准备","code":"* Stata（联网安装一次）\nssc install rforest, replace"},{"lang":"r","heading":"软件准备","code":"# R\ninstall.packages(c(\"glmnet\", \"ranger\", \"xgboost\", \"pROC\", \"shapviz\",\n                   \"caret\", \"rms\", \"mice\", \"Matrix\", \"DALEX\"))"},{"lang":"r","heading":"第 1 步：问题定义与数据准备","code":"# ---- R：生成模拟数据（N = 1000，可全程复现）----\nset.seed(2026)\nn <- 1000\ndat <- data.frame(\n  id        = 1:n,\n  age       = round(rnorm(n, 65, 12)),        # 年龄\n  sex       = rbinom(n, 1, 0.52),             # 1 = 女\n  comorb    = rpois(n, 1.5),                  # 合并症计数\n  insurance = sample(c(\"城职\", \"城居\", \"新农合\"), n, replace = TRUE,\n                     prob = c(0.4, 0.3, 0.3)),\n  los       = rpois(n, 7) + 1                 # 住院天数\n)\ndat$age    <- pmin(pmax(dat$age, 18), 95)\ndat$comorb <- pmin(dat$comorb, 8)\nfor (k in 1:5) dat[[paste0(\"z\", k)]] <- rnorm(n)   # 纯噪声特征\n\n# 结局：同一套真实信号驱动两个结局（分类 + 连续）\nlp <- -5 + 0.03 * dat$age + 0.45 * dat$comorb + 0.12 * dat$los +\n      0.40 * (dat$insurance == \"城职\") + 0.25 * dat$sex\ndat$highcost <- rbinom(n, 1, plogis(lp))      # 高费用 yes/no（约 25%）\ndat$cost <- exp(7.5 + 0.01 * dat$age + 0.18 * dat$comorb + 0.05 * dat$los +\n                0.25 * (dat$insurance == \"城职\") + 0.9 * dat$highcost +\n                rnorm(n, 0, 0.5))             # 总费用（元，右偏）\ntable(dat$highcost); summary(dat)\n\n# ---- 人为制造 10% MCAR 缺失（演示缺失处理用）----\nset.seed(2)\ndat$comorb[sample(n, 100)] <- NA\n\n# ---- 70 / 15 / 15 拆分 ----\nset.seed(3)\ndat$split <- sample(rep(1:3, c(700, 150, 150)))   # 1=训练 2=验证 3=测试\ntr <- dat[dat$split == 1, ]; va <- dat[dat$split == 2, ]; te <- dat[dat$split == 3, ]\n\n# ---- 缺失值处理：用训练集中位数插补三组（演示；正式研究用 MICE 且在 CV 折内）----\nm_med <- median(tr$comorb, na.rm = TRUE)\ntr$comorb <- ifelse(is.na(tr$comorb), m_med, tr$comorb)\nva$comorb <- ifelse(is.na(va$comorb), m_med, va$comorb)\nte$comorb <- ifelse(is.na(te$comorb), m_med, te$comorb)\n\n# 多重插补示例（正式研究用；注意应在 CV 折内进行）\n# library(mice)\n# imp <- mice(dat[, c(\"age\",\"sex\",\"comorb\",\"los\",\"highcost\",\"cost\")], m = 5, seed = 4)\n# fit <- with(imp, glm(highcost ~ age + sex + comorb + los, family = binomial))\n# pool(fit)\n\n# ---- 标准化：只在训练集上计算均值 / SD，再套用到三组 ----\nfor (v in c(\"age\", \"comorb\", \"los\")) {\n  m <- mean(tr[[v]]); s <- sd(tr[[v]])\n  tr[[paste0(v, \"_s\")]] <- (tr[[v]] - m) / s\n  va[[paste0(v, \"_s\")]] <- (va[[v]] - m) / s\n  te[[paste0(v, \"_s\")]] <- (te[[v]] - m) / s\n}"},{"lang":"stata","heading":"第 1 步：问题定义与数据准备","code":"* ---- Stata：生成同一套模拟数据 ----\nclear\nset seed 2026\nset obs 1000\ngen id = _n\ngen age = round(rnormal(65, 12))\nreplace age = min(max(age, 18), 95)\ngen sex = rbinomial(1, 0.52)\ngen comorb = min(rpoisson(1.5), 8)\ngen u = runiform()\ngen ins = cond(u < 0.4, 1, cond(u < 0.7, 2, 3))   // 1=城职 2=城居 3=新农合\nlabel define inslab 1 \"城职\" 2 \"城居\" 3 \"新农合\"\nlabel values ins inslab\ngen los = rpoisson(7) + 1\nforvalues k = 1/5 {\n    gen z`k' = rnormal()                         // 纯噪声特征\n}\ngen lp = -5 + 0.03*age + 0.45*comorb + 0.12*los + 0.40*(ins == 1) + 0.25*sex\ngen highcost = rbinomial(1, invlogit(lp))\ngen cost = exp(7.5 + 0.01*age + 0.18*comorb + 0.05*los + 0.25*(ins == 1) + 0.9*highcost + rnormal(0, 0.5))\ngen lncost = ln(cost)\ntab highcost\n\n* ---- 人为制造 10% MCAR 缺失 ----\nset seed 2026\nreplace comorb = . if runiform() < 0.10\n\n* ---- 70 / 15 / 15 拆分（1=训练 2=验证 3=测试）----\nsplitsample, generate(svar, replace) split(0.70 0.15 0.15) rseed(3)\ntab svar\n\n* ---- 缺失值处理：训练集中位数插补（演示；正式研究用 mi impute chained）----\nsummarize comorb if svar == 1, detail\nreplace comorb = r(p50) if missing(comorb)\n\n* 多重插补示例（正式研究用）\n* mi set mlong\n* mi register imputed comorb\n* mi impute chained (pmm) comorb = age sex los highcost cost, add(5) rseed(4)\n\n* ---- 标准化：以训练集均值 / SD 为基准 ----\nforeach v in age comorb los {\n    quietly summarize `v' if svar == 1\n    gen `v'_s = (`v' - r(mean)) / r(sd)\n}"},{"lang":"r","heading":"第 2 步：基线模型与变量筛选","code":"# ---- 模型矩阵（glmnet 不接受因子，需 model.matrix；含噪声变量观察收缩效果）----\nlibrary(glmnet)\nxvars <- c(\"age_s\", \"sex\", \"comorb_s\", \"los_s\", \"z1\", \"z2\", \"z3\", \"z4\", \"z5\", \"insurance\")\nf <- as.formula(paste(\"~\", paste(xvars, collapse = \" + \")))\nx_tr <- model.matrix(f, data = tr)[, -1]\nx_va <- model.matrix(f, data = va)[, -1]\nx_te <- model.matrix(f, data = te)[, -1]\ny_tr <- tr$highcost; y_te <- te$highcost\n\n# ---- 基线 logistic（不收缩，作为及格线）----\nbase <- glm(highcost ~ age_s + sex + comorb_s + los_s + insurance + z1 + z2 + z3 + z4 + z5,\n            data = tr, family = binomial)\nsummary(base)\n\n# 备选：逐步回归（仅作敏感性分析）\n# library(MASS)\n# stepAIC(base, direction = \"both\", trace = FALSE)\n\n# ---- 基线线性回归（连续费用，取对数）----\nlm_base <- lm(log(cost) ~ age_s + sex + comorb_s + los_s + insurance, data = tr)\nsummary(lm_base)\n\n# ---- LASSO（alpha = 1），10 折 CV 选 λ ----\nset.seed(7)\ncv_lasso <- cv.glmnet(x_tr, y_tr, family = \"binomial\", alpha = 1, nfolds = 10)\nplot(cv_lasso)                                # CV 误差曲线（两条虚线 = lambda.min / 1se）\ncoef(cv_lasso, s = \"lambda.min\")              # 噪声变量 z1–z5 应为 0\ncoef(cv_lasso, s = \"lambda.1se\")              # 更保守（1-SE 法则，模型更小）\n\n# ---- 弹性网（alpha = 0.5）----\nset.seed(8)\ncv_en <- cv.glmnet(x_tr, y_tr, family = \"binomial\", alpha = 0.5, nfolds = 10)\ncoef(cv_en, s = \"lambda.1se\")\n\n# ---- λ 路径图（系数随 log(λ) 增大被收缩至 0 的轨迹）----\nplot(cv_lasso$glmnet.fit, xvar = \"lambda\", label = TRUE)\nabline(v = log(cv_lasso$lambda.1se), lty = 2)\n\n# ---- 连续结局版：LASSO 回归（对数费用）----\nset.seed(9)\ncv_lasso_reg <- cv.glmnet(x_tr, log(tr$cost), alpha = 1, nfolds = 10)\ncoef(cv_lasso_reg, s = \"lambda.1se\")"},{"lang":"stata","heading":"第 2 步：基线模型与变量筛选","code":"* ---- 基线 logistic（含噪声变量，作为及格线）----\nlogit highcost age_s sex comorb_s los_s i.insurance z1-z5 if svar == 1\nest store base\n\n* ---- 基线线性回归（对数费用）----\nregress lncost age_s sex comorb_s los_s i.insurance if svar == 1\n\n* ---- LASSO（Stata 16+），CV 选 λ ----\nlasso logit highcost age_s sex comorb_s los_s i.insurance z1-z5 if svar == 1, selection(cv) rseed(7)\ncvplot                                     // CV 均方误差曲线\ncoefpath                                    // λ 路径（系数收缩轨迹）\nlassocoef, display(coef, standardized)      // 选后系数：噪声变量应被剔除\n\n* ---- 自适应 LASSO：先 CV 选 λ，再加自适应权重重估 ----\nlasso, select(adaptive)\nlassocoef, display(coef, standardized)\n\n* ---- 弹性网（Stata 17+）----\nelasticnet logit highcost age_s sex comorb_s los_s i.insurance z1-z5 if svar == 1, selection(cv) alpha(0.5) rseed(8)\nlassocoef, display(coef, standardized)\n\n* ---- 连续结局版：LASSO 回归 ----\nlasso linear lncost age_s sex comorb_s los_s i.insurance z1-z5 if svar == 1, selection(cv) rseed(9)\nlassocoef, display(coef, standardized)"},{"lang":"r","heading":"第 3 步：树模型与集成","code":"# ---- 统一建模数据框（特征 + 结局）----\nfeats <- c(\"age_s\", \"sex\", \"comorb_s\", \"los_s\", \"insurance\",\n           \"z1\", \"z2\", \"z3\", \"z4\", \"z5\", \"highcost\")\ntr_m <- tr[, feats]; va_m <- va[, feats]; te_m <- te[, feats]\n\n# ---- 随机森林（ranger；分类结局须为 factor）----\nlibrary(ranger)\nset.seed(11)\nrf <- ranger(factor(highcost) ~ ., data = tr_m,\n             num.trees = 500, mtry = 3, min.node.size = 10,\n             importance = \"impurity\", probability = TRUE)\nrf                                          # OOB 预测误差\nrf$variable.importance                      # 变量重要性\np_rf_va <- predict(rf, data = va_m)$predictions[, \"1\"]   # 验证集概率（调参用）\np_rf_te <- predict(rf, data = te_m)$predictions[, \"1\"]   # 测试集概率（仅评估一次）\n\n# ---- XGBoost：xgb.cv 单组参数 + 早停 ----\nlibrary(xgboost)\nlibrary(Matrix)\nmtr <- sparse.model.matrix(highcost ~ . - 1, data = tr_m)\nmva <- sparse.model.matrix(highcost ~ . - 1, data = va_m)\nmte <- sparse.model.matrix(highcost ~ . - 1, data = te_m)\ndtrain <- xgb.DMatrix(mtr, label = tr_m$highcost)\ndval   <- xgb.DMatrix(mva, label = va_m$highcost)\ndtest  <- xgb.DMatrix(mte, label = te_m$highcost)\n\nset.seed(12)\ncvx <- xgb.cv(params = list(objective = \"binary:logistic\", eval_metric = \"auc\",\n                            eta = 0.05, max_depth = 3,\n                            subsample = 0.8, colsample_bytree = 0.8),\n              data = dtrain, nrounds = 500, nfold = 5,\n              early_stopping_rounds = 20, verbose = 0)\ncvx$best_iteration                                   # 最佳轮数\nmax(cvx$evaluation_log$test_auc_mean)                # CV AUC\n\n# ---- XGBoost：手动小网格（验证集早停选参）----\ngrid <- expand.grid(eta = c(0.05, 0.1), max_depth = c(2, 3, 4))\nres <- lapply(seq_len(nrow(grid)), function(i) {\n  p <- list(objective = \"binary:logistic\", eval_metric = \"auc\",\n            eta = grid$eta[i], max_depth = grid$max_depth[i],\n            subsample = 0.8, colsample_bytree = 0.8)\n  xgb.train(params = p, data = dtrain, nrounds = 500,\n            watchlist = list(val = dval), early_stopping_rounds = 20, verbose = 0)\n})\nva_auc <- sapply(res, function(m) m$best_score)       # 各组合验证集 AUC\nbest_i <- which.max(va_auc)\ngrid[best_i, ]                                        # 最优参数\nbest_n <- res[[best_i]]$best_iteration                # 最优轮数\n\n# 用最优参数 + 最优轮数在「训练+验证」上重训，测试集只评估一次\nfull <- xgb.DMatrix(rbind(mtr, mva), label = c(tr_m$highcost, va_m$highcost))\nbst <- xgb.train(params = res[[best_i]]$params, data = full, nrounds = best_n)\np_xgb_te <- predict(bst, dtest)\nxgb.importance(model = bst)                           # Gain / Cover 重要性\n\n# ---- 等价写法：caret 网格调参（二选一）----\nlibrary(caret)\nset.seed(13)\ncg <- expand.grid(nrounds = 300, max_depth = c(2, 3, 4), eta = c(0.05, 0.1),\n                  gamma = 0, colsample_bytree = 0.8,\n                  min_child_weight = 1, subsample = 0.8)\nctrl <- trainControl(method = \"cv\", number = 5, classProbs = TRUE,\n                     summaryFunction = twoClassSummary)\nfit_caret <- train(x = tr_m[, -ncol(tr_m)], y = factor(tr_m$highcost),\n                   method = \"xgbTree\", trControl = ctrl, tuneGrid = cg,\n                   metric = \"ROC\")\nfit_caret$bestTune"},{"lang":"stata","heading":"第 3 步：树模型与集成","code":"* ---- 随机森林（rforest，SSC；不支持因子变量写法，先造虚拟变量）----\ntab ins if svar == 1, gen(ins_)\nrforest highcost age_s sex comorb_s los_s ins_2 ins_3 z1-z5 if svar == 1, type(class) iterations(500) numvars(3) seed(11) vimp\n* 预测概率的变量命名与后处理以 help rforest 为准；\n* 调参（numvars / depth / lsize）应在验证集（svar == 2）上比较 AUC 后进行\n\n* ---- 官方替代：梯度提升 boosting（Stata 17+）、svmachines（Stata 18）----\n* boosting highcost age_s sex comorb_s los_s ins_2 ins_3 z1-z5 if svar == 1\n* 语法与调参选项以 help boosting 为准"},{"lang":"r","heading":"第 4 步：模型评估与比较","code":"library(pROC)\n\n# ---- 各模型在测试集的预测概率（基线 / LASSO 出自 glm 与 glmnet）----\np_base_te  <- predict(base, newdata = te, type = \"response\")\np_lasso_te <- drop(predict(cv_lasso, newx = x_te, s = \"lambda.1se\", type = \"response\"))\n# p_rf_te、p_xgb_te 来自第 3 步\n\nroc_base  <- roc(y_te, p_base_te)\nroc_lasso <- roc(y_te, p_lasso_te)\nroc_rf    <- roc(y_te, p_rf_te)\nroc_xgb   <- roc(y_te, p_xgb_te)\n\nauc(roc_base); auc(roc_lasso); auc(roc_rf); auc(roc_xgb)\nci.auc(roc_rf)                                   # bootstrap 95% CI（默认 2000 次）\n\n# ---- 模型比较：DeLong 检验（相关样本）----\nroc.test(roc_rf, roc_base, method = \"delong\")    # RF vs 基线 logistic\nroc.test(roc_xgb, roc_rf, method = \"delong\")     # XGB vs RF\n\n# ---- ROC 曲线叠加 ----\nplot(roc_base, col = \"grey50\", lwd = 2)\nplot(roc_lasso, add = TRUE, col = \"steelblue\", lwd = 2)\nplot(roc_rf, add = TRUE, col = \"firebrick\", lwd = 2)\nplot(roc_xgb, add = TRUE, col = \"darkgreen\", lwd = 2)\nlegend(\"bottomright\", legend = c(\"logistic\", \"LASSO\", \"RF\", \"XGBoost\"),\n       col = c(\"grey50\", \"steelblue\", \"firebrick\", \"darkgreen\"), lwd = 2)\n\n# ---- 阈值（Youden）与敏感性 / 特异性 ----\ncoords(roc_xgb, \"best\", ret = c(\"threshold\", \"sensitivity\", \"specificity\"))\n\n# ---- 校准：Brier 分数 + 校准曲线（截距≈0、斜率≈1 为校准良好）----\nbrier <- function(p, y) mean((p - y)^2)\nbrier(p_base_te, y_te); brier(p_rf_te, y_te); brier(p_xgb_te, y_te)\n\nlibrary(rms)\nval.prob(p_xgb_te, y_te, statloc = FALSE)        # 校准图 + Brier + 校准截距/斜率\n# 校准差 → Platt scaling / isotonic regression 等再校准，或回退简单模型\n\n# ---- 回归（对数费用）：RMSE / MAE / R² ----\np_lm_te   <- predict(lm_base, newdata = te)\np_lreg_te <- drop(predict(cv_lasso_reg, newx = x_te, s = \"lambda.1se\"))\ny_cont <- log(te$cost)\nrmse <- function(p, y) sqrt(mean((p - y)^2))\nmae  <- function(p, y) mean(abs(p - y))\nrmse(p_lm_te, y_cont);   mae(p_lm_te, y_cont)\nrmse(p_lreg_te, y_cont); mae(p_lreg_te, y_cont)\n1 - sum((p_lreg_te - y_cont)^2) / sum((y_cont - mean(y_cont))^2)   # R²"},{"lang":"stata","heading":"第 4 步：模型评估与比较","code":"* ---- 各模型预测概率（基线 / LASSO 用各自 predict 产出）----\nlogit highcost age_s sex comorb_s los_s i.insurance z1-z5 if svar == 1\npredict p_base, pr\nlasso logit highcost age_s sex comorb_s los_s i.insurance z1-z5 if svar == 1, selection(cv) rseed(7)\npredict p_lasso, pr\n* （rforest / boosting 的预测变量按各自 postestimation 命令生成）\n\n* ---- 单模型 ROC 与 AUC ----\nroctab highcost p_base if svar == 3, graph\n* logit 拟合后可用 lroc（含 AUC 与 ROC 图）\n\n* ---- 多模型比较（DeLong 法与 Hanley–McNeil 法，只评估测试集）----\nroccomp highcost p_base p_lasso p_rf p_xgb if svar == 3, graph\n\n* ---- 校准：重拟合检验截距/斜率（应接近 0/1）+ Hosmer–Lemeshow + Brier ----\nlogit highcost p_xgb if svar == 3\nestat gof, group(10)\ngen b2 = (highcost - p_xgb)^2 if svar == 3\nsummarize b2                                // r(mean) 即 Brier 分数\n\n* ---- 回归指标：RMSE / MAE（对数费用）----\ngen e2 = (lncost - p_lreg)^2 if svar == 3\nsummarize e2\ndisplay \"RMSE = \" sqrt(r(mean))\ngen ae = abs(lncost - p_lreg) if svar == 3\nsummarize ae\ndisplay \"MAE = \" r(mean)"},{"lang":"r","heading":"第 5 步：可解释性","code":"library(shapviz)\n\n# ---- SHAP 值（XGBoost，测试集）----\nsv <- shapviz(bst, X_pred = mte)\nsv_importance(sv, kind = \"both\")                 # 平均|SHAP|条形图 + 蜂群图\nsv_importance(sv, kind = \"beeswarm\")             # 蜂群图：颜色 = 特征取值方向\nsv_dependence(sv, v = \"age_s\")                   # 依赖图：年龄的非线性贡献\n\n# ---- 单个患者瀑布图 ----\ni_hi <- which.max(p_xgb_te)                      # 测试集中预测风险最高者\nsv_waterfall(sv, row_id = i_hi)\n\n# ---- 随机森林置换重要性（比杂质重要性更稳健）----\nset.seed(14)\nrf_p <- ranger(factor(highcost) ~ ., data = te_m, num.trees = 500,\n               importance = \"permutation\", probability = TRUE)\nrf_p$variable.importance\n\n# ---- 备选：DALEX（模型无关解释）----\n# library(DALEX)\n# ex <- explain(bst, data = as.data.frame(as.matrix(mte)), y = te_m$highcost)\n# plot(model_parts(ex))                        # 置换重要性"},{"lang":"stata","heading":"第 5 步：可解释性","code":"* ---- Stata 侧可解释性 ----\n* rforest 变量重要性：vimp 选项生成的 _rf_v_* 变量（见 help rforest）\n* lasso / elasticnet 的标准化系数即一种可解释性输出\nlassocoef, display(coef, standardized)\n* 局限：Stata 官方目前无 SHAP 实现；完整的 SHAP 分析建议在 R（shapviz）侧完成"}],"status":"教学模板 / 原有代码摘录；本轮未在 R 或 Stata 执行，需核对依赖与变量"}},{"id":"studio-methods:markov-model-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"Markov 模型助手（卫生经济学成本-效果分析，Stata / R）","summary":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"markov-model-skill","title":"Markov 模型助手（卫生经济学成本-效果分析，Stata / R）","tags":"Markov 模型助手（卫生经济学成本-效果分析，Stata / R） markov-model-skill","use":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"skills/markov-model-skill.md","codes":[{"lang":"stata","heading":"软件准备","code":"* Stata：无需额外安装；确认版本 ≥ 17\nversion"},{"lang":"r","heading":"软件准备","code":"# R\ninstall.packages(c(\"heemod\", \"expm\"))   # expm 可选"},{"lang":"stata","heading":"第 1 步：定义健康状态与模型结构","code":"* Stata：模型结构骨架（数值在第 2–4 步给出）\n* 状态编号：1 = 无并发症  2 = 卒中后  3 = 死亡（吸收态）\n* 周期 = 1 年；时间范围 T = 30（敏感性情景 40）；起始分布 (1, 0, 0)"},{"lang":"r","heading":"第 1 步：定义健康状态与模型结构","code":"# R：结构参数集中定义，供后续各步复用\nstates       <- c(\"无并发症\", \"卒中后\", \"死亡\")\ncycle_length <- 1        # 年\nT            <- 30       # 时间范围（年）；敏感性情景取 40\ninit         <- c(1, 0, 0)   # 起始：100% 无并发症\nnames(init)  <- states"},{"lang":"stata","heading":"第 2 步：转移概率的来源与估计","code":"* Stata：率 → 概率（t 为周期长度，按年）\ndisplay 1 - exp(-0.02*1)        // 卒中年发生率 0.02 → 年概率 = 0.0198013\ndisplay 1 - exp(-0.10*1)        // 卒中后死亡年率 0.10 → 年概率 = 0.0951626\ndisplay 1 - exp(-0.02*0.5)      // 半年概率 = 0.0099502\n* 概率 → 率（反向）\ndisplay -ln(1 - 0.0198013)/1\n* 由 5 年生存率 0.80 反推年率与年死亡概率（指数生存假设）\ndisplay -ln(0.80)/5             // 年死亡率 = 0.0446287\ndisplay 1 - exp(-(-ln(0.80)/5)) // 年死亡概率 = 0.0436481\n* 周期长度换算：1 年概率 0.0198013 → 3 个月（1/4 年）概率\ndisplay 1 - (1-0.0198013)^(1/4)"},{"lang":"r","heading":"第 2 步：转移概率的来源与估计","code":"# R：同样的换算\n1 - exp(-0.02*1)                    # 0.01980133\n1 - exp(-0.10*1)                    # 0.09516258\n-log(1 - 0.01980133)                # 概率 → 率\nS5 <- 0.80\nr_annual <- -log(S5)/5              # 5 年生存率反推年率\n1 - exp(-r_annual)                  # 年死亡概率\n# 周期长度换算\n1 - (1 - 0.01980133)^(1/4)"},{"lang":"stata","heading":"第 3 步：构建转移矩阵","code":"* Stata：转移矩阵（策略 A；行：无并发症 / 卒中后 / 死亡）\nmatrix PA = (0.9701987, 0.0198013, 0.0100000 \\ ///\n             0.0000000, 0.9048374, 0.0951626 \\ ///\n             0.0000000, 0.0000000, 1.0000000)\nmatrix list PA, format(%9.4f)\n\n* Mata 校验：行和与 20 年状态分布\nmata:\nPA = (0.9701987, 0.0198013, 0.010 \\ 0, 0.9048374, 0.0951626 \\ 0, 0, 1)\nrowsum(PA)                     // 应为 (1, 1, 1)'\n(1, 0, 0) * PA^20              // 20 年后分布：非负且和为 1\nend"},{"lang":"r","heading":"第 3 步：构建转移矩阵","code":"# R：转移矩阵（策略 A 与 B）\nPA <- matrix(c(0.9701987, 0.0198013, 0.0100000,\n               0.0000000, 0.9048374, 0.0951626,\n               0.0000000, 0.0000000, 1.0000000),\n             nrow = 3, byrow = TRUE)\nPB <- matrix(c(0.9760975, 0.0139025, 0.0100000,\n               0.0000000, 0.9048374, 0.0951626,\n               0.0000000, 0.0000000, 1.0000000),\n             nrow = 3, byrow = TRUE)\nrownames(PA) <- colnames(PA) <- c(\"无并发症\", \"卒中后\", \"死亡\")\nrownames(PB) <- colnames(PB) <- c(\"无并发症\", \"卒中后\", \"死亡\")\nrowSums(PA)                     # 1 1 1\nrowSums(PB)                     # 1 1 1\n# 20 年后分布（循环连乘；也可用 expm::`%^%`）\ntrace <- c(1, 0, 0)\nfor (i in 1:20) trace <- trace %*% PA\ntrace"},{"lang":"stata","heading":"第 4 步：成本与健康效用参数","code":"* Stata：由均值 / 区间反推 PSA 分布参数（矩法）\n* beta：效用 0.85、sd 0.03 → alpha、beta\ndisplay 0.85*(0.85*(1-0.85)/0.03^2 - 1)          // alpha ≈ 119.57\ndisplay (1-0.85)*(0.85*(1-0.85)/0.03^2 - 1)      // beta  ≈ 21.10\n* gamma：成本 3000、sd 600 → shape、scale\ndisplay 3000^2/600^2                              // shape = 25\ndisplay 600^2/3000                                // scale = 120\n* lognormal：率 95% CI [0.015, 0.03] → sdlog\ndisplay (ln(0.03) - ln(0.015)) / (2*1.96)         // sdlog ≈ 0.177"},{"lang":"r","heading":"第 4 步：成本与健康效用参数","code":"# R：同上（矩法）\nbeta_mom  <- function(m, sd) { k <- m*(1-m)/sd^2 - 1; c(alpha = m*k, beta = (1-m)*k) }\ngamma_mom <- function(m, sd) c(shape = m^2/sd^2, scale = sd^2/m)\nbeta_mom(0.85, 0.03)                     # alpha 119.57, beta 21.10\ngamma_mom(3000, 600)                     # shape 25, scale 120\n(log(0.03) - log(0.015)) / (2*1.96)      # sdlog 0.177"},{"lang":"stata","heading":"5.1 Stata 纯循环（完整代码）","code":"* ============ 第 5 步：队列模拟（Stata 纯循环） ============\nlocal T = 30                     // 时间范围（年/周期）\n* 策略 A：常规治疗\nlocal p_hs1 = 1 - exp(-0.02)     // 无并发症→卒中（率 0.02/年）\nlocal p_hd1 = 0.010              // 无并发症→死亡\nlocal p_sd1 = 1 - exp(-0.10)     // 卒中后→死亡（率 0.10/年）\nlocal c_well1 = 3000             // 无并发症年成本\nlocal c_sick1 = 20000            // 卒中后年成本\nlocal u_well1 = 0.85             // 效用\nlocal u_sick1 = 0.62\n* 策略 B：干预（卒中风险降 30%）\nlocal p_hs2 = 1 - exp(-0.02*0.70)\nlocal p_hd2 = 0.010\nlocal p_sd2 = 1 - exp(-0.10)\nlocal c_well2 = 3500             // 无并发症年成本 + 500 元药费\nlocal c_sick2 = 20000\nlocal u_well2 = 0.85\nlocal u_sick2 = 0.62\n\nmatrix R = J(0, 4, .)            // 收集两策略结果：成本/QALY × (5%, 3%)\n\nforvalues s = 1/2 {\n    * ---- 状态占位（行 1..T+1；行 1 为起始分布） ----\n    clear\n    set obs `=`T'+1'\n    gen healthy = .\n    gen sick    = .\n    gen dead    = .\n    replace healthy = 1 in 1       // 起始：100% 无并发症\n    replace sick    = 0 in 1\n    replace dead    = 0 in 1\n    * ---- 转移循环：trace(t+1) = trace(t) × P ----\n    forvalues t = 2/`=`T'+1' {\n        replace healthy = healthy[`t'-1]*(1-`p_hs`s''-`p_hd`s'') in `t'\n        replace sick    = healthy[`t'-1]*`p_hs`s'' + sick[`t'-1]*(1-`p_sd`s'') in `t'\n        replace dead    = dead[`t'-1] + healthy[`t'-1]*`p_hd`s'' + sick[`t'-1]*`p_sd`s'' in `t'\n    }\n    * ---- 每周期成本与 QALY（期初状态） ----\n    gen cost_cyc = healthy*`c_well`s'' + sick*`c_sick`s''\n    gen qaly_cyc = healthy*`u_well`s'' + sick*`u_sick`s''\n    * ---- 贴现 5%：半周期校正权重 1/(1+d)^(n-0.5)，只累加第 1..T 周期 ----\n    gen w05 = 1/(1+0.05)^(_n-0.5) if _n <= `T'\n    gen cost05 = cost_cyc*w05\n    gen qaly05 = qaly_cyc*w05\n    qui sum cost05\n    local c05_`s' = r(sum)\n    qui sum qaly05\n    local q05_`s' = r(sum)\n    * ---- 贴现 3%（敏感性情景） ----\n    gen w03 = 1/(1+0.03)^(_n-0.5) if _n <= `T'\n    gen cost03 = cost_cyc*w03\n    gen qaly03 = qaly_cyc*w03\n    qui sum cost03\n    local c03_`s' = r(sum)\n    qui sum qaly03\n    local q03_`s' = r(sum)\n    * ---- 30 年死亡比例（行 T+1 的 dead） ----\n    local d30_`s' = dead[`=`T'+1']\n    matrix R = R \\ (`c05_`s'', `q05_`s'', `c03_`s'', `q03_`s'')\n}\nmatrix rownames R = A B\nmatrix colnames R = cost5 qaly5 cost3 qaly3\nmatrix list R, format(%12.2f)\ndisplay \"30 年死亡比例：A = \" %6.4f `d30_1' \"；B = \" %6.4f `d30_2'\n* 期望输出：A: cost5≈63186.10, qaly5≈10.7716；B: cost5≈63913.00, qaly5≈11.1113\n*           A: cost3≈80062.67, qaly3≈13.1935；B: cost3≈80588.47, qaly3≈13.6758\n*           30 年死亡比例：A≈0.4894，B≈0.4314"},{"lang":"r","heading":"5.2 R 从头实现（完整代码）","code":"# ============ 第 5 步：队列模拟（R 从头写） ============\nT <- 30\np_hsA <- 1 - exp(-0.02)          # 无并发症→卒中（对照）\np_hsB <- 1 - exp(-0.02*0.70)     # 无并发症→卒中（干预）\np_hd  <- 0.010                   # 背景死亡\np_sd  <- 1 - exp(-0.10)          # 卒中后死亡\nPA <- matrix(c(1-p_hsA-p_hd, p_hsA, p_hd,\n               0,           1-p_sd, p_sd,\n               0,           0,      1), nrow = 3, byrow = TRUE)\nPB <- matrix(c(1-p_hsB-p_hd, p_hsB, p_hd,\n               0,           1-p_sd, p_sd,\n               0,           0,      1), nrow = 3, byrow = TRUE)\n\n# 队列模拟：trace(t+1) = trace(t) %*% P；奖励按周期期初状态、权重 1/(1+d)^(t-0.5)\nsim <- function(P, c_well, c_sick, u_well, u_sick, dr, T = 30) {\n  trace <- matrix(0, nrow = T + 1, ncol = 3)\n  trace[1, ] <- c(1, 0, 0)                      # 起始：100% 无并发症\n  for (t in seq_len(T)) trace[t + 1, ] <- trace[t, ] %*% P\n  w   <- 1 / (1 + dr)^(seq_len(T) - 0.5)        # 半周期校正 + 贴现权重\n  cyc <- trace[seq_len(T), ]                    # 第 1..T 周期的期初分布\n  cost <- sum((cyc[, 1] * c_well + cyc[, 2] * c_sick) * w)\n  qaly <- sum((cyc[, 1] * u_well + cyc[, 2] * u_sick) * w)\n  c(cost = cost, qaly = qaly, p_dead = trace[T + 1, 3])\n}\n\nresA5 <- sim(PA, 3000, 20000, 0.85, 0.62, 0.05, T)\nresB5 <- sim(PB, 3500, 20000, 0.85, 0.62, 0.05, T)\nresA3 <- sim(PA, 3000, 20000, 0.85, 0.62, 0.03, T)\nresB3 <- sim(PB, 3500, 20000, 0.85, 0.62, 0.03, T)\nround(rbind(\"A(5%)\" = resA5, \"B(5%)\" = resB5,\n            \"A(3%)\" = resA3, \"B(3%)\" = resB3), 2)\n# 期望输出（与 5.1 的 Stata 结果一致）：\n#   A(5%): cost=63186.10, qaly=10.77, p_dead=0.4894\n#   B(5%): cost=63913.00, qaly=11.11, p_dead=0.4314\n#   A(3%): cost=80062.67, qaly=13.19；B(3%): cost=80588.47, qaly=13.68"},{"lang":"r","heading":"5.3 R heemod 等价实现","code":"# ============ 第 5 步（heemod 等价实现，heemod ≥ 1.0 API） ============\nlibrary(heemod)\n\np_hsA <- 1 - exp(-0.02); p_hsB <- 1 - exp(-0.02*0.70)\np_hd  <- 0.010; p_sd <- 1 - exp(-0.10)\n\n# 转移矩阵：C = 行内其余概率的补数（1 - 其他项之和），行和自动为 1\nmatA <- define_transition(\n  C, p_hsA, p_hd,\n  0, C,     p_sd,\n  0, 0,     C)\nmatB <- define_transition(\n  C, p_hsB, p_hd,\n  0, C,     p_sd,\n  0, 0,     C)\n\n# 状态值：discount() 在模型内按周期贴现；dispatch_strategy 区分策略成本\n# （策略名 std / intv 须与 run_model 中的名字一致）\nstate_well <- define_state(\n  cost    = discount(dispatch_strategy(std = 3000, intv = 3500), .05),\n  utility = 0.85)\nstate_sick <- define_state(\n  cost    = discount(20000, .05),\n  utility = 0.62)\nstate_dead <- define_state(\n  cost    = 0,\n  utility = 0)\n\nstratA <- define_strategy(transition = matA,\n                          state_well, state_sick, state_dead)\nstratB <- define_strategy(transition = matB,\n                          state_well, state_sick, state_dead)\n\nres <- run_model(\n  std  = stratA,\n  intv = stratB,\n  cycles = 30,\n  init   = c(1, 0, 0),      # 按 1 人队列（默认 1000 人）\n  cost   = cost,            # 成本取状态值 cost\n  effect = utility,         # 效果取状态值 utility（QALY）\n  method = \"life-table\")    # 半周期校正；默认 \"beginning\" 不做半周期校正\n\nsummary(res, threshold = c(100000, 300000))  # 阈值：1–3 倍人均 GDP（元/QALY）\nget_counts(res)                              # 各周期各状态人数\nplot(res, type = \"counts\", panels = \"by_strategy\")   # 状态分布图\nplot(res, type = \"ce\")                       # 成本-效果平面与效率前沿\n# 注：heemod 的贴现时点约定（discount() 默认首周期不打折）与 5.1/5.2 的\n# (t-0.5) 约定略有差异，结果会有小幅出入；报告时统一约定并注明实现。\n# heemod 1.0 起 run_model 已无 discount 参数，贴现写在 define_state 的\n# discount() 中（旧版 API 见 help(run_model)）。"},{"lang":"stata","heading":"第 6 步：计算 ICER","code":"* ---------- ICER 与 NMB（5% 贴现；R 第 1、2 列） ----------\ndisplay \"策略 A：成本 = \" %9.2f R[1,1] \" 元；QALY = \" %6.4f R[1,2]\ndisplay \"策略 B：成本 = \" %9.2f R[2,1] \" 元；QALY = \" %6.4f R[2,2]\ndisplay \"ΔC = \" %9.2f (R[2,1]-R[1,1]) \" 元；ΔE = \" %6.4f (R[2,2]-R[1,2]) \" QALY\"\ndisplay \"ICER(5%) = \" %9.2f ((R[2,1]-R[1,1])/(R[2,2]-R[1,2])) \" 元/QALY\"\ndisplay \"ICER(3%) = \" %9.2f ((R[2,3]-R[1,3])/(R[2,4]-R[1,4])) \" 元/QALY\"\n* NMB（λ = 100000，1 倍人均 GDP）\nlocal lam = 100000\ndisplay \"NMB_B - NMB_A (λ=100000) = \" %9.2f (`lam'*(R[2,2]-R[1,2]) - (R[2,1]-R[1,1]))\n* 期望输出：ΔC≈726.90，ΔE≈0.3397，ICER(5%)≈2139.80，ICER(3%)≈1090.04\n*           NMB 差 ≈ 33243（λ=100000）"},{"lang":"r","heading":"第 6 步：计算 ICER","code":"# ---------- ICER 与 NMB ----------\ndC   <- resB5[\"cost\"] - resA5[\"cost\"]\ndE   <- resB5[\"qaly\"] - resA5[\"qaly\"]\nc(dC = dC, dE = dE, ICER = dC / dE)   # 期望：dC≈726.90, dE≈0.3397, ICER≈2139.80\n\nlam <- 100000                          # 阈值（1 倍人均 GDP）\nNMB_A <- lam * resA5[\"qaly\"] - resA5[\"cost\"]\nNMB_B <- lam * resB5[\"qaly\"] - resB5[\"cost\"]\nc(NMB_A = NMB_A, NMB_B = NMB_B, dNMB = NMB_B - NMB_A)   # dNMB≈33243\n\n# 象限与决策规则\nif (dE > 0 & dC < 0) {\n  \"东南象限：占优（更便宜且更有效）→ 采纳\"\n} else if (dE < 0 & dC > 0) {\n  \"西北象限：被占优（更贵且更差）→ 拒绝\"\n} else if (dE > 0) {\n  ifelse(dC/dE < lam, \"东北象限：ICER < λ → 采纳\", \"东北象限：ICER ≥ λ → 拒绝\")\n} else {\n  ifelse(dC/dE > lam,\n         \"西南象限：ICER > λ（节省的钱高于损失 QALY 的价值）→ 可采纳\",\n         \"西南象限：ICER ≤ λ → 拒绝\")\n}\n# 本例期望输出：dC>0 且 dE>0 → 东北象限，ICER≈2140 < 100000 → 采纳"},{"lang":"stata","heading":"7.1 单因素敏感性分析（tornado）","code":"* ============ 第 7.1 步：tornado（Stata/Mata） ============\n* 把第 5 步的循环封装成 Mata 函数（敏感性分析需反复调用，Mata 更快）\ncapture mata mata drop markov_ce()\nmata:\nreal colvector markov_ce(real scalar phs, real scalar phd, real scalar psd,\n                         real scalar cw, real scalar cs,\n                         real scalar uw, real scalar us,\n                         real scalar d, real scalar T)\n{\n    real matrix  P, trace, cyc\n    real colvector w, c, q\n    P = ((1-phs-phd, phs, phd) \\ (0, 1-psd, psd) \\ (0, 0, 1))\n    trace = J(T+1, 3, 0)\n    trace[1, 1] = 1\n    for (t = 1; t <= T; t++) trace[t+1, .] = trace[t, .] * P\n    w   = 1 :/ (1+d) :^ ((1::T) :- 0.5)\n    cyc = trace[1..T, .]\n    c   = colsum((cyc[,1]*cw + cyc[,2]*cs) :* w)\n    q   = colsum((cyc[,1]*uw + cyc[,2]*us) :* w)\n    return((c \\ q))\n}\nend\n\nmata:\nphsA = 1-exp(-0.02)\nphsB = 1-exp(-0.014)\nphd  = 0.010\npsd  = 1-exp(-0.10)\nbA = (phsA, phd, psd, 3000, 20000, 0.85, 0.62)    // 对照臂基线（1×7）\nbB = (phsB, phd, psd, 3500, 20000, 0.85, 0.62)    // 干预臂基线\nnm = (\"对照卒中率\", \"干预卒中率\", \"背景死亡\", \"卒中后死亡\",\n      \"无并发症成本\", \"卒中后成本\", \"效用-无并发症\", \"效用-卒中后\")\npair = J(0, 3, .)                                  // 参数序号、低值 ICER、高值 ICER\nfor (i = 1; i <= 8; i++) {\n    loA = bA; loB = bB; hiA = bA; hiB = bB\n    if (i == 1) {\n        loA[1] = bA[1]*0.8; hiA[1] = bA[1]*1.2      // 仅对照臂变\n    }\n    else if (i == 2) {\n        loB[1] = bB[1]*0.8; hiB[1] = bB[1]*1.2      // 仅干预臂变\n    }\n    else {\n        j = i - 1                                    // 共用参数：两臂同变\n        loA[j] = bA[j]*0.8; loB[j] = bB[j]*0.8\n        hiA[j] = bA[j]*1.2; hiB[j] = bB[j]*1.2\n    }\n    ra = markov_ce(loA[1], loA[2], loA[3], loA[4], loA[5], loA[6], loA[7], 0.05, 30)\n    rb = markov_ce(loB[1], loB[2], loB[3], loB[4], loB[5], loB[6], loB[7], 0.05, 30)\n    icer_lo = (rb[1]-ra[1]) / (rb[2]-ra[2])\n    ra = markov_ce(hiA[1], hiA[2], hiA[3], hiA[4], hiA[5], hiA[6], hiA[7], 0.05, 30)\n    rb = markov_ce(hiB[1], hiB[2], hiB[3], hiB[4], hiB[5], hiB[6], hiB[7], 0.05, 30)\n    icer_hi = (rb[1]-ra[1]) / (rb[2]-ra[2])\n    pair = pair \\ (i, icer_lo, icer_hi)\n}\nra = markov_ce(bA[1], bA[2], bA[3], bA[4], bA[5], bA[6], bA[7], 0.05, 30)\nrb = markov_ce(bB[1], bB[2], bB[3], bB[4], bB[5], bB[6], bB[7], 0.05, 30)\nicer0 = (rb[1]-ra[1]) / (rb[2]-ra[2])\n(nm, pair[,2], pair[,3])                           // 打印结果表\nst_matrix(\"TOR\", (pair[,2], pair[,3]))\nst_numscalar(\"r_icer0\", icer0)\nend\n* 期望输出（基例 ICER ≈ 2140）：对照卒中率变化最大（ICER 约 -4734 ~ 37574）；\n* 负值表示该情景下干预占优或落入其他象限，绘图与报告需注明。\n\n* ---- tornado 图（横条 + 基线竖线；y 轴序号对应上述参数顺序） ----\nsvmat TOR\ngen y = _n\ntwoway (rcap TOR1 TOR2 y, horizontal lcolor(navy)) ///\n    (scatter y TOR1) (scatter y TOR2), ///\n    xline(`=scalar(r_icer0)', lpattern(dash) lcolor(red)) ///\n    ylabel(1(1)8) xtitle(\"ICER（元/QALY）\") ytitle(\"参数序号\") ///\n    legend(off) name(tornado, replace)"},{"lang":"r","heading":"7.1 单因素敏感性分析（tornado）","code":"# ============ 第 7.1 步：tornado（R） ============\nphsA <- 1-exp(-0.02); phsB <- 1-exp(-0.014)\nphd <- 0.010; psd <- 1-exp(-0.10)\nbA <- c(phsA, phd, psd, 3000, 20000, 0.85, 0.62)   # 对照臂基线\nbB <- c(phsB, phd, psd, 3500, 20000, 0.85, 0.62)   # 干预臂基线\nnms <- c(\"对照卒中率\", \"干预卒中率\", \"背景死亡\", \"卒中后死亡\",\n         \"无并发症成本\", \"卒中后成本\", \"效用-无并发症\", \"效用-卒中后\")\n\nmakeP <- function(p) matrix(c(1-p[1]-p[2], p[1], p[2],\n                              0, 1-p[3], p[3],\n                              0, 0, 1), nrow = 3, byrow = TRUE)\npair_icer <- function(pA, pB) {                    # 复用第 5.2 步的 sim()\n  rA <- sim(makeP(pA), pA[4], pA[5], pA[6], pA[7], 0.05, 30)\n  rB <- sim(makeP(pB), pB[4], pB[5], pB[6], pB[7], 0.05, 30)\n  (rB[\"cost\"] - rA[\"cost\"]) / (rB[\"qaly\"] - rA[\"qaly\"])\n}\nicer0 <- pair_icer(bA, bB)                         # ≈ 2139.80\n\ntor <- t(sapply(seq_along(nms), function(i) {\n  loA <- bA; loB <- bB; hiA <- bA; hiB <- bB\n  if (i == 1)      { loA[1] <- bA[1]*.8; hiA[1] <- bA[1]*1.2 }\n  else if (i == 2) { loB[1] <- bB[1]*.8; hiB[1] <- bB[1]*1.2 }\n  else {\n    j <- i - 1\n    loA[j] <- bA[j]*.8; loB[j] <- bB[j]*.8\n    hiA[j] <- bA[j]*1.2; hiB[j] <- bB[j]*1.2\n  }\n  c(lo = pair_icer(loA, loB), hi = pair_icer(hiA, hiB))\n}))\ntor_df <- data.frame(param = nms, lo = tor[, \"lo\"], hi = tor[, \"hi\"])\ntor_df\n\n# 基础 R tornado 图（横条 + 基线竖线）\ndotchart(tor_df$lo, labels = tor_df$param,\n         xlim = range(c(tor_df$lo, tor_df$hi, icer0)))\nsegments(tor_df$lo, seq_along(nms), tor_df$hi, seq_along(nms))\nabline(v = icer0, lty = 2, col = \"red\")\n# 负 ICER：表示该情景下落入了占优 / 西南象限，见第 6 步象限规则，勿直接比较大小"},{"lang":"stata","heading":"7.2 概率敏感性分析（PSA）","code":"* ============ 第 7.2 步：PSA（Stata：rbeta/rgamma/rnormal 循环） ============\n* 分布依据：概率→beta；率/RR→lognormal（exp(rnormal)）；成本→gamma；效用→beta\n* 注：1000 次 × 30 周期的 .do 循环较慢（数分钟）；可改调第 7.1 步的\n* Mata markov_ce() 提速（逻辑完全相同）。\nset seed 2026\nlocal N = 1000\nlocal T = 30\nmatrix PSA = J(0, 4, .)                    // 每行：cA qA cB qB\nforvalues i = 1/`N' {\n    * ---- 参数抽样 ----\n    local p_hsA = 1 - exp(-exp(rnormal(ln(0.02), 0.20)))\n    local p_hsB = 1 - exp(-exp(rnormal(ln(0.02), 0.20))*exp(rnormal(ln(0.70), 0.10)))\n    local p_hd  = rbeta(24.74, 2449.26)\n    local p_sd  = 1 - exp(-exp(rnormal(ln(0.10), 0.15)))\n    local cA0   = rgamma(25, 120)\n    local cB0   = `cA0' + rgamma(25, 20)\n    local cS    = rgamma(25, 800)\n    local uW    = rbeta(119.6, 21.1)\n    local uS    = rbeta(57.8, 35.4)\n    * ---- 对照臂队列模拟（同第 5.1 步循环） ----\n    clear\n    set obs `=`T'+1'\n    gen healthy = .\n    gen sick = .\n    gen dead = .\n    replace healthy = 1 in 1\n    replace sick = 0 in 1\n    replace dead = 0 in 1\n    forvalues t = 2/`=`T'+1' {\n        replace healthy = healthy[`t'-1]*(1-`p_hsA'-`p_hd') in `t'\n        replace sick    = healthy[`t'-1]*`p_hsA' + sick[`t'-1]*(1-`p_sd') in `t'\n        replace dead    = dead[`t'-1] + healthy[`t'-1]*`p_hd' + sick[`t'-1]*`p_sd' in `t'\n    }\n    gen w = 1/(1+0.05)^(_n-0.5) if _n <= `T'\n    gen cA_c = (healthy*`cA0' + sick*`cS')*w\n    gen qA_c = (healthy*`uW'  + sick*`uS')*w\n    qui sum cA_c\n    local cA_t = r(sum)\n    qui sum qA_c\n    local qA_t = r(sum)\n    * ---- 干预臂队列模拟 ----\n    replace healthy = 1 in 1\n    replace sick = 0 in 1\n    replace dead = 0 in 1\n    forvalues t = 2/`=`T'+1' {\n        replace healthy = healthy[`t'-1]*(1-`p_hsB'-`p_hd') in `t'\n        replace sick    = healthy[`t'-1]*`p_hsB' + sick[`t'-1]*(1-`p_sd') in `t'\n        replace dead    = dead[`t'-1] + healthy[`t'-1]*`p_hd' + sick[`t'-1]*`p_sd' in `t'\n    }\n    gen cB_c = (healthy*`cB0' + sick*`cS')*w\n    gen qB_c = (healthy*`uW'  + sick*`uS')*w\n    qui sum cB_c\n    local cB_t = r(sum)\n    qui sum qB_c\n    local qB_t = r(sum)\n    matrix PSA = PSA \\ (`cA_t', `qA_t', `cB_t', `qB_t')\n}\nmatrix colnames PSA = cA qA cB qB\n\n* ---- 成本-效果散点图 ----\nsvmat PSA, names(col)\ngen dC = cB - cA\ngen dE = qB - qA\ntwoway (scatter dC dE, msize(tiny) mcolor(gs8%35)), ///\n    yline(0, lpattern(dash)) xline(0, lpattern(dash)) ///\n    xtitle(\"增量 QALY\") ytitle(\"增量成本（元）\") ///\n    title(\"PSA 成本-效果散点图 (n=1000)\") name(ce_scatter, replace)\n\n* ---- CEAC ----\nmatrix CEAC = J(0, 2, .)\nforvalues lam = 0(20000)300000 {\n    count if dC - `lam'*dE < 0\n    local pce = r(N)/_N\n    matrix CEAC = CEAC \\ (`lam', `pce')\n}\nsvmat CEAC\nline CEAC2 CEAC1, xtitle(\"阈值 λ（元/QALY）\") ///\n    ytitle(\"P(具有成本效果)\") name(ceac, replace)\n* 期望（示意，与随机种子有关）：本示例各阈值下 P(CE) > 0.99，P(占优) ≈ 0.36"},{"lang":"r","heading":"7.2 概率敏感性分析（PSA）","code":"# ============ 第 7.2 步：PSA（R：基础循环 + 绘图） ============\nset.seed(2026)\nN <- 1000\npsa <- matrix(NA, nrow = N, ncol = 4,\n              dimnames = list(NULL, c(\"cA\", \"qA\", \"cB\", \"qB\")))\nfor (i in 1:N) {\n  r_hs <- rlnorm(1, meanlog = log(0.02), sdlog = 0.20)   # 卒中发生率\n  rr   <- rlnorm(1, meanlog = log(0.70), sdlog = 0.10)   # 干预 RR\n  p_hsA <- 1 - exp(-r_hs)\n  p_hsB <- 1 - exp(-r_hs * rr)\n  p_hd  <- rbeta(1, 24.74, 2449.26)                      # 背景死亡概率\n  p_sd  <- 1 - exp(-rlnorm(1, meanlog = log(0.10), sdlog = 0.15))\n  cA0   <- rgamma(1, shape = 25, scale = 120)            # 注意：scale= 必须显式\n  cB0   <- cA0 + rgamma(1, shape = 25, scale = 20)       #（默认第二参数是 rate）\n  cS    <- rgamma(1, shape = 25, scale = 800)\n  uW    <- rbeta(1, 119.6, 21.1)\n  uS    <- rbeta(1, 57.8, 35.4)\n  PAi <- matrix(c(1-p_hsA-p_hd, p_hsA, p_hd,\n                  0, 1-p_sd, p_sd,\n                  0, 0, 1), nrow = 3, byrow = TRUE)\n  PBi <- matrix(c(1-p_hsB-p_hd, p_hsB, p_hd,\n                  0, 1-p_sd, p_sd,\n                  0, 0, 1), nrow = 3, byrow = TRUE)\n  rA <- sim(PAi, cA0, cS, uW, uS, 0.05, 30)              # 复用第 5.2 步的 sim()\n  rB <- sim(PBi, cB0, cS, uW, uS, 0.05, 30)\n  psa[i, ] <- c(rA[\"cost\"], rA[\"qaly\"], rB[\"cost\"], rB[\"qaly\"])\n}\nhead(psa)\n\ndC <- psa[, \"cB\"] - psa[, \"cA\"]\ndE <- psa[, \"qB\"] - psa[, \"qA\"]\n\n# 成本-效果散点图（含象限线与阈值线）\nplot(dE, dC, xlab = \"增量 QALY\", ylab = \"增量成本（元）\",\n     main = \"PSA 成本-效果散点图 (n = 1000)\",\n     pch = 16, col = rgb(0, 0, 0, 0.25))\nabline(h = 0, lty = 2); abline(v = 0, lty = 2)\nabline(a = 0, b = 100000, col = \"red\")   # λ = 100000 元/QALY 阈值线\ntext(quantile(dE, 0.05), quantile(dC, 0.95),\n     paste0(\"P(占优) = \", round(mean(dC < 0 & dE > 0), 3)))\n\n# CEAC\nlam_grid <- seq(0, 300000, by = 5000)\nceac <- sapply(lam_grid, function(l) mean(dC - l*dE < 0))\nplot(lam_grid, ceac, type = \"l\", xlab = \"阈值 λ（元/QALY）\",\n     ylab = \"具有成本效果的概率\", main = \"CEAC\")\nabline(h = 0.5, lty = 3); abline(h = 0.95, lty = 3)\n# 期望（示意）：本示例各阈值下 P(CE) > 0.99，P(占优) ≈ 0.36；\n# heemod 集成版 PSA 见 vignette(\"e-probabilistic\", \"heemod\")（define_psa + run_psa）"},{"lang":"stata","heading":"第 8 步：报告与校验","code":"mata:\n// ① 卒中率 → 0：两臂 QALY 应完全相同\nmarkov_ce(0, 0.010, 1-exp(-0.10), 3000, 20000, 0.85, 0.62, 0.05, 30)[2]\nmarkov_ce(0, 0.010, 1-exp(-0.10), 3500, 20000, 0.85, 0.62, 0.05, 30)[2]\n//   期望输出：两值相同（≈ 12.0320）\n// ② 卒中率 = 0 且死亡率 = 1：第 1 周期后全灭，仅剩第 1 周期奖励\nmarkov_ce(0, 1, 1, 3000, 20000, 0.85, 0.62, 0.05, 30)\n//   期望输出：cost ≈ 2927.70（3000×0.9759），qaly ≈ 0.8295（0.85×0.9759）\n// ③ 效用全 = 1：QALY 应等于贴现生命年（≈ 13.0549）\nmarkov_ce(1-exp(-0.02), 0.010, 1-exp(-0.10), 0, 0, 1, 1, 0.05, 30)[2]\nend"},{"lang":"r","heading":"第 8 步：报告与校验","code":"# R：同样的极端值检验（复用 sim() 与 makeP()）\n# ① 卒中率 → 0：两臂应完全相同\nP0 <- makeP(c(0, phd, psd))\nr01 <- sim(P0, 3000, 20000, 0.85, 0.62, 0.05)\nr02 <- sim(P0, 3500, 20000, 0.85, 0.62, 0.05)\nround(c(qaly_A = r01[\"qaly\"], qaly_B = r02[\"qaly\"], equal = r01[\"qaly\"] == r02[\"qaly\"]), 4)\n# ② 卒中率 = 0 且死亡率 = 1：第 1 周期后全灭\nsim(makeP(c(0, 1, 1)), 3000, 20000, 0.85, 0.62, 0.05)\n# ③ 效用全 = 1：QALY = 贴现生命年\nsim(makeP(c(phsA, phd, psd)), 0, 0, 1, 1, 0.05)[\"qaly\"]"}],"status":"教学模板 / 原有代码摘录；本轮未在 R 或 Stata 执行，需核对依赖与变量"}},{"id":"studio-methods:meta-analysis-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"Meta 分析助手（系统评价效应合并，Stata / R）","summary":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"meta-analysis-skill","title":"Meta 分析助手（系统评价效应合并，Stata / R）","tags":"Meta 分析助手（系统评价效应合并，Stata / R） meta-analysis-skill","use":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"skills/meta-analysis-skill.md","codes":[{"lang":"stata","heading":"软件准备","code":"* Stata（联网安装一次；metafunnel 一般随 metan 附带，缺失时单独安装）\nssc install metan, replace\nssc install metareg, replace\nssc install metabias, replace"},{"lang":"r","heading":"软件准备","code":"# R\ninstall.packages(c(\"meta\", \"metafor\"))"},{"lang":"stata","heading":"第 1 步：研究问题与检索（PICO + PRISMA）","code":"* Stata：示例数据录入（8 项 RCT，虚构演示数据）\n* et/nt = 试验组住院事件数/总数；ec/nc = 对照组住院事件数/总数\n* m_t/sd_t/m_c/sd_c = 两组 HbA1c 变化值(%)的均值/标准差\n* mode：1 = 个案管理+电话随访，0 = 单纯电话随访（第 5 步亚组用）\nclear\ninput str10 study year et nt ec nc m_t sd_t m_c sd_c mode\n\"Zhang\" 2018 45 150 68 150 -1.0  0.7  -0.30 0.8  1\n\"Li\"    2019 40 140 64 140 -1.1  0.75 -0.25 0.85 1\n\"Wang\"  2019 60 150 62 150 -0.4  0.8  -0.25 0.75 0\n\"Chen\"  2020 38 130 60 128 -0.9  0.7  -0.20 0.75 1\n\"Liu\"   2021 64 160 62 155 -0.35 0.85 -0.30 0.8  0\n\"Zhao\"  2022 36 100 37 100 -0.5  0.8  -0.30 0.8  0\n\"Sun\"   2023 48 130 50 130 -0.45 0.8  -0.25 0.75 0\n\"Xu\"    2024 42 135 63 132 -1.0  0.75 -0.25 0.8  1\nend\nlabel define mode_lb 1 \"个案管理+电话随访\" 0 \"单纯电话随访\"\nlabel values mode mode_lb"},{"lang":"r","heading":"第 1 步：研究问题与检索（PICO + PRISMA）","code":"# R：同一份示例数据\ndat <- data.frame(\n  study = c(\"Zhang\", \"Li\", \"Wang\", \"Chen\", \"Liu\", \"Zhao\", \"Sun\", \"Xu\"),\n  year  = c(2018, 2019, 2019, 2020, 2021, 2022, 2023, 2024),\n  et = c(45, 40, 60, 38, 64, 36, 48, 42),\n  nt = c(150, 140, 150, 130, 160, 100, 130, 135),\n  ec = c(68, 64, 62, 60, 62, 37, 50, 63),\n  nc = c(150, 140, 150, 128, 155, 100, 130, 132),\n  m_t  = c(-1.0, -1.1, -0.4, -0.9, -0.35, -0.5, -0.45, -1.0),\n  sd_t = c(0.7, 0.75, 0.8, 0.7, 0.85, 0.8, 0.8, 0.75),\n  m_c  = c(-0.3, -0.25, -0.25, -0.2, -0.3, -0.3, -0.25, -0.25),\n  sd_c = c(0.8, 0.85, 0.75, 0.75, 0.8, 0.8, 0.75, 0.8),\n  mode = c(1, 1, 0, 1, 0, 0, 0, 1))   # 1=个案管理+电话随访，0=单纯电话随访"},{"lang":"stata","heading":"第 2 步：效应量与标准误计算","code":"* ---- 手工计算（先核对，再交给批量命令）----\ngen a = et                      // 试验组事件\ngen b = nt - et                 // 试验组非事件\ngen c = ec                      // 对照组事件\ngen d = nc - ec                 // 对照组非事件\ngen lnOR   = ln((a*d)/(b*c))\ngen selnOR = sqrt(1/a + 1/b + 1/c + 1/d)\ngen OR     = exp(lnOR)\ngen loOR   = exp(lnOR - 1.96*selnOR)\ngen hiOR   = exp(lnOR + 1.96*selnOR)\ngen lnRR   = ln((a/nt)/(c/nc))\ngen selnRR = sqrt(1/a - 1/nt + 1/c - 1/nc)\n* 零单元处理：a b c d 各 +0.5 后按上式重算（或 metan ..., peto）\n\n* ---- 连续：MD 与 Hedges' g ----\ngen MD    = m_t - m_c\ngen seMD  = sqrt(sd_t^2/nt + sd_c^2/nc)\ngen sp    = sqrt(((nt-1)*sd_t^2 + (nc-1)*sd_c^2)/(nt+nc-2))\ngen J     = 1 - 3/(4*(nt+nc) - 9)\ngen g     = MD/sp*J\ngen seg   = sqrt((nt+nc)/(nt*nc) + g^2/(2*(nt+nc)))\nlist study year OR loOR hiOR MD seMD g seg\n\n* 单研究核对：esize 逐条 immediate 形式（语法以 help esize 为准）\nesize twosample 150 -1.0 0.7 150 -0.3 0.8, hedgesg"},{"lang":"r","heading":"第 2 步：效应量与标准误计算","code":"# R：手工计算（与 Stata 公式一致）\ndat$a <- dat$et; dat$b <- dat$nt - dat$et\ndat$c <- dat$ec; dat$d <- dat$nc - dat$ec\ndat$lnOR   <- with(dat, log((a*d)/(b*c)))\ndat$selnOR <- with(dat, sqrt(1/a + 1/b + 1/c + 1/d))\ndat$MD   <- dat$m_t - dat$m_c\ndat$seMD <- with(dat, sqrt(sd_t^2/nt + sd_c^2/nc))\nsp <- with(dat, sqrt(((nt-1)*sd_t^2 + (nc-1)*sd_c^2)/(nt+nc-2)))\nJ  <- with(dat, 1 - 3/(4*(nt+nc) - 9))\ndat$g   <- dat$MD/sp*J\ndat$seg <- with(dat, sqrt((nt+nc)/(nt*nc) + g^2/(2*(nt+nc))))\n\n# R：metafor::escalc（由原始数据直接计算，推荐；结果 yi/vi 与手工一致）\nlibrary(metafor)\ndat.or  <- escalc(measure = \"OR\",  ai = et, n1i = nt, ci = ec, n2i = nc,\n                  data = dat, slab = study)\ndat.rr  <- escalc(measure = \"RR\",  ai = et, n1i = nt, ci = ec, n2i = nc,\n                  data = dat, slab = study)\ndat.md  <- escalc(measure = \"MD\",  m1i = m_t, sd1i = sd_t, n1i = nt,\n                  m2i = m_c, sd2i = sd_c, n2i = nc, data = dat, slab = study)\ndat.smd <- escalc(measure = \"SMD\", m1i = m_t, sd1i = sd_t, n1i = nt,\n                  m2i = m_c, sd2i = sd_c, n2i = nc, data = dat, slab = study)\n# 注：escalc 的 \"SMD\" 默认即 Hedges' g（含小样本校正）；参数名以 ?escalc 为准"},{"lang":"stata","heading":"第 3 步：固定效应与随机效应模型","code":"* ---- 官方 meta 命令族（Stata 16+；fixed 为 common 的兼容旧称）----\nmeta set lnOR selnOR, studylabel(study) esize(OR)\nmeta summarize, fixed random                 // Q、I²、tau² + 合并估计\nmeta esize, fixed random(dlaird)             // 合并估计；REML 用 random(reml)\nmeta esize, random(reml)                     // 方法子选项以 help meta esize 为准\n\n* ---- SSC metan（经典）----\n* 二分类四变量顺序 = 试验组事件 试验组总数 对照组事件 对照组总数（不是四格表 a b c d！）\nmetan et nt ec nc, or fixed random label(namevar=study, yearvar=year) sortby(year)\n\n* 连续六变量 = n1 mean1 sd1 n2 mean2 sd2；md 或 hedges（选项以 help metan 为准）\nmetan nt m_t sd_t nc m_c sd_c, md fixed random label(namevar=study, yearvar=year)\nmetan nt m_t sd_t nc m_c sd_c, hedges fixed random label(namevar=study, yearvar=year)"},{"lang":"r","heading":"第 3 步：固定效应与随机效应模型","code":"# R：meta 包\nlibrary(meta)\nm.or <- metagen(TE = lnOR, seTE = selnOR, studlab = study, data = dat,\n                sm = \"OR\", fixed = TRUE, random = TRUE,\n                method.tau = \"REML\", prediction = TRUE)\nm.md <- metacont(n.e = nt, mean.e = m_t, sd.e = sd_t,\n                 n.c = nc, mean.c = m_c, sd.c = sd_c,\n                 studlab = study, data = dat, sm = \"MD\",\n                 fixed = TRUE, random = TRUE, method.tau = \"REML\")\nm.smd <- update(m.md, sm = \"SMD\")            // 默认 Hedges' g\nsummary(m.or)\n\n# 也可直接由四格表计数（metabin，参数名注意 event.e/n.e/event.c/n.c）\nm.or2 <- metabin(event.e = et, n.e = nt, event.c = ec, n.c = nc,\n                 studlab = study, data = dat, sm = \"OR\", method = \"MH\",\n                 fixed = TRUE, random = TRUE, method.tau = \"REML\")"},{"lang":"stata","heading":"第 4 步：森林图","code":"* 官方\nmeta forestplot, esrefline(1) fixed random          // OR 无效线 = 1\n* 连续结局：先 meta set g seg, studylabel(study) esize(hedgesg)，无效线 = 0\nmeta forestplot, esrefline(0) random\ngraph export forest_or.png, replace width(2000)     // 导出高清图\n\n* metan 自带森林图（lcols/rcols 控制列；favours 以 # 分隔左右文字）\nmetan et nt ec nc, or random label(namevar=study, yearvar=year) sortby(year) ///\n    forestplot(lcols(study year) rcols(OR loOR hiOR) favours(慢病管理降低住院 # 常规照护降低住院))"},{"lang":"r","heading":"第 4 步：森林图","code":"# R：meta 包\nforest(m.or, sortvar = year, leftcols = c(\"studlab\", \"year\"),\n       rightcols = c(\"effect\", \"ci\"), test.overall = TRUE)\n\n# R：metafor（atransf = exp 把对数尺度还原为 OR；refline = 1 为无效线）\nres <- rma(yi, vi, data = dat.or, method = \"REML\")\nforest(res, slab = dat.or$study, atransf = exp, refline = 1,\n       xlab = \"OR（对数尺度）\")"},{"lang":"stata","heading":"第 5 步：异质性检验与亚组分析、Meta 回归","code":"* ---- 亚组（mode：1=个案管理+电话随访，0=单纯电话随访）----\n* 官方：subgroup() 森林图 + 分组合并\nmeta forestplot, subgroup(mode) addoverall\nmeta esize if mode == 1, random\nmeta esize if mode == 0, random\n* 或 SSC metan 的 by()\nmetan et nt ec nc, or random by(mode) label(namevar=study, yearvar=year)\n\n* ---- Meta 回归（协变量 year：发表年份趋势）----\nmeta regress year                     // 官方：REML + Knapp–Hartung（以 help meta regress 为准）\nmetareg lnOR year, wsse(selnOR) mm    // SSC metareg；REML 用 metareg ..., wsse(selnOR) reml\n* metareg 输出含 tau² 与 I²_res；语法细节以 help metareg 为准"},{"lang":"r","heading":"第 5 步：异质性检验与亚组分析、Meta 回归","code":"# R：亚组（byvar 须在 metagen 的原始 data 中）\nupdate(m.or, byvar = mode)\nforest(update(m.or, byvar = mode))\n\n# R：meta 回归\nmetareg(m.or, ~ year)                                # meta 包（默认 REML，以 help(metareg) 为准）\nrma(yi, vi, mods = ~ year, data = dat.or, method = \"REML\")   # metafor\nrma(yi, vi, mods = ~ mode, data = dat.or, method = \"REML\")   # 二分类协变量等价于亚组"},{"lang":"stata","heading":"第 6 步：发表偏倚评估","code":"* 官方（Stata 16+/17+/18+）\nmeta funnelplot, esrefline(1)\nconfunnel                       // 轮廓增强漏斗图（Stata 17+；选项以 help confunnel 为准）\nmeta bias, egger begg harbord   // Stata 18+；以 help meta bias 为准\n\n* SSC（metafunnel 一般随 metan 附带；以 ssc describe metan 为准）\nmetafunnel _ES _seES\nmetabias lnOR selnOR, egger     // Egger：效应量与 SE 两变量\nmetabias lnOR selnOR, begg      // Begg 秩相关（选项名以 help metabias 为准）\nmetabias a b c d, harbord       // Harbord：四格表单元 a b c d"},{"lang":"r","heading":"第 6 步：发表偏倚评估","code":"# R：meta 包\nfunnel(m.or)\nmetabias(m.or, method.bias = \"linreg\")   // Egger（加权线性回归）\nmetabias(m.or, method.bias = \"rank\")     // Begg 秩相关\ntf <- trimfill(m.or); summary(tf); funnel(tf)\n\n# R：metafor\nfunnel(res)\nregtest(res, model = \"rma\")              // Egger（meta 回归法）\ntrimfill(res)                            // 输出 k0 与调整后估计"},{"lang":"stata","heading":"第 7 步：报告清单与证据分级（PRISMA 2020 + GRADE）","code":"* 汇总表所需数字（填入报告模板）\nmeta summarize, fixed random\nmeta forestplot, fixed random            // 报告用图；graph export 导出高清格式"},{"lang":"r","heading":"第 7 步：报告清单与证据分级（PRISMA 2020 + GRADE）","code":"# R：一键提取汇总表关键数字（meta 包）\nwith(m.or, c(Q = Q, df.Q = df.Q, p.Q = pval.Q, I2_pct = I2*100,\n             tau2 = tau2, tau = tau, OR_rand = exp(TE.random),\n             lo_rand = exp(lower.random), hi_rand = exp(upper.random),\n             p_rand = pval.random))\nm.or$w.random                            // 各研究随机效应权重（%）"}],"status":"教学模板 / 原有代码摘录；本轮未在 R 或 Stata 执行，需核对依赖与变量"}},{"id":"studio-methods:microsimulation-studio","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"个体微观模拟 · Microsimulation","summary":"从个体状态和病史到竞争事件、成本/QALY、校准验证、共同随机数、MCSE与PSA；含痴呆、衰弱和慢病文献示例。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"microsimulation-studio","title":"个体微观模拟 · Microsimulation","tags":"微观模拟 microsimulation 痴呆 dementia 衰弱 frailty 照护 CHIME MISCAN JFEM 共同随机数 校准 验证 PSA","use":"从个体状态和病史到竞争事件、成本/QALY、校准验证、共同随机数、MCSE与PSA；含痴呆、衰弱和慢病文献示例。","url":"guides/microsimulation-studio.html","codes":[{"lang":"r","heading":"个体微观模拟 · R教学模型","code":"# 虚构四状态教学模型，不读取任何受访者数据。\nsource(\"scripts/microsimulation_demo.R\")\ndemo <- run_demo(n = 5000, cycles = 20, reps = 8, psa_draws = 40,\n                 seed = 20261024, out = \"microsimulation-demo\")\ndemo$comparison\ndemo$mcse"},{"lang":"stata","heading":"个体微观模拟 · Stata模板","code":"* 虚构参数；此模板尚未在Stata执行，使用前核对版本和路径。\ndo \"scripts/microsimulation_demo.do\" \"microsimulation-demo-stata\""}],"status":"原创R教学脚本已运行，29项检查通过；虚构参数。Stata模板未执行；未复现真实数据或论文模型。"}},{"id":"studio-methods:rdd-studio","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"断点回归 · RDD","summary":"适用明确阈值分配规则；检查操纵、连续性、带宽与局部解释。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"rdd-studio","title":"断点回归 · RDD","tags":"断点 regression discontinuity rdrobust","use":"适用明确阈值分配规则；检查操纵、连续性、带宽与局部解释。","url":"skills/rdd-studio.md","codes":[{"lang":"r","heading":"R 模板","code":"# 依赖 rdrobust / rddensity；df 包含 y、running\nlibrary(rdrobust)\nlibrary(rddensity)\ncutoff <- 0 # 替换为真实制度阈值\nfit <- rdrobust(y=df$y, x=df$running, c=cutoff, p=1)\nsummary(fit)\nsummary(rddensity(X=df$running, c=cutoff))\nrdplot(y=df$y, x=df$running, c=cutoff)\n# 模糊断点需另传 fuzzy=df$treatment；不得用高阶全局多项式代替局部识别"},{"lang":"stata","heading":"STATA 模板","code":"* 依赖：ssc install rdrobust / rddensity（自行确认安装）\n* 数据含 y running；阈值 0 仅为模板\nrdrobust y running, c(0) p(1)\nrddensity running, c(0)\nrdplot y running, c(0)\n* 模糊断点：rdrobust y running, c(0) fuzzy(treatment)"}],"status":"教学模板 / 原有代码摘录；本轮未在 R 或 Stata 执行，需核对依赖与变量"}},{"id":"studio-methods:sdid-studio","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"合成双重差分 · Synthetic DID","summary":"结合单位与时间权重；要求适合的面板结构和有效对照。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"sdid-studio","title":"合成双重差分 · Synthetic DID","tags":"合成控制 SDID synthetic difference","use":"结合单位与时间权重；要求适合的面板结构和有效对照。","url":"skills/sdid-studio.md","codes":[{"lang":"r","heading":"R 模板","code":"# 依赖 synthdid；df: unit,time,outcome,treated（处理后为1）\nlibrary(synthdid)\npm <- panel.matrices(df, unit=\"unit\", time=\"time\",\n                     outcome=\"outcome\", treatment=\"treated\")\ntau <- synthdid_estimate(pm$Y, pm$N0, pm$T0)\nprint(tau); plot(tau)\nse <- sqrt(vcov(tau, method=\"placebo\"))\nprint(se)\n# 此模板针对包支持的块状处理，不能直接套用任意交错实施"},{"lang":"stata","heading":"STATA 模板","code":"* 依赖 sdid；balanced panel，treated为实际受处理的单位-时间状态\nsdid outcome unit time treated, vce(placebo) seed(2026)\n* 检查对照池、处理前拟合、同期冲击和推断方法有效性"}],"status":"教学模板 / 原有代码摘录；本轮未在 R 或 Stata 执行，需核对依赖与变量"}},{"id":"studio-methods:sem-qca-qualitative-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"定性方法与结构方程专题（扎根理论 / SEM / 多层SEM / fsQCA，R / Stata）","summary":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"sem-qca-qualitative-skill","title":"定性方法与结构方程专题（扎根理论 / SEM / 多层SEM / fsQCA，R / Stata）","tags":"定性方法与结构方程专题（扎根理论 / SEM / 多层SEM / fsQCA，R / Stata） sem-qca-qualitative-skill","use":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"skills/sem-qca-qualitative-skill.md","codes":[{"lang":"stata","heading":"软件准备","code":"* Stata（联网安装一次）\nssc install fuzzy, replace"},{"lang":"r","heading":"软件准备","code":"# R\ninstall.packages(c(\"lavaan\", \"QCA\", \"quanteda\"))"},{"lang":"r","heading":"专题 1：扎根理论（Grounded Theory）","code":"library(quanteda)\n# 示例访谈（虚构片段，演示用）\ninterviews <- data.frame(\n  id = 1:6,\n  text = c(\n    \"医保报销比例提高以后，我们村慢病患者愿意按时复诊了，负担轻了很多。\",\n    \"村卫生室药品不全，想开的药经常没有，只能去镇上，路远还要排队。\",\n    \"签约的家庭医生上门测血压，老人觉得被重视，依从性好多了。\",\n    \"镇卫生院设备老化，有些检查做不了，来回县里要一整天。\",\n    \"报销手续以前要跑好几趟，现在村卫生室直接结算，方便多了。\",\n    \"宣传不够，很多老人根本不知道慢病门诊能报销多少。\")\n)\ncorp <- corpus(interviews, text_field = \"text\", docid_field = \"id\")\ntoks <- tokens(corp, remove_punct = TRUE)\ndfmat <- dfm(toks)\ntextstat_frequency(dfmat, n = 20)               # 高频词 → 开放编码线索\nfcm(toks, context = \"window\", window = 5) |>    # 词共现 → 主轴编码线索\n  textplot_network(min_freq = 2)"},{"lang":"r","heading":"专题 2：结构方程模型（SEM）","code":"library(lavaan)\nset.seed(2026)\nn <- 300\nHL <- rnorm(n); SE <- 0.6 * HL + rnorm(n, 0, sqrt(1 - 0.36)); AD <- 0.5 * SE + rnorm(n, 0, sqrt(1 - 0.25))\nd <- data.frame(\n  x1 = 0.7 * HL + rnorm(n, 0, 0.7), x2 = 0.8 * HL + rnorm(n, 0, 0.6), x3 = 0.75 * HL + rnorm(n, 0, 0.65),\n  y1 = 0.7 * SE + rnorm(n, 0, 0.7), y2 = 0.8 * SE + rnorm(n, 0, 0.6), y3 = 0.75 * SE + rnorm(n, 0, 0.65),\n  z1 = 0.7 * AD + rnorm(n, 0, 0.7), z2 = 0.8 * AD + rnorm(n, 0, 0.6), z3 = 0.75 * AD + rnorm(n, 0, 0.65))\n\nmodel <- '\n  HL =~ x1 + x2 + x3\n  SE =~ y1 + y2 + y3\n  AD =~ z1 + z2 + z3\n  SE ~ HL\n  AD ~ SE\n'\nfit <- sem(model, data = d)\nsummary(fit, standardized = TRUE, fit.measures = TRUE)\nfitMeasures(fit, c(\"chisq\", \"df\", \"cfi\", \"tli\", \"rmsea\", \"srmr\"))"},{"lang":"stata","heading":"专题 2：结构方程模型（SEM）","code":"* Stata：等价写法（数据需先导入）\nsem (HL -> x1 x2 x3) (SE -> y1 y2 y3) (AD -> z1 z2 z3) ///\n    (SE <- HL) (AD <- SE), method(mlmv) standardized\nestat gof, stats(all)"},{"lang":"r","heading":"专题 3：多层 / 多重嵌套 SEM（Multilevel SEM）","code":"library(lavaan)\nset.seed(2026)\nH <- 30; m <- 20; n <- H * m\nd <- data.frame(hospital = rep(1:H, each = m))\nd$ueff <- rnorm(H, 0, 0.5)[d$hospital]          # 医院随机效应\nd$HL <- 0.4 * d$ueff + rnorm(n)\nd$SE <- 0.5 * d$HL + 0.3 * d$ueff + rnorm(n)\nd$x1 <- 0.7 * d$HL + rnorm(n, 0, 0.7); d$x2 <- 0.8 * d$HL + rnorm(n, 0, 0.6); d$x3 <- 0.75 * d$HL + rnorm(n, 0, 0.65)\nd$y1 <- 0.7 * d$SE + rnorm(n, 0, 0.7); d$y2 <- 0.8 * d$SE + rnorm(n, 0, 0.6); d$y3 <- 0.75 * d$SE + rnorm(n, 0, 0.65)\n\nmodel2 <- '\n  level: 1\n    HLw =~ x1 + x2 + x3\n    SEw =~ y1 + y2 + y3\n    SEw ~ HLw\n  level: 2\n    HLb =~ x1 + x2 + x3\n    SEb =~ y1 + y2 + y3\n    SEb ~ HLb\n'\nfit2 <- sem(model2, data = d, cluster = \"hospital\")\nsummary(fit2)"},{"lang":"stata","heading":"专题 3：多层 / 多重嵌套 SEM（Multilevel SEM）","code":"* Stata：gsem 多层（医院随机截距）；语法细节以 help gsem 为准\ngsem (HL -> x1 x2 x3) (SE -> y1 y2 y3) (SE <- HL M1[hospital]), vce(cluster hospital)\nestat icc"},{"lang":"r","heading":"专题 4：fsQCA 模糊集定性比较分析","code":"library(QCA)\nset.seed(2026)\nn <- 30\nd <- data.frame(\n  score = rnorm(n, 70, 15),          # 成效评分\n  res   = rnorm(n, 20, 8),           # 资源投入\n  staff = rnorm(n, 10, 4),           # 人员配置\n  inc   = rnorm(n, 0.5, 0.2),        # 激励机制\n  it    = rnorm(n, 3, 1.2))          # 信息化\nd <- data.frame(\n  成效   = calibrate(d$score, c(60, 75, 90)),\n  资源   = calibrate(d$res, c(10, 20, 30)),\n  人员   = calibrate(d$staff, c(5, 10, 15)),\n  激励   = calibrate(d$inc, c(0.2, 0.5, 0.8)),\n  信息化 = calibrate(d$it, c(1, 3, 5)))\n\ntt <- truthTable(d, outcome = \"成效\", n.cut = 1, incl.cut = 0.8)\ntt\nsol <- minimize(tt, include = \"?\")   # 中间解\nsol\nXYplot(tt$tt$资源, d$成效, xlab = \"资源\", ylab = \"成效\")   # 必要条件散点（对单一条件判断必要性）"},{"lang":"stata","heading":"专题 4：fsQCA 模糊集定性比较分析","code":"* Stata：SSC 的 fuzzy 包做模糊集校准与操作（以 help fuzzy 为准）\nssc install fuzzy\nfzcalc zy, low(60) cross(75) high(90)"}],"status":"教学模板 / 原有代码摘录；本轮未在 R 或 Stata 执行，需核对依赖与变量"}},{"id":"studio-methods:socialmedicineskills","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"socialmedicineskills —— 社医卫管方法学速查手册（54 条）","summary":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"socialmedicineskills","title":"socialmedicineskills —— 社医卫管方法学速查手册（54 条）","tags":"socialmedicineskills —— 社医卫管方法学速查手册（54 条） socialmedicineskills","use":"完整工作流见方法手册；按步骤使用代码，不把单个片段视为独立分析。","url":"skills/socialmedicineskills.md","codes":[{"lang":"stata","heading":"A1 横断面研究","code":"* ===== Stata =====\nproportion disease                       // 患病率及 CI（默认 Wald）\nci proportions disease, wilson           // Wilson CI，小样本更稳\nglm disease exposed, family(binomial) link(log) eform   // PR；不收敛时换 poisson disease exposed, irr vce(robust)\n\n* ===== R =====\nx <- 45; n <- 300                        # 患病人数与调查人数\nprop.test(x, n, correct = FALSE)         # 患病率及近似 CI\nbinom.test(x, n)                         # 精确 Clopper-Pearson CI\nm <- glm(disease ~ exposed, family = binomial(link = \"log\"), data = d)\nexp(cbind(PR = coef(m), confint.default(m)))   # 患病率比 PR"},{"lang":"stata","heading":"A2 病例对照研究","code":"* ===== Stata =====\ntabulate exposed case, chi2\ncci 40 20 30 70          // 病例暴露/非暴露=40/20，对照暴露/非暴露=30/70 → OR 与 CI\nlogistic case exposed age, or              // 不匹配：多因素校正 OR\nclogit case exposed, group(pairid) or      // 1:1 匹配：条件 logistic\n\n* ===== R =====\nm <- matrix(c(40, 20, 30, 70), nrow = 2)   # 病例暴露/非暴露、对照暴露/非暴露\nfisher.test(m)                             # 精确 OR 与 CI\nglm(case ~ exposed + age, family = binomial(), data = d)   # 校正 OR\nlibrary(survival)\nclogit(case ~ exposed + strata(pairid), data = d)          # 匹配条件 logistic"},{"lang":"stata","heading":"A3 队列研究","code":"* ===== Stata =====\ncs disease exposed, exact                  // 2×2：RR 及 CI\nir disease exposed                         // 发病密度比 IRR\nstset time, failure(disease)               // 人时/生存数据\nstrate exposed                             // 各组人时与发病率\npoisson disease exposed, exposure(time) irr\nstcox exposed age                          // 校正 HR\n\n* ===== R =====\nlibrary(epiR)\nepi.2by2(matrix(c(45, 955, 20, 980), nrow = 2), method = \"cohort.count\")\nglm(disease ~ exposed + offset(log(time)), family = poisson(), data = d)  # IRR\nlibrary(survival)\ncoxph(Surv(time, disease) ~ exposed + age, data = d)                      # HR"},{"lang":"stata","heading":"A4 随机对照试验 RCT","code":"* ===== Stata =====\nset obs 120\nset seed 2026\negen block = seq(), to(30) block(4)       // 30 个区组、组内 4 人\ngen treat = rbinomial(1, 0.5)             // 简单随机（严格 2:2 区组需再抽样）\ntab treat\ncsi 20 10 25 5                            // 干预组事件/非事件=20/10，对照组=25/5 → RD、RR 及 CI；NNT=1/RD\nttest y, by(treat)                        // 连续结局：MD\nregress y treat, vce(robust)\n\n* ===== R =====\nset.seed(2026)\nd$treat <- sample(rep(0:1, 60))           # 120 人简单随机（可用 blockrand 做区组）\nlibrary(epiR)\nepi.2by2(table(d$treat, d$outcome), method = \"cohort.count\")   # RR/RD/NNT\nt.test(y ~ treat, data = d)\nlm(y ~ treat, data = d)"},{"lang":"stata","heading":"A5 准实验设计","code":"* ===== Stata =====\ngen did = treat * post\nregress y treat post did, vce(cluster id)        // did 系数即 DID 估计\nxtset id time\nxtreg y ib0.time#i.treat i.time, fe vce(cluster id)   // 事件研究：检验平行趋势\ngen time_after = max(0, time - t0)               // ITS 分段回归（t0 = 干预时点）\nnewey y time post time_after, lag(1)             // Newey-West 处理自相关\n\n* ===== R =====\nm <- lm(y ~ treat * post, data = d)              # 交互项系数即 DID\nlibrary(sandwich); library(lmtest)\ncoeftest(m, vcov = vcovCL(m, cluster = ~id))     # 聚类稳健标准误\nd$time_after <- pmax(0, d$time - t0)\nits <- lm(y ~ time + post + time_after, data = d)   # ITS 分段回归"},{"lang":"stata","heading":"A6 生态学研究","code":"* ===== Stata =====\nregress rate exposure [aweight = pop]           // 按地区人口加权\ncorr rate exposure\ndstdize deaths pop, by(agegrp) using(stdpop)    // 直接标化；stdpop.dta 含 agegrp 与 pop\n\n* ===== R =====\nm <- lm(rate ~ exposure, data = d, weights = pop)\ncor(d$rate, d$exposure)\nlibrary(epitools)\nageadjust.direct(count = d$deaths, pop = d$pop, stdpop = ref$stdpop)  # 直接标化"},{"lang":"stata","heading":"A7 混合方法研究（定性+定量）","code":"* ===== Stata =====\nttest score, by(group)                 // 定量侧比较\ntab theme group, chi2                  // 定性主题（编码后）与分组交叉：量化整合\n\n* ===== R =====\nlibrary(quanteda)\ndfm <- dfm(tokens(interview_text), remove_punct = TRUE)   # 定性文本：词频辅助主题\ntopfeatures(dfm, 20)\nt.test(score ~ group, data = d)                           # 定量侧\nchisq.test(table(d$theme, d$group))                       # 主题×分组整合"},{"lang":"stata","heading":"A8 德尔菲法（Delphi）","code":"* ===== Stata =====\negen mean_score = mean(score), by(item)\negen sd_score = sd(score), by(item)\ngen cv = sd_score / mean_score\nlist item mean_score cv if mean_score >= 3.5 & cv <= 0.25   // 示例筛选阈值\nssc install concord\nconcord score1-score15                       // 各专家评分变量 → 肯德尔 W\n\n* ===== R =====\nlibrary(dplyr)\nd |> group_by(item) |>\n  summarise(m = mean(score), cv = sd(score)/mean(score)) |>\n  filter(m >= 3.5 & cv <= 0.25)              # 示例筛选阈值\nlibrary(irr)\nw <- kendall(t(as.matrix(xtabs(score ~ expert + item, data = d))))   # 行为条目、列为专家\nw$value                                      # 肯德尔和谐系数 W"},{"lang":"stata","heading":"C1 成本测算","code":"* 成本均数与 bootstrap 95% CI（成本右偏时优于正态 CI）\nbootstrap r(mean), reps(1000) seed(2026): summarize cost\nci means cost"},{"lang":"r","heading":"C1 成本测算","code":"set.seed(2026)\nboot <- replicate(1000, mean(sample(cost, replace = TRUE)))\nmean(cost)                       # 点估计\nquantile(boot, c(.025, .975))    # 95% 百分位置信区间"},{"lang":"stata","heading":"C2 成本-效果分析 CEA 与成本-效用分析 CUA","code":"* ICER 增量计算（arm：1 = 干预，0 = 对照；ttest 按组值升序，组 2 为 arm=1）\nttest c, by(arm)\nscalar dC = r(mu_2) - r(mu_1)\nttest e, by(arm)\nscalar dE = r(mu_2) - r(mu_1)\ndi \"ICER = \" dC / dE"},{"lang":"r","heading":"C2 成本-效果分析 CEA 与成本-效用分析 CUA","code":"dC <- with(dat, mean(c[arm == 1]) - mean(c[arm == 0]))\ndE <- with(dat, mean(e[arm == 1]) - mean(e[arm == 0]))\nset.seed(2026)\nicers <- replicate(1000, with(dat[sample(nrow(dat), replace = TRUE), ],\n              (mean(c[arm==1]) - mean(c[arm==0])) / (mean(e[arm==1]) - mean(e[arm==0]))))\nquantile(icers, c(.025, .975))   # bootstrap ICER 置信区间"},{"lang":"stata","heading":"C3 QALY 与 DALY 计算","code":"* QALY：效用曲线下面积（q 为效用、t 为时点，按 id 与 t 排序）\nbysort id (t): gen qaly_step = (q + q[_n-1]) / 2 * (t - t[_n-1]) if _n > 1\nbysort id: egen qaly = total(qaly_step)\n* DALY：YLL + YLD（yll、yld 按 GBD 公式预先计算）\ngen daly = yll + yld"},{"lang":"r","heading":"C3 QALY 与 DALY 计算","code":"dat <- dat[order(dat$id, dat$t), ]\nqaly <- tapply(seq_len(nrow(dat)), dat$id, function(i) {\n  t <- dat$t[i]; q <- dat$q[i]\n  sum(diff(t) * (head(q, -1) + tail(q, -1)) / 2)     # 梯形面积法\n})"},{"lang":"stata","heading":"C4 预算影响分析 BIA","code":"* 逐年预算影响 = 目标人群 × 渗透率 ×（新方案 − 现行方案）人均年费用\ngen bi = pop * uptake * (c_new - c_cur)\nlist year bi\nsum bi                         // 研究期内合计"},{"lang":"r","heading":"C4 预算影响分析 BIA","code":"bia <- data.frame(year = 1:5, pop = 1e5, uptake = c(.2, .4, .6, .8, .9))\nbia$bi <- bia$pop * bia$uptake * (c_new - c_cur)\nbia                            # 逐年预算影响；sum(bia$bi) 为合计"},{"lang":"stata","heading":"C5 卫生技术评估 HTA 流程","code":"* PSA 决策判断：净货币效益 NMB = λ·ΔE − ΔC，λ 为阈值（如 1–3 倍人均 GDP）\ngen nmb = lambda * (e_new - e_cur) - (c_new - c_cur)\ncount if nmb > 0\ndi \"成本-效果概率 = \" r(N) / _N"},{"lang":"r","heading":"C5 卫生技术评估 HTA 流程","code":"nmb <- lambda * (e_new - e_cur) - (c_new - c_cur)\nmean(nmb > 0)                  # NMB > 0 的比例 → CEAC 上阈值 λ 处的接受概率"},{"lang":"stata","heading":"C6 随机前沿分析 SFA 与数据包络分析 DEA（效率评价）","code":"* SFA：Cobb-Douglas 随机前沿，半正态无效率\nsfcross y x1 x2, distribution(hnormal)\npredict te_sfa, te\n* DEA：投入导向 BCC（先 ssc install dea）\ndea x1 x2 = y, rts(vrs) ort(in)   // 效率得分；选项以 help dea 为准"},{"lang":"r","heading":"C6 随机前沿分析 SFA 与数据包络分析 DEA（效率评价）","code":"library(Benchmarking)\nr_dea <- dea(X = cbind(x1, x2), Y = y, RTS = \"vrs\")    # 投入导向 BCC\neff_dea <- r_dea$eff                                    # Farrell 效率 E ∈ (0,1]\nlibrary(frontier)\neff_sfa <- efficiencies(sfa(log(y) ~ log(x1) + log(x2), data = dat))"},{"lang":"stata","heading":"C7 医保支付标准测算（DRG/DIP 权重与点值测算思路）","code":"* DRG 权重：病组例均费用 ÷ 全样本例均费用\nbysort drg: egen fee_drg = mean(fee)\negen fee_all = mean(fee)\ngen rw = fee_drg / fee_all\n* DIP 点值：区域基金总额 ÷ Σ(病种分值 × 病例数)\ngen point_amt = dip_score * n_cases\nsum point_amt\ndi \"每分值金额 = \" fund_budget / r(sum)"},{"lang":"r","heading":"C7 医保支付标准测算（DRG/DIP 权重与点值测算思路）","code":"library(dplyr)\nrw <- dat |> group_by(drg) |> summarise(rw = mean(fee) / mean(dat$fee))\ndip_value <- fund_budget / sum(dat$dip_score * dat$n_cases)   # DIP 点值"},{"lang":"stata","heading":"C8 卫生服务利用公平性（集中指数 CI 与不平等分解）","code":"ssc install conindex, replace\nconindex y, rankvar(r)      // r：按经济状况排序的分数秩；选项以 help conindex 为准"},{"lang":"r","heading":"C8 卫生服务利用公平性（集中指数 CI 与不平等分解）","code":"r  <- (rank(ses) - 0.5) / length(ses)   # 按经济状况排序的分数秩\nci <- 2 * cov(y, r) / mean(y)           # 协方差法集中指数"},{"lang":"stata","heading":"D1 倾向得分匹配（PSM）","code":"* Stata：1:1 近邻匹配估计 ATT（y 结局、t 处理、x1 x2 协变量）\nteffects psmatch (y) (t x1 x2), atet\ntebalance summarize          // 平衡性：SMD 与方差比\ntebalance density            // 共同支撑：倾向得分重叠图"},{"lang":"r","heading":"D1 倾向得分匹配（PSM）","code":"library(MatchIt)\nm <- matchit(t ~ x1 + x2, data = dat, method = \"nearest\")\nmd <- match.data(m)\nlm(y ~ t + x1 + x2, data = md, weights = weights)\ncobalt::love.plot(m, abs = TRUE)   # SMD 平衡图"},{"lang":"stata","heading":"D2 工具变量（IV）","code":"* Stata：2SLS（核心命令）\nivregress 2sls y (t = z), first vce(robust)\nestat firststage            // 一阶段 F，>10 为强工具变量\n* 多工具变量：ivregress 2sls y (t = z1 z2), first，再用 estat overid 做过度识别检验"},{"lang":"r","heading":"D2 工具变量（IV）","code":"library(AER)\nm_iv <- ivreg(y ~ t | z, data = dat)\nsummary(m_iv, diagnostics = TRUE)   # 含弱工具变量诊断"},{"lang":"stata","heading":"D3 断点回归（RDD）","code":"ssc install rdrobust\nssc install rddensity\nrdrobust y x, c(0)          // x 为中心化运行变量\nrdplot y x, c(0)            // RD 图\nrddensity x, c(0)           // 密度操纵检验"},{"lang":"r","heading":"D3 断点回归（RDD）","code":"library(rdrobust)\nrdrobust(y = dat$y, x = dat$x, c = 0)\nrddensity::rddensity(dat$x, c = 0)   # 操纵检验"},{"lang":"stata","heading":"D4 合成控制法（SCM）","code":"ssc install synth\ntsset id time\nsynth y x1 x2 y(1990(1)1999), trunit(1) trperiod(2000) fig\n* 安慰剂检验：对每个对照单元重复 synth 比较效应轨迹（以 help synth 为准）"},{"lang":"r","heading":"D4 合成控制法（SCM）","code":"dp <- Synth::dataprep(foo = dat, predictors = c(\"x1\", \"x2\"), dependent = \"y\",\n                      unit.variable = \"id\", time.variable = \"time\",\n                      treatment.identifier = 1, controls.identifier = 2:20,\n                      time.predictors.prior = 1990:1999, time.optimize.ssr = 1990:1999)\ns1 <- Synth::synth(dp)"},{"lang":"stata","heading":"D5 事件研究法","code":"ssc install reghdfe\nssc install coefplot\nreghdfe y ib(-1).time#i.treat, absorb(id time) vce(cluster id)\ncoefplot, keep(*.time#1.treat)          // 事件研究图"},{"lang":"r","heading":"D5 事件研究法","code":"library(fixest)\nm <- feols(y ~ i(time, treat, ref = -1) | id + time, data = dat)\ncoefplot(m)                              // 事件研究图"},{"lang":"stata","heading":"D6 双重差分（DID）","code":"* Stata：2×2 DID（交互项系数即效应）\nreg y i.treat##i.post, vce(cluster id)"},{"lang":"r","heading":"D6 双重差分（DID）","code":"## R：fixest 双向固定效应 DID\nfeols(y ~ treat*post | id + time, data = dat, cluster = ~id)"},{"lang":"stata","heading":"D7 间断时间序列（ITS）","code":"tsset time\ngen post = (time >= t0)          // t0 = 政策实施期\ngen time_after = (time - t0) * post\nnewey y time post time_after, lag(1)"},{"lang":"r","heading":"D7 间断时间序列（ITS）","code":"m <- lm(y ~ time + post + time_after, data = dat)\nlmtest::coeftest(m, vcov = sandwich::NeweyWest(m, lag = 1))"},{"lang":"stata","heading":"D8 三重差分（DDD）","code":"* Stata：DDD（三重交互项系数即效应）\nreg y i.treat##i.post##i.group, vce(cluster id)"},{"lang":"r","heading":"D8 三重差分（DDD）","code":"## R：DDD 三重交互（主效应被固定效应吸收时仅保留三重交互）\nfeols(y ~ treat:post:group | id + time, data = dat, cluster = ~id)"},{"lang":"stata","heading":"E1 缺失数据与多重插补","code":"* ===== Stata =====\nmi set mlong                                  // 声明多重插补 long 格式\nmi register imputed y x                       // 登记含缺失的待插补变量\nmi impute chained (regress) y (logit) x = z age, add(20) rseed(2026)\n                                              // y 连续用 regress、x 二分类用 logit；z age 为完整辅助变量\nmi estimate: regress y x age                  // 逐集估计 + Rubin 规则合并\n* ===== R =====\nlibrary(mice)\nimp <- mice(data, m = 20, seed = 2026)        # 链式方程多重插补\ndensityplot(imp)                              # 检查插补值分布\nfit <- with(imp, glm(y ~ x + age, family = binomial()))   # 逐集估计\npool(fit)                                     # Rubin 规则合并"},{"lang":"stata","heading":"E2 样本量与统计效能计算","code":"* ===== Stata =====\npower twomeans 100 110, sd(20) power(0.8)    // 两组均数 100 vs 110、SD 20 → 每组 n\npower twomeans 100 110, sd(20) n(64)         // 固定每组 n=64 → 检验效能\npower twoproportions 0.30 0.20, power(0.8)   // 两率 0.30 vs 0.20 → 每组 n\npower oneproportion 0.20 0.25                // 单组率 0.20 对目标 0.25\npower twomeans 100 110, sd(20) power(0.8) nratio(2)   // 组间比 1:2 设计\n* ===== R =====\nlibrary(pwr)\npwr.t.test(d = 0.5, sig.level = 0.05, power = 0.8, type = \"two.sample\")  // 每组 n\npwr.t.test(d = 0.5, sig.level = 0.05, n = 64, type = \"two.sample\")       // 固定 n → 效能\npwr.2p.test(h = ES.h(0.3, 0.2), sig.level = 0.05, power = 0.8)           // 两率\npwr.anova.test(k = 3, f = 0.25, sig.level = 0.05, power = 0.8)           // 多组方差分析\npwr.chisq.test(w = 0.1, df = 1, sig.level = 0.05, power = 0.8)           // 卡方检验"},{"lang":"stata","heading":"E3 LASSO/弹性网与正则化","code":"* ===== Stata =====\nlasso linear y x1-x200, selection(cv) nolog    // 连续结局，CV 选 λ\nlasso logit y x1-x200, selection(cv)           // 二分类结局\n* ===== R =====\ncvfit <- glmnet::cv.glmnet(x, y, family = \"binomial\", alpha = 0.5)   // 弹性网\ncoef(cvfit, s = \"lambda.1se\")                  // 1-SE λ 的系数"},{"lang":"stata","heading":"E4 随机森林与 XGBoost","code":"* ===== Stata =====\n* Stata 无内置实现；可经 PyStata 调用 Python（sklearn/xgboost）\n* ===== R =====\nlibrary(randomForest); library(xgboost); set.seed(2026)\nrf <- randomForest(y ~ ., data = train, ntree = 500, importance = TRUE)   # 分类/回归自动识别\nbst <- xgboost(data = xgb.DMatrix(as.matrix(train_x), label = train_y),\n               nrounds = 100, eta = 0.1, max_depth = 3, objective = \"binary:logistic\")"},{"lang":"stata","heading":"E5 聚类分析（K-means/层次聚类）","code":"* ===== Stata =====\ncluster kmeans x1 x2, k(3) start(krandom(2026)) name(k3)   // K-means（x1 x2 宜先标准化）\ncluster stop, rule(duda)                      // 肘部指标辅助选 K（Duda-Hart）\ncluster wardslinkage x1 x2, name(hc)          // 层次聚类（Ward 法）\ncluster dendrogram hc, labels(id)             // 树状图\ncluster generate g = groups(k3)               // 保存 K-means 分组结果\n* ===== R =====\nset.seed(2026); km <- kmeans(scale(x), centers = 3, nstart = 25)   # K-means（scale 标准化）\nd <- dist(scale(x)); hc <- hclust(d, method = \"ward.D2\")           # 层次聚类（Ward 法）\nplot(hc); table(cutree(hc, k = 3), km$cluster)                     # 树状图；两法结果交叉\nlibrary(cluster)\nmean(silhouette(km$cluster, dist(scale(x)))[, 3])                  # 平均轮廓系数（选 K）"},{"lang":"stata","heading":"E6 贝叶斯统计入门","code":"* ===== Stata =====\nbayes, mcmcsize(10000) rseed(2026): regress y x   // 默认弱信息先验的贝叶斯回归\nbayesgraph diagnostics {y:x}                      // 轨迹/自相关/R-hat 收敛诊断\nbayesstats ess                                   // 有效样本量\nbayesstats summary, credible                     // 后验均值与 95% 可信区间\nbayes, prior({y:x}, normal(0, 10)) rseed(2026): regress y x   // 自定义先验 → 敏感性分析\n* ===== R =====\nlibrary(brms)\nfit <- brm(y ~ x, data = d, chains = 4, iter = 2000, warmup = 1000, seed = 2026)\nsummary(fit)                                 # 后验均值、95% CI、R-hat、ESS\nplot(fit)                                    # 链轨迹与后验密度\nhypothesis(fit, \"x > 0\")                     # 后验概率 P(x > 0)"},{"lang":"stata","heading":"F1 Andersen 卫生服务利用行为模型","code":"* Andersen 三因素变量清单（就诊行为研究示例）\n* 倾向特征 predisposing：age 年龄、female 性别(0/1)、edu 教育年限\n* 使能资源 enabling：lnincome 家庭收入对数、insured 是否参保(0/1)、dist 距最近机构(km)\n* 需要因素 need：sahealth 自评健康(1-5)、chronic 慢病种数\nlogit visit age female edu lnincome insured dist sahealth chronic, or"},{"lang":"stata","heading":"F2 Grossman 健康需求模型","code":"* 健康需求简化式：H = f(工资率 w, 教育 E, 年龄 t, 医疗服务价格 p)\n* 预期：教育、工资系数为正（效率与投资回报），年龄为负（折旧），价格为负\nreg health lnwage edu age age2 lnprice\n* 教育→健康的机制检验：加入知识/行为中介后看 edu 系数变化\nreg health edu age\nreg health edu age knowl healthbehavior"},{"lang":"stata","heading":"F3 健康信念模型 HBM","code":"* HBM 维度量表（Likert 1-5，各维度多题取均值）与预防行为模型\n* perc_sus 感知易感性 | perc_sev 感知严重性 | perc_ben 感知收益 | perc_bar 感知障碍\n* cue 行动线索(0/1) | selfeff 自我效能\nlogit vaccinate perc_sus perc_sev perc_ben perc_bar cue selfeff, or"},{"lang":"stata","heading":"F4 计划行为理论 TPB","code":"* Stata sem：观测题项均值入模（单指标路径模型）\nsem (intention <- att sn pbc) (behavior <- intention pbc), standardized\nestat gof, stats(all)"},{"lang":"r","heading":"F4 计划行为理论 TPB","code":"## R lavaan 等价写法（多题项做潜变量时用）\nlibrary(lavaan)\nmodel <- 'intention ~ att + sn + pbc\n          behavior ~ intention + pbc'\nfit <- sem(model, data = d)\nsummary(fit, standardized = TRUE, fit.measures = TRUE)"},{"lang":"stata","heading":"F5 社会生态学模型","code":"* 个体层：age sex edu | 人际层：fam_support 家庭支持(0-3) | 社区层：park_density 绿地密度\n* 随机截距两水平模型（个体嵌套于社区）\nmixed behavior age sex edu fam_support park_density || community: , mle\n* 跨层交互：社区环境调节个体因素\nmixed behavior age sex edu fam_support c.fam_support#c.park_density || community: , mle"},{"lang":"stata","heading":"F6 健康公平与正义理论（Rawls / Sen 可行能力）","code":"* 集中指数 CI（便利回归法）：按收入排序后，2*σ²_rank*(h/μ) 对 rank 回归\nsort income\ngen rank = _n / _N\nquietly summarize h\nlocal mu = r(mean)\nquietly summarize rank\ngen lhs = 2 * r(Var) * (h / `mu')\nreg lhs rank\n* _b[rank] 即 CI（正=亲富，负=亲穷）；SSC 版：ssc install conindex; conindex h, rankvar(income)"},{"lang":"stata","heading":"F7 健康社会决定因素框架（CSDH）","code":"* 结构层：ses 教育年限/收入/职业 | 中间层：housing 居住条件、smoke 吸烟、stress 心理压力 | 结局：health\n* Oaxaca-Blinder 分解：高/低 SES 组健康差距分解为「禀赋效应 + 系数效应」\nssc install oaxaca\noaxaca health housing smoke stress, by(ses_group)"},{"lang":"stata","heading":"F8 委托代理与供方诱导需求（SID）","code":"* 供方诱导需求简化估计式：y = β0 + β1·beds + β2·need + β3·price + ε\n* β1 > 0 且在控制需要与价格后仍显著 → 诱导需求的证据\nreg visits beds_per1000 chronic lnprice insurance age\n* 稳健性：换供给指标（医生密度）重复\nreg visits doctors_per1000 chronic lnprice insurance age"},{"lang":"r","heading":"G1 扎根理论（Grounded Theory）","code":"library(quanteda)\ncorp <- corpus(data.frame(text = interviews$text, doc_id = interviews$id))\ndfm(corp, remove = stopwords(\"zh\"), remove_punct = TRUE) |>\n  textstat_frequency(n = 30)          # 高频词提示开放编码线索"},{"lang":"stata","heading":"G2 结构方程模型（SEM）","code":"* Stata：三潜变量 SEM\nsem (HL -> x1 x2 x3) (SE -> y1 y2 y3) (AD -> z1 z2 z3) (HL -> SE) (SE -> AD), method(mlmv)\nestat gof"},{"lang":"r","heading":"G2 结构方程模型（SEM）","code":"# R：lavaan 等价写法\nlibrary(lavaan)\nmodel <- ' HL =~ x1 + x2 + x3\n           SE =~ y1 + y2 + y3\n           AD =~ z1 + z2 + z3\n           SE ~ HL\n           AD ~ SE '\nfit <- sem(model, data = dat)\nfitMeasures(fit, c(\"cfi\",\"tli\",\"rmsea\",\"srmr\"))"},{"lang":"stata","heading":"G3 多层 / 多重嵌套 SEM（Multilevel SEM）","code":"* Stata：gsem 多层（医院随机截距）\ngsem (HL -> x1 x2 x3) (SE -> y1 y2 y3) (SE <- HL M1[hospital]), vce(cluster hospital)"},{"lang":"r","heading":"G3 多层 / 多重嵌套 SEM（Multilevel SEM）","code":"# R：lavaan 双层写法\nlibrary(lavaan)\nmodel2 <- '\n  level: 1\n    SEw =~ y1 + y2 + y3\n    SEw ~ HLw\n  level: 2\n    SEb =~ y1 + y2 + y3\n    SEb ~ HLb '\nfit2 <- sem(model2, data = dat, cluster = \"hospital\")"},{"lang":"r","heading":"G4 fsQCA 模糊集定性比较分析","code":"library(QCA)\ndat2 <- data.frame(成效 = calibrate(score, c(60, 75, 90)),\n                   资源 = calibrate(res, c(10, 20, 30)),\n                   人员 = calibrate(staff, c(5, 10, 15)),\n                   激励 = calibrate(inc, c(0.2, 0.5, 0.8)),\n                   信息化 = calibrate(it, c(1, 3, 5)))\ntt <- truthTable(dat2, outcome = \"成效\", n.cut = 1, incl.cut = 0.8)\nminimize(tt, include = \"?\")            # 中间解"},{"lang":"stata","heading":"G4 fsQCA 模糊集定性比较分析","code":"* Stata：SSC 的 fuzzy 包做校准等（以 help fuzzy 为准）\nssc install fuzzy\nfzcalc zy, low(60) cross(75) high(90)"}],"status":"教学模板 / 原有代码摘录；本轮未在 R 或 Stata 执行，需核对依赖与变量"}},{"id":"studio-methods:survival-studio","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"生存分析与竞争风险","summary":"定义时间零点、事件与删失；竞争风险不是简单独立删失。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"survival-studio","title":"生存分析与竞争风险","tags":"生存 Cox Kaplan Meier competing risk","use":"定义时间零点、事件与删失；竞争风险不是简单独立删失。","url":"skills/survival-studio.md","codes":[{"lang":"r","heading":"R 模板","code":"library(survival)\n# event: 1事件 0删失；df为研究者数据\nfit <- coxph(Surv(time,event) ~ exposure + age, data=df)\nsummary(fit); cox.zph(fit)\nplot(survfit(Surv(time,event) ~ exposure,data=df))\n# 竞争事件用累积发生函数/适当竞争风险模型，不将1-KM当作累积发生率"},{"lang":"stata","heading":"STATA 模板","code":"stset time, failure(event==1)\nsts graph, by(exposure)\nstcox exposure age\nestat phtest, detail\n* 若 event==2 为竞争事件，明确目标后考虑：\n* stcrreg exposure age, compete(event==2)"}],"status":"教学模板 / 原有代码摘录；本轮未在 R 或 Stata 执行，需核对依赖与变量"}},{"id":"studio-methods:network-meta-analysis-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"网状 Meta 分析","summary":"传递性、网络结构、多臂相关、直接/间接诊断与证据确定性。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"network-meta-analysis-skill","title":"网状 Meta 分析","tags":"NMA 证据综合","use":"传递性、网络结构、多臂相关、直接/间接诊断与证据确定性。","url":"guides/network-meta-analysis-skill.html","codes":[{"lang":"r","heading":"R：可复现的玩具网络","code":"library(netmeta)\nd <- data.frame(\n  study = paste0(\"trial\", 1:6),\n  t1 = c(\"education\",\"digital\",\"family\",\"digital\",\"family\",\"family\"),\n  t2 = c(\"usual\",\"usual\",\"usual\",\"education\",\"education\",\"digital\"),\n  TE = log(c(.85,.72,.76,.87,.91,1.05)),\n  seTE = c(.14,.16,.17,.19,.20,.18)\n)\nfit <- netmeta(TE, seTE, t1, t2, studlab=study, data=d,\n  sm=\"OR\", reference.group=\"usual\", common=FALSE, random=TRUE,\n  method.tau=\"REML\", small.values=\"desirable\")\nsummary(fit)\nnetgraph(fit)\nnetleague(fit)\nnetsplit(fit)\ndecomp.design(fit)\nnetrank(fit, small.values=\"good\")\nsessionInfo()"}],"status":"结构化方法教程；教学参数、数据接口与运行边界见全文"}},{"id":"studio-methods:umbrella-review-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"meta-meta / 伞状综述","summary":"综述家族、原始研究重叠矩阵、CCA、AMSTAR 2/ROBIS与二阶合并边界。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"umbrella-review-skill","title":"meta-meta / 伞状综述","tags":"umbrella meta-meta","use":"综述家族、原始研究重叠矩阵、CCA、AMSTAR 2/ROBIS与二阶合并边界。","url":"guides/umbrella-review-skill.html","codes":[{"lang":"r","heading":"重叠：CCA 的可运行计算","code":"M <- matrix(c(1,1,0, 1,0,1, 0,1,1, 1,1,1), nrow=4, byrow=TRUE)\nrownames(M) <- paste0(\"study\",1:4)\ncolnames(M) <- paste0(\"review\",1:3)\nM <- M[rowSums(M)>0,,drop=FALSE]\nN <- sum(M); r <- nrow(M); c <- ncol(M)\ncca <- if(c>1) (N-r)/(r*c-r) else NA_real_\ncca\npair_overlap <- crossprod(M)\npair_overlap"},{"lang":"stata","heading":"重叠：CCA 的可运行计算","code":"* 当前数据：每行一个去重后的原始研究，rev1-rev3 为0/1\negen n_reviews = rowtotal(rev1-rev3)\nkeep if n_reviews > 0\nquietly count\nscalar r = r(N)\nquietly summarize n_reviews\nscalar N = r(sum)\nscalar c = 3\ndisplay (N-r)/(r*c-r)"}],"status":"结构化方法教程；教学参数、数据接口与运行边界见全文"}},{"id":"studio-methods:target-trial-emulation-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"目标试验模拟 TTE","summary":"七要素协议、time zero、IPW、CCW路线与TARGET报告。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"target-trial-emulation-skill","title":"目标试验模拟 TTE","tags":"目标实验模拟 TTE","use":"七要素协议、time zero、IPW、CCW路线与TARGET报告。","url":"guides/target-trial-emulation-skill.html","codes":[{"lang":"r","heading":"R：固定12个月结局的教学估计","code":"set.seed(20261023)\nn <- 2000\nd <- data.frame(age=rnorm(n,65,8), severity=rnorm(n))\nd$a <- rbinom(n,1,plogis(-.2+.02*(d$age-65)+.5*d$severity))\nd$y <- rbinom(n,1,plogis(-1+.35*d$a+.3*d$severity+.01*(d$age-65)))\npsfit <- glm(a~age+severity,binomial(),data=d)\nd$ps <- predict(psfit,type=\"response\")\nstopifnot(all(d$ps>0 & d$ps<1))\np <- mean(d$a)\nd$w <- ifelse(d$a==1,p/d$ps,(1-p)/(1-d$ps))\nprint(quantile(d$w,c(0,.01,.5,.99,1)))\nprint(with(d, tapply(ps,a,range)))\nprint(sum(d$w)^2/sum(d$w^2)) # 总体有效样本量摘要；也应分组检查\nlibrary(survey)\ndes <- svydesign(ids=~1,weights=~w,data=d)\nfit <- svyglm(y~a,design=des,family=quasibinomial(link=\"identity\"))\ncoef(fit)[\"a\"]\nconfint(fit)[\"a\",]\n# 研究分析还需检查加权前后平衡、权重重叠，并按估计流程考虑bootstrap。"},{"lang":"stata","heading":"R：固定12个月结局的教学估计","code":"* 真实固定时点数据需先明确基线、完整随访和混杂变量\nteffects ipw (y) (a age severity, logit), ate\ntebalance summarize"}],"status":"结构化方法教程；教学参数、数据接口与运行边界见全文"}},{"id":"studio-methods:monte-carlo-simulation-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"蒙特卡洛模拟与 PSA","summary":"ADEMP、偏倚/覆盖/效能、MCSE、PSA与CEAC的教学实验。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"monte-carlo-simulation-skill","title":"蒙特卡洛模拟与 PSA","tags":"Monte Carlo PSA","use":"ADEMP、偏倚/覆盖/效能、MCSE、PSA与CEAC的教学实验。","url":"guides/monte-carlo-simulation-skill.html","codes":[{"lang":"r","heading":"R：两组均值差的完整教学实验","code":"set.seed(20261023)\nB <- 2000; n <- 100; truth <- 0.3\none <- function(){\n  y0 <- rnorm(n,0,1); y1 <- rnorm(n,truth,1.5)\n  z <- t.test(y1,y0) # Welch；估计方向为y1-y0\n  c(est=mean(y1)-mean(y0),lo=z$conf.int[1],hi=z$conf.int[2],p=z$p.value)\n}\nans <- t(replicate(B,one()))\nerr <- ans[,\"est\"]-truth\ncovered <- ans[,\"lo\"]<=truth & ans[,\"hi\"]>=truth\ncoverage <- mean(covered)\nc(bias=mean(err),MCSE_bias=sd(ans[,\"est\"])/sqrt(B),\n  RMSE=sqrt(mean(err^2)),coverage=coverage,\n  MCSE_coverage=sqrt(coverage*(1-coverage)/B),\n  power=mean(ans[,\"p\"]<.05))\n# 将truth设为0并重新生成数据，才能估计该场景的I类错误率。"},{"lang":"stata","heading":"R：两组均值差的完整教学实验","code":"clear\nset seed 20261023\ncapture program drop mc_mean\nprogram define mc_mean, rclass\n    drop _all\n    set obs 200\n    gen byte a = _n > 100\n    gen y = .3*a + rnormal(0,cond(a==1,1.5,1))\n    quietly regress y a, vce(robust)\n    return scalar est = _b[a]\n    return scalar se = _se[a]\n    return scalar covered = abs((_b[a]-.3)/_se[a]) <= invttail(e(df_r),.025)\nend\nsimulate est=r(est) se=r(se) covered=r(covered), reps(2000): mc_mean\nsummarize est se covered\n* Stata示例用稳健回归区间，R示例用Welch区间，二者并非完全同一方法。"},{"lang":"r","heading":"PSA：用增量净收益表达决策","code":"set.seed(20261023)\nB <- 10000\ndelta_cost <- rnorm(B,1200,300)\ndelta_qaly <- rnorm(B,.08,.025)\nlambda <- seq(0,100000,by=5000) # 教学阈值范围，不是政策推荐\nceac <- sapply(lambda,function(k) mean(k*delta_qaly-delta_cost>0))\ndata.frame(lambda,Pr_cost_effective=ceac)"}],"status":"结构化方法教程；教学参数、数据接口与运行边界见全文"}},{"id":"studio-methods:advanced-causal-inference-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"因果推断进阶","summary":"SDID、DML、IPW/MSM与g方法：先定估计目标，再核对识别和诊断。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"advanced-causal-inference-skill","title":"因果推断进阶","tags":"SDID DML","use":"SDID、DML、IPW/MSM与g方法：先定估计目标，再核对识别和诊断。","url":"guides/advanced-causal-inference-skill.html","codes":[{"lang":"r","heading":"SDID：空间与时间一起构造参照","code":"library(synthdid)\ndata(\"california_prop99\")\nz <- panel.matrices(california_prop99)\natt <- synthdid_estimate(z$Y,z$N0,z$T0)\nplot(att)\nse <- sqrt(vcov(att,method=\"placebo\"))\nc(estimate=as.numeric(att),SE=as.numeric(se))"},{"lang":"r","heading":"DML：灵活处理混杂，保留目标参数","code":"library(DoubleML)\nlibrary(mlr3)\nlibrary(mlr3learners)\nset.seed(20261023)\nn <- 600\nd <- data.frame(x1=rnorm(n),x2=rnorm(n))\nd$a <- .5*d$x1+rnorm(n)\nd$y <- .8*d$a+d$x1+d$x2^2+rnorm(n)\nobj <- DoubleMLData$new(d,y_col=\"y\",d_cols=\"a\",x_cols=c(\"x1\",\"x2\"))\nfit <- DoubleMLPLR$new(obj,\n  ml_l=lrn(\"regr.rpart\"),ml_m=lrn(\"regr.rpart\"),n_folds=5)\nfit$fit()\nfit$summary()"},{"lang":"stata","heading":"IPW/MSM与g方法：看清时间顺序","code":"* 仅基线处理、固定时点结局的教学接口，不是纵向MSM\nteffects aipw (y age severity) (a age severity), ate\ntebalance summarize"}],"status":"结构化方法教程；教学参数、数据接口与运行边界见全文"}},{"id":"studio-methods:policy-text-analysis-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"卫生政策文本分析","summary":"三维框架、PMC、主题模型、LLM编码与人工金标准验证。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"policy-text-analysis-skill","title":"卫生政策文本分析","tags":"PMC BERTopic LLM","use":"三维框架、PMC、主题模型、LLM编码与人工金标准验证。","url":"guides/policy-text-analysis-skill.html","codes":[{"lang":"r","heading":"R：对人工/模型标签做基本核验","code":"# 0/1教学标签；真实任务应保留无法判断、缺失及多标签规则\ngold <- c(1,0,1,0,1,0,1,0)\npred <- c(1,0,0,0,1,1,1,0)\ntab <- table(factor(gold,levels=0:1),factor(pred,levels=0:1))\nprecision <- tab[2,2]/sum(tab[,2])\nrecall <- tab[2,2]/sum(tab[2,])\nc(precision=precision,recall=recall,F1=2*precision*recall/(precision+recall))\n# 稀有标签、空分母、地区/年份分层及区间需在正式分析中单独处理。"}],"status":"结构化方法教程；教学参数、数据接口与运行边界见全文"}},{"id":"studio-methods:medical-ai-evaluation-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"医疗 AI 与人工评价","summary":"准确性、一致性、配对比较、校准、安全错误和TRIPOD-LLM。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"medical-ai-evaluation-skill","title":"医疗 AI 与人工评价","tags":"Kappa ICC 医学AI","use":"准确性、一致性、配对比较、校准、安全错误和TRIPOD-LLM。","url":"guides/medical-ai-evaluation-skill.html","codes":[{"lang":"r","heading":"R / Stata核心接口","code":"library(irr)\nratings <- data.frame(r1=c(1,1,0,0,1,0),\n                      r2=c(1,0,0,0,1,1),r3=c(1,1,0,1,1,0))\nkappam.fleiss(ratings)\n# 两分类器在相同病例上正确/错误的配对表\nmcnemar.test(matrix(c(30,4,9,13),nrow=2))\n# 连续评分ICC需明确模型、绝对一致/一致性、单个/平均评分\n# irr::icc(score_matrix, model=\"twoway\",type=\"agreement\",unit=\"single\")"},{"lang":"stata","heading":"R / Stata核心接口","code":"* 两位评审、名义分类\nkap rater1 rater2\n* 该接口是两位评审的Cohen Kappa，不是Fleiss Kappa。"}],"status":"结构化方法教程；教学参数、数据接口与运行边界见全文"}},{"id":"studio-methods:longitudinal-public-data-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"公共数据库与纵向研究","summary":"CHARLS/CLHLS/CFPS口径、抽样、失访、轨迹与竞争风险。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"longitudinal-public-data-skill","title":"公共数据库与纵向研究","tags":"纵向 公共数据库","use":"CHARLS/CLHLS/CFPS口径、抽样、失访、轨迹与竞争风险。","url":"guides/longitudinal-public-data-skill.html","codes":[{"lang":"r","heading":"R：长表结构核验与纵向模型接口","code":"# d为已由代码本核实的长表；此处不假定任何数据库原始变量名\nstopifnot(!anyDuplicated(d[c(\"id\",\"wave\")]))\nwith(d,table(wave,is.na(cognition)))\nlibrary(lme4)\nfit <- lmer(cognition ~ time*baseline_exposure+age0+sex+(1+time|id),data=d)\nsummary(fit)\n# 这是条件均值关联模型；baseline_exposure需先定义，不能直接称因果效应。"},{"lang":"stata","heading":"R：长表结构核验与纵向模型接口","code":"isid id wave\nxtset id wave\nmixed cognition c.time##i.baseline_exposure age0 i.sex || id: time, covariance(unstructured)\n* 若目标是复杂抽样总体估计，需另核对设计权重和模型的兼容性。"}],"status":"结构化方法教程；教学参数、数据接口与运行边界见全文"}},{"id":"studio-methods:simulation-models-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"模拟模型选型","summary":"Markov、微观、DES、系统动力学、ABM：结构、参数、校准与验证。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"simulation-models-skill","title":"模拟模型选型","tags":"微观模拟 ABM DES","use":"Markov、微观、DES、系统动力学、ABM：结构、参数、校准与验证。","url":"guides/simulation-models-skill.html","codes":[{"lang":"r","heading":"一个很小的R排队实验","code":"set.seed(20261023)\nn <- 1000\narrival <- cumsum(rexp(n,rate=4))\nservice <- rexp(n,rate=5)\nstart <- finish <- numeric(n)\nfor(i in seq_len(n)){\n  start[i] <- max(arrival[i],if(i==1) 0 else finish[i-1])\n  finish[i] <- start[i]+service[i]\n}\nwait <- start-arrival\nc(mean_wait=mean(wait),p95_wait=unname(quantile(wait,.95)))"}],"status":"结构化方法教程；教学参数、数据接口与运行边界见全文"}},{"id":"studio-methods:implementation-equity-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"实施科学与健康公平","summary":"CFIR/RE-AIM、可测机制链、定性定量整合和公平性评价。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"implementation-equity-skill","title":"实施科学与健康公平","tags":"实施科学 公平","use":"CFIR/RE-AIM、可测机制链、定性定量整合和公平性评价。","url":"guides/implementation-equity-skill.html","codes":[{"lang":"r","heading":"公平性最小指标","code":"# 教学数据：0/1结局且均值>0；fractional rank按收入从低到高\nd <- data.frame(income=c(1,2,3,4,5),use=c(0,0,1,0,1))\nn <- nrow(d)\nd$r <- (rank(d$income,ties.method=\"average\")-.5)/n\nmu <- mean(d$use)\ncov_pop <- mean((d$use-mu)*(d$r-mean(d$r)))\nCI <- 2*cov_pop/mu\nErreygers <- 4*mu*CI # 仅适用于0/1界限；未加权教学表达\nc(CI=CI,Erreygers=Erreygers)"}],"status":"结构化方法教程；教学参数、数据接口与运行边界见全文"}},{"id":"studio-methods:propensity-score-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"PSM、匹配与加权","summary":"ATE/ATT/ATO、变量选择、共同支持、Love plot与匹配后推断。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"propensity-score-skill","title":"PSM、匹配与加权","tags":"PSM IPW 熵平衡","use":"ATE/ATT/ATO、变量选择、共同支持、Love plot与匹配后推断。","url":"guides/propensity-score-skill.html","codes":[{"lang":"r","heading":"R教学样例：匹配ATT与诊断","code":"library(MatchIt)\nlibrary(cobalt)\nset.seed(20261023)\nn <- 1000\nd <- data.frame(age=rnorm(n,65,8),severity=rnorm(n))\nd$a <- rbinom(n,1,plogis(-.3+.03*(d$age-65)+.5*d$severity))\nd$y <- 100+8*d$a+2*d$severity+.4*d$age+rnorm(n,0,10)\nm <- matchit(a~age+severity,data=d,method=\"nearest\",distance=\"glm\",\n  link=\"linear.logit\",estimand=\"ATT\",ratio=1,caliper=.2,std.caliper=TRUE)\nsummary(m)\nlove.plot(m,thresholds=c(m=.1))\nmd <- match.data(m)\nfit <- lm(y~a,data=md,weights=weights)\nlmtest::coeftest(fit,vcov.=sandwich::vcovCL(fit,cluster=~subclass))\n# 示例为无替换1:1匹配；复杂替换/聚类设计需重新选择推断。"},{"lang":"stata","heading":"R教学样例：匹配ATT与诊断","code":"* ATT（Stata称ATET），a为处理，y为结局\nteffects psmatch (y) (a age severity, logit), atet nneighbor(1)\ntebalance summarize\n* 这是内置估计器示例，不与R卡钳匹配的全部细节完全等价。"}],"status":"结构化方法教程；教学参数、数据接口与运行边界见全文"}},{"id":"studio-methods:staggered-did-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"交错 DID / 分批政策","summary":"ATT(g,t)、C&S、Sun–Abraham、事件窗、对照支持与小簇推断。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"staggered-did-skill","title":"交错 DID / 分批政策","tags":"交错DID csdid","use":"ATT(g,t)、C&S、Sun–Abraham、事件窗、对照支持与小簇推断。","url":"guides/staggered-did-skill.html","codes":[{"lang":"r","heading":"R：软件自带面板的C&S演示","code":"library(did)\ndata(mpdta)\natt <- att_gt(yname=\"lemp\",tname=\"year\",idname=\"countyreal\",\n  gname=\"first.treat\",xformla=~1,data=mpdta,panel=TRUE,\n  control_group=\"nevertreated\",est_method=\"dr\",bstrap=TRUE,cband=TRUE)\nsummary(att)\ndyn <- aggte(att,type=\"dynamic\",min_e=-2,max_e=2)\nsummary(dyn)\nggdid(dyn)\naggte(att,type=\"group\")"},{"lang":"r","heading":"R：软件自带面板的C&S演示","code":"library(fixest)\n# 教学接口：d中never-treated的g按sunab文档编码；不得直接复制did的0编码\n# d$g_sa <- ifelse(d$g==0, max(d$year)+100, d$g)\n# m <- feols(y ~ sunab(g_sa,year,ref.p=-1) | id+year,data=d,cluster=~id)\n# iplot(m)"},{"lang":"stata","heading":"R：软件自带面板的C&S演示","code":"* 第三方csdid；先核对所装版本help csdid，g=0为从未处理\n* ssc install csdid\n* ssc install drdid\ncsdid y x_pre, ivar(id) time(year) gvar(g) method(dripw) cluster(id)\nestat simple\nestat event, window(-2 2)"}],"status":"结构化方法教程；教学参数、数据接口与运行边界见全文"}},{"id":"studio-methods:clustering-analysis-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"聚类分析与分型","summary":"距离、变量尺度、算法、类数、稳定性与外部验证的完整路线。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"clustering-analysis-skill","title":"聚类分析与分型","tags":"K-means 聚类 潜类别","use":"距离、变量尺度、算法、类数、稳定性与外部验证的完整路线。","url":"guides/clustering-analysis-skill.html","codes":[{"lang":"r","heading":"R教学样例","code":"set.seed(20261023)\nX <- scale(iris[,1:4])\nkm <- kmeans(X,centers=3,nstart=50)\ntable(km$cluster)\naggregate(iris[,1:4],list(cluster=km$cluster),mean)\nlibrary(cluster)\nsil <- silhouette(km$cluster,dist(X))\nmean(sil[,\"sil_width\"])\n# iris物种标签未参与建模；本例仅示范接口，不作为患者分型依据。"},{"lang":"stata","heading":"R教学样例","code":"* 仅对选定连续特征标准化；先完成缺失与异常检查\negen z_age = std(age)\negen z_util = std(utilization)\negen z_cost = std(logcost)\nset seed 20261023\ncluster kmeans z_age z_util z_cost, k(3) name(km3)\ntabulate km3"}],"status":"结构化方法教程；教学参数、数据接口与运行边界见全文"}},{"id":"studio-methods:econometric-modeling-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"卫生计量模型选型","summary":"GLM、两部、计数、面板FE/RE/CRE、分位数、IV/RDD和空间模型。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"econometric-modeling-skill","title":"卫生计量模型选型","tags":"计量经济学 模型逻辑","use":"GLM、两部、计数、面板FE/RE/CRE、分位数、IV/RDD和空间模型。","url":"guides/econometric-modeling-skill.html","codes":[{"lang":"r","heading":"两部模型的R接口","code":"# d: cost>=0，a为处理/分组，x为治疗前协变量\nd$anycost <- as.integer(d$cost>0)\npmod <- glm(anycost~a+x,family=binomial(),data=d)\ncmod <- glm(cost~a+x,family=Gamma(link=\"log\"),data=subset(d,cost>0))\nd1 <- d0 <- d; d1$a <- 1; d0$a <- 0\npred_mean <- function(nd) predict(pmod,nd,type=\"response\") *\n  predict(cmod,nd,type=\"response\")\nmean(pred_mean(d1)-pred_mean(d0))\n# 这只是标准化模型差异；因果解释另需识别假设。\n# 区间可对整个两部拟合与标准化过程适当重抽样；聚类数据按簇抽样。"},{"lang":"stata","heading":"两部模型的R接口","code":"glm cost i.a c.x if cost>0, family(gamma) link(log) vce(robust)\nmargins, dydx(a)\npoisson visits i.a c.x, exposure(person_time) vce(robust)\nqreg cost i.a c.x, quantile(.5)"},{"lang":"stata","heading":"面板FE、RE与CRE","code":"xtset id year\nxtreg y x i.year, fe vce(cluster id)\nxtreg y x i.year, re vce(cluster id)\nbysort id: egen xbar = mean(x)\nxtreg y x xbar i.year, re vce(cluster id)"},{"lang":"stata","heading":"IV、RDD与空间模型","code":"ivregress 2sls y x (a=z), vce(robust)\nestat firststage"}],"status":"结构化方法教程；教学参数、数据接口与运行边界见全文"}},{"id":"studio-methods:charls-database-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"CHARLS · 中国健康与养老追踪调查","summary":"中老年健康、经济与家庭追踪；原始模块、Harmonized与第三方清洗版分别建立版本清单。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"charls-database-skill","title":"CHARLS · 中国健康与养老追踪调查","tags":"CHARLS 数据库","use":"中老年健康、经济与家庭追踪；原始模块、Harmonized与第三方清洗版分别建立版本清单。","url":"guides/charls-database-skill.html","codes":[{"lang":"r","heading":"R与Stata：标准化层的最小接口","code":"# d: id=字符个人键, year=实际访谈年, y=已核对结局, x=预定协变量\nstopifnot(!anyNA(d$id), !anyDuplicated(d[c(\"id\", \"year\")]))\nd <- d[order(d$id, d$year), ]\n# 纵向条件关联；不自动等于政策因果效应\nm <- fixest::feols(y ~ x | id + year, cluster = ~id, data = d)\nsummary(m)\n# 按复杂抽样作描述应另用对应设计与权重，而非仅cluster=id。"},{"lang":"stata","heading":"R与Stata：标准化层的最小接口","code":"* id_s 是字符键，pid 仅作当前数据内面板编码，不输出映射。\nassert !missing(id_s, year)\nisid id_s year\negen long pid = group(id_s)\nxtset pid year\nxtreg y x i.year, fe vce(cluster pid)\n* 单波数据不运行此面板流程；先选择合适的survey描述/横断面模型。"}],"status":"数据库专题：版本、变量、权重和分析路线；执行状态见正文"}},{"id":"studio-methods:clhls-database-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"CLHLS / CLHLS-HF · 老年健康与家庭追踪","summary":"高龄健康与照护研究；2021家庭扩展、存活者问卷、死亡家属问卷和补充样本分别处理。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"clhls-database-skill","title":"CLHLS / CLHLS-HF · 老年健康与家庭追踪","tags":"CLHLS / CLHLS-HF 数据库","use":"高龄健康与照护研究；2021家庭扩展、存活者问卷、死亡家属问卷和补充样本分别处理。","url":"guides/clhls-database-skill.html","codes":[{"lang":"r","heading":"R与Stata：标准化层的最小接口","code":"# d: id=字符个人键, year=实际访谈年, y=已核对结局, x=预定协变量\nstopifnot(!anyNA(d$id), !anyDuplicated(d[c(\"id\", \"year\")]))\nd <- d[order(d$id, d$year), ]\n# 纵向条件关联；不自动等于政策因果效应\nm <- fixest::feols(y ~ x | id + year, cluster = ~id, data = d)\nsummary(m)\n# 按复杂抽样作描述应另用对应设计与权重，而非仅cluster=id。"},{"lang":"stata","heading":"R与Stata：标准化层的最小接口","code":"* id_s 是字符键，pid 仅作当前数据内面板编码，不输出映射。\nassert !missing(id_s, year)\nisid id_s year\negen long pid = group(id_s)\nxtset pid year\nxtreg y x i.year, fe vce(cluster pid)\n* 单波数据不运行此面板流程；先选择合适的survey描述/横断面模型。"}],"status":"数据库专题：版本、变量、权重和分析路线；执行状态见正文"}},{"id":"studio-methods:hrs-database-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"HRS · Health and Retirement Study","summary":"以健康、工作退休和家庭经济为主线的纵向调查。Core、RAND、家庭、CAMS、退出和生物标志物产品分开。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"hrs-database-skill","title":"HRS · Health and Retirement Study","tags":"HRS 数据库","use":"以健康、工作退休和家庭经济为主线的纵向调查。Core、RAND、家庭、CAMS、退出和生物标志物产品分开。","url":"guides/hrs-database-skill.html","codes":[{"lang":"r","heading":"R与Stata：标准化层的最小接口","code":"# d: id=字符个人键, year=实际访谈年, y=已核对结局, x=预定协变量\nstopifnot(!anyNA(d$id), !anyDuplicated(d[c(\"id\", \"year\")]))\nd <- d[order(d$id, d$year), ]\n# 纵向条件关联；不自动等于政策因果效应\nm <- fixest::feols(y ~ x | id + year, cluster = ~id, data = d)\nsummary(m)\n# 按复杂抽样作描述应另用对应设计与权重，而非仅cluster=id。"},{"lang":"stata","heading":"R与Stata：标准化层的最小接口","code":"* id_s 是字符键，pid 仅作当前数据内面板编码，不输出映射。\nassert !missing(id_s, year)\nisid id_s year\negen long pid = group(id_s)\nxtset pid year\nxtreg y x i.year, fe vce(cluster pid)\n* 单波数据不运行此面板流程；先选择合适的survey描述/横断面模型。"}],"status":"数据库专题：版本、变量、权重和分析路线；执行状态见正文"}},{"id":"studio-methods:elsa-database-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"ELSA · English Longitudinal Study of Ageing","summary":"英格兰老龄纵向研究；访问、护士测量、自填、生命历程和退出材料按组件衔接。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"elsa-database-skill","title":"ELSA · English Longitudinal Study of Ageing","tags":"ELSA 数据库","use":"英格兰老龄纵向研究；访问、护士测量、自填、生命历程和退出材料按组件衔接。","url":"guides/elsa-database-skill.html","codes":[{"lang":"r","heading":"R与Stata：标准化层的最小接口","code":"# d: id=字符个人键, year=实际访谈年, y=已核对结局, x=预定协变量\nstopifnot(!anyNA(d$id), !anyDuplicated(d[c(\"id\", \"year\")]))\nd <- d[order(d$id, d$year), ]\n# 纵向条件关联；不自动等于政策因果效应\nm <- fixest::feols(y ~ x | id + year, cluster = ~id, data = d)\nsummary(m)\n# 按复杂抽样作描述应另用对应设计与权重，而非仅cluster=id。"},{"lang":"stata","heading":"R与Stata：标准化层的最小接口","code":"* id_s 是字符键，pid 仅作当前数据内面板编码，不输出映射。\nassert !missing(id_s, year)\nisid id_s year\negen long pid = group(id_s)\nxtset pid year\nxtreg y x i.year, fe vce(cluster pid)\n* 单波数据不运行此面板流程；先选择合适的survey描述/横断面模型。"}],"status":"数据库专题：版本、变量、权重和分析路线；执行状态见正文"}},{"id":"studio-methods:share-database-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"SHARE · Survey of Health, Ageing and Retirement in Europe","summary":"跨欧洲国家的健康与退休调查；国家、常规波、SHARELIFE、疫情及退出组件需识别。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"share-database-skill","title":"SHARE · Survey of Health, Ageing and Retirement in Europe","tags":"SHARE 数据库","use":"跨欧洲国家的健康与退休调查；国家、常规波、SHARELIFE、疫情及退出组件需识别。","url":"guides/share-database-skill.html","codes":[{"lang":"r","heading":"R与Stata：标准化层的最小接口","code":"# d: id=字符个人键, year=实际访谈年, y=已核对结局, x=预定协变量\nstopifnot(!anyNA(d$id), !anyDuplicated(d[c(\"id\", \"year\")]))\nd <- d[order(d$id, d$year), ]\n# 纵向条件关联；不自动等于政策因果效应\nm <- fixest::feols(y ~ x | id + year, cluster = ~id, data = d)\nsummary(m)\n# 按复杂抽样作描述应另用对应设计与权重，而非仅cluster=id。"},{"lang":"stata","heading":"R与Stata：标准化层的最小接口","code":"* id_s 是字符键，pid 仅作当前数据内面板编码，不输出映射。\nassert !missing(id_s, year)\nisid id_s year\negen long pid = group(id_s)\nxtset pid year\nxtreg y x i.year, fe vce(cluster pid)\n* 单波数据不运行此面板流程；先选择合适的survey描述/横断面模型。"}],"status":"数据库专题：版本、变量、权重和分析路线；执行状态见正文"}},{"id":"studio-methods:klosa-database-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"KLoSA · Korean Longitudinal Study of Aging","summary":"2006起的中老年家庭追踪；基线抽样覆盖与后期补样须分别说明。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"klosa-database-skill","title":"KLoSA · Korean Longitudinal Study of Aging","tags":"KLoSA 数据库","use":"2006起的中老年家庭追踪；基线抽样覆盖与后期补样须分别说明。","url":"guides/klosa-database-skill.html","codes":[{"lang":"r","heading":"R与Stata：标准化层的最小接口","code":"# d: id=字符个人键, year=实际访谈年, y=已核对结局, x=预定协变量\nstopifnot(!anyNA(d$id), !anyDuplicated(d[c(\"id\", \"year\")]))\nd <- d[order(d$id, d$year), ]\n# 纵向条件关联；不自动等于政策因果效应\nm <- fixest::feols(y ~ x | id + year, cluster = ~id, data = d)\nsummary(m)\n# 按复杂抽样作描述应另用对应设计与权重，而非仅cluster=id。"},{"lang":"stata","heading":"R与Stata：标准化层的最小接口","code":"* id_s 是字符键，pid 仅作当前数据内面板编码，不输出映射。\nassert !missing(id_s, year)\nisid id_s year\negen long pid = group(id_s)\nxtset pid year\nxtreg y x i.year, fe vce(cluster pid)\n* 单波数据不运行此面板流程；先选择合适的survey描述/横断面模型。"}],"status":"数据库专题：版本、变量、权重和分析路线；执行状态见正文"}},{"id":"studio-methods:mhas-database-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"MHAS / ENASEM · Mexican Health and Aging Study","summary":"健康、家庭和经济追踪；Core、代理、家属、Master Follow-Up、Mex-Cog及插补资料分开。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"mhas-database-skill","title":"MHAS / ENASEM · Mexican Health and Aging Study","tags":"MHAS / ENASEM 数据库","use":"健康、家庭和经济追踪；Core、代理、家属、Master Follow-Up、Mex-Cog及插补资料分开。","url":"guides/mhas-database-skill.html","codes":[{"lang":"r","heading":"R与Stata：标准化层的最小接口","code":"# d: id=字符个人键, year=实际访谈年, y=已核对结局, x=预定协变量\nstopifnot(!anyNA(d$id), !anyDuplicated(d[c(\"id\", \"year\")]))\nd <- d[order(d$id, d$year), ]\n# 纵向条件关联；不自动等于政策因果效应\nm <- fixest::feols(y ~ x | id + year, cluster = ~id, data = d)\nsummary(m)\n# 按复杂抽样作描述应另用对应设计与权重，而非仅cluster=id。"},{"lang":"stata","heading":"R与Stata：标准化层的最小接口","code":"* id_s 是字符键，pid 仅作当前数据内面板编码，不输出映射。\nassert !missing(id_s, year)\nisid id_s year\negen long pid = group(id_s)\nxtset pid year\nxtreg y x i.year, fe vce(cluster pid)\n* 单波数据不运行此面板流程；先选择合适的survey描述/横断面模型。"}],"status":"数据库专题：版本、变量、权重和分析路线；执行状态见正文"}},{"id":"studio-methods:nhanes-database-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"NHANES · 复杂抽样与健康检查实操","summary":"从XPT与Doc File核对到周期合并、组件权重、survey分析和死亡链接。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"nhanes-database-skill","title":"NHANES · 复杂抽样与健康检查实操","tags":"NHANES 数据库","use":"从XPT与Doc File核对到周期合并、组件权重、survey分析和死亡链接。","url":"guides/nhanes-database-skill.html","codes":[{"lang":"r","heading":"R：仅示范2015—2016普通MEC成人BMI","code":"library(haven)\nlibrary(survey)\ndem <- read_xpt(\"DEMO_I.XPT\")\nbmx <- read_xpt(\"BMX_I.XPT\")\nstopifnot(!anyDuplicated(dem$SEQN), !anyDuplicated(bmx$SEQN))\nd <- merge(dem, bmx, by=\"SEQN\", all.x=TRUE, sort=FALSE)\nstopifnot(nrow(d)==nrow(dem))\n# BMI为MEC测量；此例不含实验室子样本或多周期。\nz <- subset(d, !is.na(WTMEC2YR) & WTMEC2YR>0 &\n               !is.na(SDMVPSU) & !is.na(SDMVSTRA))\ndes <- svydesign(ids=~SDMVPSU, strata=~SDMVSTRA,\n                 weights=~WTMEC2YR, nest=TRUE, data=z)\nadult <- subset(des, RIDAGEYR>=20)\nsvymean(~BMXBMI, adult, na.rm=TRUE)\nconfint(svymean(~BMXBMI, adult, na.rm=TRUE))\n# 补报未加权人数、BMI缺失率与估计可靠性；不将缺失直接当健康。"},{"lang":"stata","heading":"Stata：同一周期的分析层","code":"* d 已在周期内按 SEQN 合并，字段经Doc File核对。\nisid SEQN\nsvyset SDMVPSU [pweight=WTMEC2YR], strata(SDMVSTRA)\ngen byte adult = RIDAGEYR>=20 if !missing(RIDAGEYR)\nsvy, subpop(adult): mean BMXBMI\n* 保留完整设计再subpop，不先只保留成人破坏设计方差。"}],"status":"数据库专题：版本、变量、权重和分析路线；执行状态见正文"}},{"id":"studio-methods:cross-cohort-harmonization-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"HRS家族 · 跨国协调与外部验证","summary":"把CHARLS、HRS、ELSA、SHARE、KLoSA、LASI、MHAS及扩展研究按共同概念协调。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"cross-cohort-harmonization-skill","title":"HRS家族 · 跨国协调与外部验证","tags":"HRS家族 数据库","use":"把CHARLS、HRS、ELSA、SHARE、KLoSA、LASI、MHAS及扩展研究按共同概念协调。","url":"guides/cross-cohort-harmonization-skill.html","codes":[{"lang":"r","heading":"推荐输出","code":"# 标准化层示意；不改变原始ID，不公开对应表。\nstopifnot(all(c(\"study\",\"id\",\"visit_date\") %in% names(d)))\nd$key <- paste(d$study, d$id, sep=\":\")\nstopifnot(!anyDuplicated(d[c(\"key\",\"visit_date\")]))\n# study内真实设计单元与权重需单独核对，key不是PSU。"}],"status":"数据库专题：版本、变量、权重和分析路线；执行状态见正文"}},{"id":"studio-methods:claims-clinical-database-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"医保结算、临床登记与MEPS · 费用和事件数据","summary":"按人、就诊、费用明细和制度暴露构建可追溯分析层，区分结算费用与真实成本。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"claims-clinical-database-skill","title":"医保结算、临床登记与MEPS · 费用和事件数据","tags":"医保结算、临床登记与MEPS 数据库","use":"按人、就诊、费用明细和制度暴露构建可追溯分析层，区分结算费用与真实成本。","url":"guides/claims-clinical-database-skill.html","codes":[{"lang":"r","heading":"MEPS选型入口","code":"# 已去标识的标准化示意：每次就诊费用先聚合，避免多表多行膨胀。\nstopifnot(!anyDuplicated(encounter$encounter_id))\ntot <- aggregate(amount ~ encounter_id, cost_detail, sum)\na <- merge(encounter, tot, by=\"encounter_id\", all.x=TRUE)\nstopifnot(nrow(a)==nrow(encounter))\n# 缺少明细先核查，不能不加判断地将NA替换为0。"}],"status":"数据库专题：版本、变量、权重和分析路线；执行状态见正文"}},{"id":"studio-methods:standardized-patient-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"模拟与标准化病人 SP / USP","summary":"用训练过的扮演者在统一情境下测量服务过程，区分教学考核与未预先告知的质量研究。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"standardized-patient-skill","title":"模拟与标准化病人 SP / USP","tags":"标准化病人 模拟病人 SP USP OSCE 服务质量","use":"用训练过的扮演者在统一情境下测量服务过程，区分教学考核与未预先告知的质量研究。","url":"guides/standardized-patient-skill.html","codes":[{"lang":"r","heading":"机构层级分析 · R","code":"# Original teaching simulation. No patient data and no clinical recommendations.\n# Balanced cases/actors at every clinic; inference is conditional on this set.\nset.seed(20261024)\nclinics <- data.frame(clinic_id=1:36, arm=sample(rep(0:1, each=18)))\nvisits <- merge(clinics, expand.grid(case_id=1:3, actor_id=1:4))\nclinic_effect <- rnorm(36, 0, 0.45)\np <- plogis(-0.55 + 0.5*visits$arm + clinic_effect[visits$clinic_id] +\n             c(-0.2,0,0.2)[visits$case_id])\nvisits$correct <- rbinom(nrow(visits), 1, p)\nvisits$detected <- rbinom(nrow(visits), 1, 0.07)\nstopifnot(nrow(visits)==432, all(table(visits$clinic_id)==12))\nclinic_scores <- aggregate(correct ~ clinic_id + arm, visits, mean)\n# Clinic allocation means visits are not independent trial units.\ncomparison <- t.test(clinic_scores$correct[clinic_scores$arm==1],\n                     clinic_scores$correct[clinic_scores$arm==0])\nrd <- unname(diff(rev(comparison$estimate)))\nstopifnot(abs(rd - (mean(clinic_scores$correct[clinic_scores$arm==1]) -\n                   mean(clinic_scores$correct[clinic_scores$arm==0]))) < 1e-12)\nprint(data.frame(estimand='Equal-clinic mean risk difference (arm 1 minus 0)',\n                 estimate=rd, lower=comparison$conf.int[1], upper=comparison$conf.int[2]))\n# Detection is reported, not silently deleted; exclusions may select on treatment.\nprint(aggregate(detected ~ arm, visits, mean))\nprint(aggregate(correct ~ arm + case_id, visits, mean))\n# For observational samples these contrasts are descriptive, not causal effects.\n# Unbalanced scripts/actors, few clusters, or cross-classified random actors need\n# a prespecified adjustment model and suitable uncertainty estimation.\nstopifnot(all(visits$correct %in% 0:1), all(is.finite(comparison$conf.int)))\ncat('PASS: 432 visits; 36 randomized clinics; balanced scripts; finite clinic-level interval\\n')"},{"lang":"stata","heading":"机构层级分析 · STATA","code":"* Original teaching simulation; not run in Stata in this release.\nclear all\nset seed 20261024\nset obs 36\ngen clinic_id = _n\ngen allocation_order = runiform()\nsort allocation_order\ngen arm = (_n > 18)\ngen clinic_effect = rnormal(0, .45)\nexpand 12\nbysort clinic_id: gen visit = _n\ngen case_id = mod(visit-1,3)+1\ngen actor_id = ceil(visit/3)\ngen p = invlogit(-.55 + .5*arm + clinic_effect + .2*(case_id-2))\ngen correct = (runiform() < p)\ngen detected = (runiform() < .07)\nassert inlist(correct,0,1)\nassert _N == 432\ntabstat detected, by(arm) stat(mean n)\n* Equal clinic weighting; case and actor assignments are balanced by construction.\npreserve\ncollapse (mean) correct, by(clinic_id arm)\nassert _N == 36\nregress correct i.arm, vce(robust)\nlincom 1.arm\nrestore\n* The individual-visit model is an alternative, not an independent replication.\nregress correct i.arm i.case_id i.actor_id, vce(cluster clinic_id)\nlincom 1.arm\n* Few clusters, unbalanced case mixes and observational allocation require\n* separate design-based decisions. Do not delete detected visits without a plan."}],"status":"R 4.6.1模拟示例已运行；Stata未执行；不代表真实数据验证"}},{"id":"studio-methods:implementation-science-skill","collection":"studio-methods","collection_title":"代码与方法教程","kind":"方法","title":"实施科学：从框架到研究设计","summary":"区分服务干预与实施策略，把障碍、策略、实施结局、健康结局和持续使用连接到具体设计。","url":"index.html#methods","source":"data/studio-methods.json","details":{"id":"implementation-science-skill","title":"实施科学：从框架到研究设计","tags":"实施科学 CFIR RE-AIM Proctor ERIC hybrid StaRI","use":"区分服务干预与实施策略，把障碍、策略、实施结局、健康结局和持续使用连接到具体设计。","url":"guides/implementation-science-skill.html","codes":[{"lang":"r","heading":"机构层级分析 · R","code":"# Original teaching simulation: same clinical service, two implementation strategies.\nset.seed(20261025)\nd <- data.frame(site_id=1:40, strategy=sample(rep(0:1,each=20)))\nd$eligible <- sample(50:100, 40, replace=TRUE)\nd$adopted <- rbinom(40, 1, plogis(-0.1 + 0.6*d$strategy))\nd$reached <- ifelse(d$adopted==1,\n                    rbinom(40, d$eligible, plogis(-0.3 + 0.3*d$strategy)), 0)\nd$completed <- rbinom(40, d$reached, 0.8)\nsafe_ratio <- function(num,den) ifelse(den>0,num/den,NA_real_)\nd$reach <- safe_ratio(d$reached,d$eligible)\nd$fidelity <- safe_ratio(d$completed,d$reached)\nd$implementation_cost <- 500 + 15*d$reached\nsummary_by_arm <- do.call(rbind,lapply(split(d,d$strategy), function(x)\n data.frame(strategy=x$strategy[1], sites=nrow(x),\n adoption=mean(x$adopted), reach_person_weighted=sum(x$reached)/sum(x$eligible),\n reach_site_weighted=mean(x$reach), fidelity_among_reached=safe_ratio(sum(x$completed),sum(x$reached)),\n cost_per_reached=safe_ratio(sum(x$implementation_cost),sum(x$reached)))))\nprint(summary_by_arm)\n# Primary example: strategy effect on equal-site reach, at one prespecified time.\nfit <- t.test(d$reach[d$strategy==1],d$reach[d$strategy==0])\nprint(data.frame(reach_difference=unname(fit$estimate[1]-fit$estimate[2]),\n                 lower=fit$conf.int[1],upper=fit$conf.int[2]))\n# Fidelity conditions on reach. Its between-arm contrast is not automatically\n# a causal strategy effect because reaching the service is post-allocation.\nstopifnot(all(d$completed<=d$reached),all(d$reached<=d$eligible),\n          is.na(safe_ratio(0,0)),all(is.na(d$fidelity[d$reached==0])),\n          all(is.finite(fit$conf.int)))\ncat('PASS: denominators, missing zero-denominator ratios, 40 randomized sites, finite interval\\n')"},{"lang":"stata","heading":"机构层级分析 · STATA","code":"* Teaching template. Not run in Stata in this release.\nclear all\nset seed 20261025\nset obs 40\ngen site_id = _n\ngen allocation_order = runiform()\nsort allocation_order\ngen strategy = (_n > 20)\ngen eligible = 50 + floor(51*runiform())\ngen adopted = runiform() < invlogit(-.1+.6*strategy)\ngen reached = rbinomial(eligible, invlogit(-.3+.3*strategy)) if adopted==1\nreplace reached = 0 if adopted==0\ngen completed = rbinomial(reached,.8)\ngen reach = reached/eligible if eligible>0\ngen fidelity = completed/reached if reached>0\ngen implementation_cost = 500+15*reached\nassert completed<=reached & reached<=eligible\nassert missing(fidelity) if reached==0\ntabstat adopted reach fidelity, by(strategy) stat(mean n)\n* Site-weighted reach contrast. Zero reach remains in the primary analysis.\nregress reach i.strategy, vce(robust)\nlincom 1.strategy\n* Person-weighted summaries use pooled numerators AND denominators.\npreserve\ncollapse (sum) reached eligible completed implementation_cost, by(strategy)\ngen pooled_reach = reached/eligible\ngen pooled_fidelity = completed/reached if reached>0\ngen cost_per_reached = implementation_cost/reached if reached>0\nlist, noobs\nrestore\n* These are different estimands; do not equate a conditional fidelity contrast\n* with a randomized strategy effect on everyone originally eligible."}],"status":"R 4.6.1模拟示例已运行；Stata未执行；不代表真实数据验证"}},{"id":"code-library:methods-54-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"A1 横断面研究","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-1","title":"A1 横断面研究","language":"Stata + R（混合片段）","code":"* ===== Stata =====\nproportion disease                       // 患病率及 CI（默认 Wald）\nci proportions disease, wilson           // Wilson CI，小样本更稳\nglm disease exposed, family(binomial) link(log) eform   // PR；不收敛时换 poisson disease exposed, irr vce(robust)\n\n* ===== R =====\nx <- 45; n <- 300                        # 患病人数与调查人数\nprop.test(x, n, correct = FALSE)         # 患病率及近似 CI\nbinom.test(x, n)                         # 精确 Clopper-Pearson CI\nm <- glm(disease ~ exposed, family = binomial(link = \"log\"), data = d)\nexp(cbind(PR = coef(m), confint.default(m)))   # 患病率比 PR","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":32}},{"id":"code-library:methods-54-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"A2 病例对照研究","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-2","title":"A2 病例对照研究","language":"Stata + R（混合片段）","code":"* ===== Stata =====\ntabulate exposed case, chi2\ncci 40 20 30 70          // 病例暴露/非暴露=40/20，对照暴露/非暴露=30/70 → OR 与 CI\nlogistic case exposed age, or              // 不匹配：多因素校正 OR\nclogit case exposed, group(pairid) or      // 1:1 匹配：条件 logistic\n\n* ===== R =====\nm <- matrix(c(40, 20, 30, 70), nrow = 2)   # 病例暴露/非暴露、对照暴露/非暴露\nfisher.test(m)                             # 精确 OR 与 CI\nglm(case ~ exposed + age, family = binomial(), data = d)   # 校正 OR\nlibrary(survival)\nclogit(case ~ exposed + strata(pairid), data = d)          # 匹配条件 logistic","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":58}},{"id":"code-library:methods-54-3","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"A3 队列研究","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-3","title":"A3 队列研究","language":"Stata + R（混合片段）","code":"* ===== Stata =====\ncs disease exposed, exact                  // 2×2：RR 及 CI\nir disease exposed                         // 发病密度比 IRR\nstset time, failure(disease)               // 人时/生存数据\nstrate exposed                             // 各组人时与发病率\npoisson disease exposed, exposure(time) irr\nstcox exposed age                          // 校正 HR\n\n* ===== R =====\nlibrary(epiR)\nepi.2by2(matrix(c(45, 955, 20, 980), nrow = 2), method = \"cohort.count\")\nglm(disease ~ exposed + offset(log(time)), family = poisson(), data = d)  # IRR\nlibrary(survival)\ncoxph(Surv(time, disease) ~ exposed + age, data = d)                      # HR","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":85}},{"id":"code-library:methods-54-4","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"A4 随机对照试验 RCT","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-4","title":"A4 随机对照试验 RCT","language":"Stata + R（混合片段）","code":"* ===== Stata =====\nset obs 120\nset seed 2026\negen block = seq(), to(30) block(4)       // 30 个区组、组内 4 人\ngen treat = rbinomial(1, 0.5)             // 简单随机（严格 2:2 区组需再抽样）\ntab treat\ncsi 20 10 25 5                            // 干预组事件/非事件=20/10，对照组=25/5 → RD、RR 及 CI；NNT=1/RD\nttest y, by(treat)                        // 连续结局：MD\nregress y treat, vce(robust)\n\n* ===== R =====\nset.seed(2026)\nd$treat <- sample(rep(0:1, 60))           # 120 人简单随机（可用 blockrand 做区组）\nlibrary(epiR)\nepi.2by2(table(d$treat, d$outcome), method = \"cohort.count\")   # RR/RD/NNT\nt.test(y ~ treat, data = d)\nlm(y ~ treat, data = d)","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":114}},{"id":"code-library:methods-54-5","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"A5 准实验设计","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-5","title":"A5 准实验设计","language":"Stata + R（混合片段）","code":"* ===== Stata =====\ngen did = treat * post\nregress y treat post did, vce(cluster id)        // did 系数即 DID 估计\nxtset id time\nxtreg y ib0.time#i.treat i.time, fe vce(cluster id)   // 事件研究：检验平行趋势\ngen time_after = max(0, time - t0)               // ITS 分段回归（t0 = 干预时点）\nnewey y time post time_after, lag(1)             // Newey-West 处理自相关\n\n* ===== R =====\nm <- lm(y ~ treat * post, data = d)              # 交互项系数即 DID\nlibrary(sandwich); library(lmtest)\ncoeftest(m, vcov = vcovCL(m, cluster = ~id))     # 聚类稳健标准误\nd$time_after <- pmax(0, d$time - t0)\nits <- lm(y ~ time + post + time_after, data = d)   # ITS 分段回归","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":146}},{"id":"code-library:methods-54-6","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"A6 生态学研究","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-6","title":"A6 生态学研究","language":"Stata + R（混合片段）","code":"* ===== Stata =====\nregress rate exposure [aweight = pop]           // 按地区人口加权\ncorr rate exposure\ndstdize deaths pop, by(agegrp) using(stdpop)    // 直接标化；stdpop.dta 含 agegrp 与 pop\n\n* ===== R =====\nm <- lm(rate ~ exposure, data = d, weights = pop)\ncor(d$rate, d$exposure)\nlibrary(epitools)\nageadjust.direct(count = d$deaths, pop = d$pop, stdpop = ref$stdpop)  # 直接标化","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":175}},{"id":"code-library:methods-54-7","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"A7 混合方法研究（定性+定量）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-7","title":"A7 混合方法研究（定性+定量）","language":"Stata + R（混合片段）","code":"* ===== Stata =====\nttest score, by(group)                 // 定量侧比较\ntab theme group, chi2                  // 定性主题（编码后）与分组交叉：量化整合\n\n* ===== R =====\nlibrary(quanteda)\ndfm <- dfm(tokens(interview_text, remove_punct = TRUE))   # 定性文本：词频辅助主题\ntopfeatures(dfm, 20)\nt.test(score ~ group, data = d)                           # 定量侧\nchisq.test(table(d$theme, d$group))                       # 主题×分组整合","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":200}},{"id":"code-library:methods-54-8","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"A8 德尔菲法（Delphi）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-8","title":"A8 德尔菲法（Delphi）","language":"Stata + R（混合片段）","code":"* ===== Stata =====\negen mean_score = mean(score), by(item)\negen sd_score = sd(score), by(item)\ngen cv = sd_score / mean_score\nlist item mean_score cv if mean_score >= 3.5 & cv <= 0.25   // 示例筛选阈值\nssc install concord\nconcord score1-score15                       // 各专家评分变量 → 肯德尔 W\n\n* ===== R =====\nlibrary(dplyr)\nd |> group_by(item) |>\n  summarise(m = mean(score), cv = sd(score)/mean(score)) |>\n  filter(m >= 3.5 & cv <= 0.25)              # 示例筛选阈值\nlibrary(irr)\nw <- kendall(t(as.matrix(xtabs(score ~ expert + item, data = d))))   # 行为条目、列为专家\nw$value                                      # 肯德尔和谐系数 W","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":225}},{"id":"code-library:methods-54-9","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"B1 描述统计与正态性检验","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-9","title":"B1 描述统计与正态性检验","language":"stata+r","code":"* Stata\nsummarize y, detail              // n、均值、百分位数、偏度峰度\nswilk y                          // Shapiro-Wilk 正态性检验\nhistogram y, normal              // 直方图叠加正态密度曲线\nqnorm y                          // 分位-分位（Q-Q）图\n\n## R\nsummary(df$y)                    // 五数概括\nshapiro.test(df$y)               // Shapiro-Wilk 检验\nhist(df$y, prob = TRUE); lines(density(df$y))\nqqnorm(df$y); qqline(df$y)       // Q-Q 图","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":264}},{"id":"code-library:methods-54-10","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"B2 t 检验与方差分析","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-10","title":"B2 t 检验与方差分析","language":"stata+r","code":"* Stata\nttest y, by(group) unequal       // Welch t 检验（不假定方差齐）\nttest y1 == y2                   // 配对 t 检验\noneway y group, bonferroni       // 单因素 ANOVA + Bonferroni 两两比较\nanova y group                    // 方差分析表\n\n## R\nt.test(y ~ group, data = df)              // 默认 Welch t 检验\nt.test(df$y1, df$y2, paired = TRUE)       // 配对 t 检验\nfit <- aov(y ~ group, data = df)\nsummary(fit); TukeyHSD(fit)               // ANOVA + Tukey 两两比较","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":292}},{"id":"code-library:methods-54-11","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"B3 卡方检验与非参数检验","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-11","title":"B3 卡方检验与非参数检验","language":"stata+r","code":"* Stata\ntab a b, chi2 exact              // Pearson 卡方 + Fisher 精确检验\nranksum y, by(group)             // Mann-Whitney U（Wilcoxon 秩和）\nsignrank y1 = y2                 // 配对 Wilcoxon 符号秩检验\nkwallis y, by(group)             // Kruskal-Wallis 检验\n\n## R\nchisq.test(table(df$a, df$b))    // Pearson 卡方\nfisher.test(table(df$a, df$b))   // Fisher 精确检验\nwilcox.test(y ~ group, data = df)\nkruskal.test(y ~ group, data = df)","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":320}},{"id":"code-library:methods-54-12","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"B4 相关与简单线性回归","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-12","title":"B4 相关与简单线性回归","language":"stata+r","code":"* Stata\nscatter y x                     // 先画散点图判断线性\npwcorr x y, sig                 // Pearson r 及 p 值\nspearman x y                    // Spearman ρ\nregress y x                     // 简单线性回归\n\n## R\nplot(df$x, df$y)\ncor.test(df$x, df$y, method = \"pearson\")\ncor.test(df$x, df$y, method = \"spearman\")\nfit <- lm(y ~ x, data = df); summary(fit)","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":348}},{"id":"code-library:methods-54-13","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"B5 二元 logistic 回归","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-13","title":"B5 二元 logistic 回归","language":"stata+r","code":"* Stata\nlogit y x1 x2, or               // 二元 logistic，报告 OR\nestat gof, group(10)            // Hosmer-Lemeshow 校准检验\nlroc                            // ROC 曲线与 AUC\n\n## R\nfit <- glm(y ~ x1 + x2, data = df, family = binomial)\nexp(cbind(OR = coef(fit), confint(fit)))   // OR 与 95% CI\nsummary(fit)","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":376}},{"id":"code-library:methods-54-14","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"B6 有序/多项 logistic 回归","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-14","title":"B6 有序/多项 logistic 回归","language":"stata+r","code":"* Stata\nologit y x1 x2, or              // 有序 logistic（比例优势模型）\nbrant, detail                   // 比例优势/平行线假设检验（SSC 安装）\nmlogit y x1 x2, rrr base(1)     // 多项 logistic，参照组=1，报 RRR\n\n## R\nlibrary(MASS)\nfit <- polr(y ~ x1 + x2, data = df, Hess = TRUE)   // y 需为有序因子\nexp(cbind(coef(fit), confint(fit)))                // 公共 OR\nlibrary(nnet)\nmfit <- multinom(y ~ x1 + x2, data = df)           // 多项模型，exp(coef)=RRR","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":402}},{"id":"code-library:methods-54-15","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"B7 Poisson 与负二项回归","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-15","title":"B7 Poisson 与负二项回归","language":"stata+r","code":"* Stata\npoisson y x1 x2, irr vce(robust)          // Poisson + 稳健标准误\nnbreg y x1 x2, irr                        // 负二项回归\npoisson y x1 x2, irr exposure(t)          // 随访时间不等：按 t 调整\n\n## R\nglm(y ~ x1 + x2, data = df, family = poisson)\nlibrary(MASS)\nfit <- glm.nb(y ~ x1 + x2, data = df)     // 负二项\nexp(cbind(IRR = coef(fit), confint(fit)))","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":430}},{"id":"code-library:methods-54-16","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"B8 生存分析（Kaplan-Meier 与 Cox）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-16","title":"B8 生存分析（Kaplan-Meier 与 Cox）","language":"stata+r","code":"* Stata\nstset time, failure(event)               // event=1 事件，0 删失\nsts graph, by(group)                     // Kaplan-Meier 生存曲线\nsts test group                           // log-rank 检验\nstcox x1 x2                              // Cox 比例风险模型（HR）\nestat phtest, detail                     // PH 假设检验\n\n## R\nlibrary(survival)\nfit <- survfit(Surv(time, event) ~ group, data = df)\nplot(fit); survdiff(Surv(time, event) ~ group, data = df)\ncfit <- coxph(Surv(time, event) ~ x1 + x2, data = df)\nsummary(cfit); cox.zph(cfit)             // PH 假设检验","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":457}},{"id":"code-library:methods-54-17","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"B9 重复测量与混合效应模型","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-17","title":"B9 重复测量与混合效应模型","language":"stata+r","code":"* Stata\nxtset id time\nmixed y x##c.time || id: time, reml      // 随机截距 + 随机斜率\nmixed y x##c.time || id:                 // 仅随机截距（供比较）\nestat ic                                 // AIC/BIC 比较\n* 二分类结局改用：melogit y x || id:\n\n## R\nlibrary(lme4); library(lmerTest)         // lmerTest 提供 p 值\nfit <- lmer(y ~ x*time + (1 + time | id), data = df)\nsummary(fit)\nranova(fit)                              // 随机效应结构的似然比检验","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":487}},{"id":"code-library:methods-54-18","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"B10 主成分分析与因子分析","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-18","title":"B10 主成分分析与因子分析","language":"stata+r","code":"* Stata\npca x1-x10                          // 主成分分析\nfactor x1-x10, pcf                  // 因子分析（主成分因子法）\nestat kmo                           // KMO（Bartlett 检验：ssc install factortest）\nrotate, varimax                     // 正交旋转后看载荷\nalpha x1-x10                        // Cronbach's α\n\n## R\nlibrary(psych)\nKMO(df); cortest.bartlett(df)       // 适用性检验\nfa.parallel(df)                     // 平行分析定因子数\nfa(df, nfactors = 3, rotate = \"varimax\")   // 探索性因子分析\nalpha(df)                           // Cronbach's α","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":516}},{"id":"code-library:methods-54-19","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"C1 成本测算","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-19","title":"C1 成本测算","language":"stata","code":"* 成本均数与 bootstrap 95% CI（成本右偏时优于正态 CI）\nbootstrap r(mean), reps(1000) seed(2026): summarize cost\nci means cost","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":552}},{"id":"code-library:methods-54-20","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"C1 成本测算","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-20","title":"C1 成本测算","language":"r","code":"set.seed(2026)\nboot <- replicate(1000, mean(sample(cost, replace = TRUE)))\nmean(cost)                       # 点估计\nquantile(boot, c(.025, .975))    # 95% 百分位置信区间","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":558}},{"id":"code-library:methods-54-21","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"C2 成本-效果分析 CEA 与成本-效用分析 CUA","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-21","title":"C2 成本-效果分析 CEA 与成本-效用分析 CUA","language":"stata","code":"* ICER 增量计算（arm：1 = 干预，0 = 对照；ttest 按组值升序，组 2 为 arm=1）\nttest c, by(arm)\nscalar dC = r(mu_2) - r(mu_1)\nttest e, by(arm)\nscalar dE = r(mu_2) - r(mu_1)\ndi \"ICER = \" dC / dE","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":577}},{"id":"code-library:methods-54-22","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"C2 成本-效果分析 CEA 与成本-效用分析 CUA","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-22","title":"C2 成本-效果分析 CEA 与成本-效用分析 CUA","language":"r","code":"dC <- with(dat, mean(c[arm == 1]) - mean(c[arm == 0]))\ndE <- with(dat, mean(e[arm == 1]) - mean(e[arm == 0]))\nset.seed(2026)\nicers <- replicate(1000, with(dat[sample(nrow(dat), replace = TRUE), ],\n              (mean(c[arm==1]) - mean(c[arm==0])) / (mean(e[arm==1]) - mean(e[arm==0]))))\nquantile(icers, c(.025, .975))   # bootstrap ICER 置信区间","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":586}},{"id":"code-library:methods-54-23","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"C3 QALY 与 DALY 计算","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-23","title":"C3 QALY 与 DALY 计算","language":"stata","code":"* QALY：效用曲线下面积（q 为效用、t 为时点，按 id 与 t 排序）\nbysort id (t): gen qaly_step = (q + q[_n-1]) / 2 * (t - t[_n-1]) if _n > 1\nbysort id: egen qaly = total(qaly_step)\n* DALY：YLL + YLD（yll、yld 按 GBD 公式预先计算）\ngen daly = yll + yld","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":607}},{"id":"code-library:methods-54-24","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"C3 QALY 与 DALY 计算","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-24","title":"C3 QALY 与 DALY 计算","language":"r","code":"dat <- dat[order(dat$id, dat$t), ]\nqaly <- tapply(seq_len(nrow(dat)), dat$id, function(i) {\n  t <- dat$t[i]; q <- dat$q[i]\n  sum(diff(t) * (head(q, -1) + tail(q, -1)) / 2)     # 梯形面积法\n})","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":615}},{"id":"code-library:methods-54-25","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"C4 预算影响分析 BIA","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-25","title":"C4 预算影响分析 BIA","language":"stata","code":"* 逐年预算影响 = 目标人群 × 渗透率 ×（新方案 − 现行方案）人均年费用\ngen bi = pop * uptake * (c_new - c_cur)\nlist year bi\nsum bi                         // 研究期内合计","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":634}},{"id":"code-library:methods-54-26","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"C4 预算影响分析 BIA","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-26","title":"C4 预算影响分析 BIA","language":"r","code":"bia <- data.frame(year = 1:5, pop = 1e5, uptake = c(.2, .4, .6, .8, .9))\nbia$bi <- bia$pop * bia$uptake * (c_new - c_cur)\nbia                            # 逐年预算影响；sum(bia$bi) 为合计","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":641}},{"id":"code-library:methods-54-27","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"C5 卫生技术评估 HTA 流程","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-27","title":"C5 卫生技术评估 HTA 流程","language":"stata","code":"* PSA 决策判断：净货币效益 NMB = λ·ΔE − ΔC，λ 为阈值（如 1–3 倍人均 GDP）\ngen nmb = lambda * (e_new - e_cur) - (c_new - c_cur)\ncount if nmb > 0\ndi \"成本-效果概率 = \" r(N) / _N","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":658}},{"id":"code-library:methods-54-28","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"C5 卫生技术评估 HTA 流程","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-28","title":"C5 卫生技术评估 HTA 流程","language":"r","code":"nmb <- lambda * (e_new - e_cur) - (c_new - c_cur)\nmean(nmb > 0)                  # NMB > 0 的比例 → CEAC 上阈值 λ 处的接受概率","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":665}},{"id":"code-library:methods-54-29","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"C6 随机前沿分析 SFA 与数据包络分析 DEA（效率评价）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-29","title":"C6 随机前沿分析 SFA 与数据包络分析 DEA（效率评价）","language":"stata","code":"* SFA：Cobb-Douglas 随机前沿，半正态无效率\nsfcross y x1 x2, distribution(hnormal)\npredict te_sfa, te\n* DEA：投入导向 BCC（先 ssc install dea）\ndea x1 x2 = y, rts(vrs) ort(in)   // 效率得分；选项以 help dea 为准","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":682}},{"id":"code-library:methods-54-30","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"C6 随机前沿分析 SFA 与数据包络分析 DEA（效率评价）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-30","title":"C6 随机前沿分析 SFA 与数据包络分析 DEA（效率评价）","language":"r","code":"library(Benchmarking)\nr_dea <- dea(X = cbind(x1, x2), Y = y, RTS = \"vrs\")    # 投入导向 BCC\neff_dea <- r_dea$eff                                    # Farrell 效率 E ∈ (0,1]\nlibrary(frontier)\neff_sfa <- efficiencies(sfa(log(y) ~ log(x1) + log(x2), data = dat))","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":690}},{"id":"code-library:methods-54-31","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"C7 医保支付标准测算（DRG/DIP 权重与点值测算思路）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-31","title":"C7 医保支付标准测算（DRG/DIP 权重与点值测算思路）","language":"stata","code":"* DRG 权重：病组例均费用 ÷ 全样本例均费用\nbysort drg: egen fee_drg = mean(fee)\negen fee_all = mean(fee)\ngen rw = fee_drg / fee_all\n* DIP 点值：区域基金总额 ÷ Σ(病种分值 × 病例数)\ngen point_amt = dip_score * n_cases\nsum point_amt\ndi \"每分值金额 = \" fund_budget / r(sum)","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":710}},{"id":"code-library:methods-54-32","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"C7 医保支付标准测算（DRG/DIP 权重与点值测算思路）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-32","title":"C7 医保支付标准测算（DRG/DIP 权重与点值测算思路）","language":"r","code":"library(dplyr)\nrw <- dat |> group_by(drg) |> summarise(rw = mean(fee) / mean(dat$fee))\ndip_value <- fund_budget / sum(dat$dip_score * dat$n_cases)   # DIP 点值","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":721}},{"id":"code-library:methods-54-33","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"C8 卫生服务利用公平性（集中指数 CI 与不平等分解）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-33","title":"C8 卫生服务利用公平性（集中指数 CI 与不平等分解）","language":"stata","code":"ssc install conindex, replace\nconindex y, rankvar(r)      // r：按经济状况排序的分数秩；选项以 help conindex 为准","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":738}},{"id":"code-library:methods-54-34","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"C8 卫生服务利用公平性（集中指数 CI 与不平等分解）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-34","title":"C8 卫生服务利用公平性（集中指数 CI 与不平等分解）","language":"r","code":"r  <- (rank(ses) - 0.5) / length(ses)   # 按经济状况排序的分数秩\nci <- 2 * cov(y, r) / mean(y)           # 协方差法集中指数","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":743}},{"id":"code-library:methods-54-35","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"D1 倾向得分匹配（PSM）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-35","title":"D1 倾向得分匹配（PSM）","language":"stata","code":"* Stata：1:1 近邻匹配估计 ATT（y 结局、t 处理、x1 x2 协变量）\nteffects psmatch (y) (t x1 x2), atet\ntebalance summarize          // 平衡性：SMD 与方差比\ntebalance density            // 共同支撑：倾向得分重叠图","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":765}},{"id":"code-library:methods-54-36","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"D1 倾向得分匹配（PSM）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-36","title":"D1 倾向得分匹配（PSM）","language":"r","code":"library(MatchIt)\nm <- matchit(t ~ x1 + x2, data = dat, method = \"nearest\")\nmd <- match.data(m)\nlm(y ~ t + x1 + x2, data = md, weights = weights)\ncobalt::love.plot(m, abs = TRUE)   # SMD 平衡图","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":772}},{"id":"code-library:methods-54-37","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"D2 工具变量（IV）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-37","title":"D2 工具变量（IV）","language":"stata","code":"* Stata：2SLS（核心命令）\nivregress 2sls y (t = z), first vce(robust)\nestat firststage            // 一阶段 F，>10 为强工具变量\n* 多工具变量：ivregress 2sls y (t = z1 z2), first，再用 estat overid 做过度识别检验","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":795}},{"id":"code-library:methods-54-38","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"D2 工具变量（IV）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-38","title":"D2 工具变量（IV）","language":"r","code":"library(AER)\nm_iv <- ivreg(y ~ t | z, data = dat)\nsummary(m_iv, diagnostics = TRUE)   # 含弱工具变量诊断","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":802}},{"id":"code-library:methods-54-39","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"D3 断点回归（RDD）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-39","title":"D3 断点回归（RDD）","language":"stata","code":"ssc install rdrobust\nssc install rddensity\nrdrobust y x, c(0)          // x 为中心化运行变量\nrdplot y x, c(0)            // RD 图\nrddensity x, c(0)           // 密度操纵检验","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":822}},{"id":"code-library:methods-54-40","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"D3 断点回归（RDD）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-40","title":"D3 断点回归（RDD）","language":"r","code":"library(rdrobust)\nrdrobust(y = dat$y, x = dat$x, c = 0)\nrddensity::rddensity(dat$x, c = 0)   # 操纵检验","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":830}},{"id":"code-library:methods-54-41","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"D4 合成控制法（SCM）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-41","title":"D4 合成控制法（SCM）","language":"stata","code":"ssc install synth\ntsset id time\nsynth y x1 x2 y(1990(1)1999), trunit(1) trperiod(2000) fig\n* 安慰剂检验：对每个对照单元重复 synth 比较效应轨迹（以 help synth 为准）","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":849}},{"id":"code-library:methods-54-42","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"D4 合成控制法（SCM）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-42","title":"D4 合成控制法（SCM）","language":"r","code":"dp <- Synth::dataprep(foo = dat, predictors = c(\"x1\", \"x2\"), dependent = \"y\",\n                      unit.variable = \"id\", time.variable = \"time\",\n                      treatment.identifier = 1, controls.identifier = 2:20,\n                      time.predictors.prior = 1990:1999, time.optimize.ssr = 1990:1999)\ns1 <- Synth::synth(dp)","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":856}},{"id":"code-library:methods-54-43","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"D5 事件研究法","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-43","title":"D5 事件研究法","language":"stata","code":"ssc install reghdfe\nssc install coefplot\nreghdfe y ib(-1).time#i.treat, absorb(id time) vce(cluster id)\ncoefplot, keep(*.time#1.treat)          // 事件研究图","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":879}},{"id":"code-library:methods-54-44","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"D5 事件研究法","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-44","title":"D5 事件研究法","language":"r","code":"library(fixest)\nm <- feols(y ~ i(time, treat, ref = -1) | id + time, data = dat)\ncoefplot(m)                              // 事件研究图","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":886}},{"id":"code-library:methods-54-45","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"D6 双重差分（DID）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-45","title":"D6 双重差分（DID）","language":"stata","code":"* Stata：2×2 DID（交互项系数即效应）\nreg y i.treat##i.post, vce(cluster id)","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":901}},{"id":"code-library:methods-54-46","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"D6 双重差分（DID）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-46","title":"D6 双重差分（DID）","language":"r","code":"## R：fixest 双向固定效应 DID\nfeols(y ~ treat*post | id + time, data = dat, cluster = ~id)","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":905}},{"id":"code-library:methods-54-47","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"D7 间断时间序列（ITS）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-47","title":"D7 间断时间序列（ITS）","language":"stata","code":"tsset time\ngen post = (time >= t0)          // t0 = 政策实施期\ngen time_after = (time - t0) * post\nnewey y time post time_after, lag(1)","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":919}},{"id":"code-library:methods-54-48","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"D7 间断时间序列（ITS）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-48","title":"D7 间断时间序列（ITS）","language":"r","code":"m <- lm(y ~ time + post + time_after, data = dat)\nlmtest::coeftest(m, vcov = sandwich::NeweyWest(m, lag = 1))","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":925}},{"id":"code-library:methods-54-49","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"D8 三重差分（DDD）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-49","title":"D8 三重差分（DDD）","language":"stata","code":"* Stata：DDD（三重交互项系数即效应）\nreg y i.treat##i.post##i.group, vce(cluster id)","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":940}},{"id":"code-library:methods-54-50","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"D8 三重差分（DDD）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-50","title":"D8 三重差分（DDD）","language":"r","code":"## R：DDD 三重交互（主效应被固定效应吸收时仅保留三重交互）\nfeols(y ~ treat:post:group | id + time, data = dat, cluster = ~id)","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":944}},{"id":"code-library:methods-54-51","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"E1 缺失数据与多重插补","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-51","title":"E1 缺失数据与多重插补","language":"Stata + R（混合片段）","code":"* ===== Stata =====\nmi set mlong                                  // 声明多重插补 long 格式\nmi register imputed y x                       // 登记含缺失的待插补变量\nmi impute chained (regress) y (logit) x = z age, add(20) rseed(2026)\n                                              // y 连续用 regress、x 二分类用 logit；z age 为完整辅助变量\nmi estimate: regress y x age                  // 逐集估计 + Rubin 规则合并\n* ===== R =====\nlibrary(mice)\nimp <- mice(data, m = 20, seed = 2026)        # 链式方程多重插补\ndensityplot(imp)                              # 检查插补值分布\nfit <- with(imp, glm(y ~ x + age, family = binomial()))   # 逐集估计\npool(fit)                                     # Rubin 规则合并","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":966}},{"id":"code-library:methods-54-52","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"E2 样本量与统计效能计算","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-52","title":"E2 样本量与统计效能计算","language":"Stata + R（混合片段）","code":"* ===== Stata =====\npower twomeans 100 110, sd(20) power(0.8)    // 两组均数 100 vs 110、SD 20 → 每组 n\npower twomeans 100 110, sd(20) n(64)         // 固定每组 n=64 → 检验效能\npower twoproportions 0.30 0.20, power(0.8)   // 两率 0.30 vs 0.20 → 每组 n\npower oneproportion 0.20 0.25                // 单组率 0.20 对目标 0.25\npower twomeans 100 110, sd(20) power(0.8) nratio(2)   // 组间比 1:2 设计\n* ===== R =====\nlibrary(pwr)\npwr.t.test(d = 0.5, sig.level = 0.05, power = 0.8, type = \"two.sample\")  // 每组 n\npwr.t.test(d = 0.5, sig.level = 0.05, n = 64, type = \"two.sample\")       // 固定 n → 效能\npwr.2p.test(h = ES.h(0.3, 0.2), sig.level = 0.05, power = 0.8)           // 两率\npwr.anova.test(k = 3, f = 0.25, sig.level = 0.05, power = 0.8)           // 多组方差分析\npwr.chisq.test(w = 0.1, df = 1, sig.level = 0.05, power = 0.8)           // 卡方检验","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":993}},{"id":"code-library:methods-54-53","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"E3 LASSO/弹性网与正则化","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-53","title":"E3 LASSO/弹性网与正则化","language":"Stata + R（混合片段）","code":"* ===== Stata =====\nlasso linear y x1-x200, selection(cv) nolog    // 连续结局，CV 选 λ\nlasso logit y x1-x200, selection(cv)           // 二分类结局\n* ===== R =====\ncvfit <- glmnet::cv.glmnet(x, y, family = \"binomial\", alpha = 0.5)   // 弹性网\ncoef(cvfit, s = \"lambda.1se\")                  // 1-SE λ 的系数","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1021}},{"id":"code-library:methods-54-54","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"E4 随机森林与 XGBoost","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-54","title":"E4 随机森林与 XGBoost","language":"Stata + R（混合片段）","code":"* ===== Stata =====\n* Stata 无内置实现；可经 PyStata 调用 Python（sklearn/xgboost）\n* ===== R =====\nlibrary(randomForest); library(xgboost); set.seed(2026)\nrf <- randomForest(y ~ ., data = train, ntree = 500, importance = TRUE)   # 分类/回归自动识别\nbst <- xgboost(data = xgb.DMatrix(as.matrix(train_x), label = train_y),\n               nrounds = 100, eta = 0.1, max_depth = 3, objective = \"binary:logistic\")","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1041}},{"id":"code-library:methods-54-55","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"E5 聚类分析（K-means/层次聚类）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-55","title":"E5 聚类分析（K-means/层次聚类）","language":"Stata + R（混合片段）","code":"* ===== Stata =====\ncluster kmeans x1 x2, k(3) start(krandom(2026)) name(k3)   // K-means（x1 x2 宜先标准化）\ncluster stop, rule(duda)                      // 肘部指标辅助选 K（Duda-Hart）\ncluster wardslinkage x1 x2, name(hc)          // 层次聚类（Ward 法）\ncluster dendrogram hc, labels(id)             // 树状图\ncluster generate g = groups(k3)               // 保存 K-means 分组结果\n* ===== R =====\nset.seed(2026); km <- kmeans(scale(x), centers = 3, nstart = 25)   # K-means（scale 标准化）\nd <- dist(scale(x)); hc <- hclust(d, method = \"ward.D2\")           # 层次聚类（Ward 法）\nplot(hc); table(cutree(hc, k = 3), km$cluster)                     # 树状图；两法结果交叉\nlibrary(cluster)\nmean(silhouette(km$cluster, dist(scale(x)))[, 3])                  # 平均轮廓系数（选 K）","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1062}},{"id":"code-library:methods-54-56","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"E6 贝叶斯统计入门","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-56","title":"E6 贝叶斯统计入门","language":"Stata + R（混合片段）","code":"* ===== Stata =====\nbayes, mcmcsize(10000) rseed(2026): regress y x   // 默认弱信息先验的贝叶斯回归\nbayesgraph diagnostics {y:x}                      // 轨迹/自相关/R-hat 收敛诊断\nbayesstats ess                                   // 有效样本量\nbayesstats summary, credible                     // 后验均值与 95% 可信区间\nbayes, prior({y:x}, normal(0, 10)) rseed(2026): regress y x   // 自定义先验 → 敏感性分析\n* ===== R =====\nlibrary(brms)\nfit <- brm(y ~ x, data = d, chains = 4, iter = 2000, warmup = 1000, seed = 2026)\nsummary(fit)                                 # 后验均值、95% CI、R-hat、ESS\nplot(fit)                                    # 链轨迹与后验密度\nhypothesis(fit, \"x > 0\")                     # 后验概率 P(x > 0)","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1089}},{"id":"code-library:methods-54-57","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"F1 Andersen 卫生服务利用行为模型","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-57","title":"F1 Andersen 卫生服务利用行为模型","language":"stata","code":"* Andersen 三因素变量清单（就诊行为研究示例）\n* 倾向特征 predisposing：age 年龄、female 性别(0/1)、edu 教育年限\n* 使能资源 enabling：lnincome 家庭收入对数、insured 是否参保(0/1)、dist 距最近机构(km)\n* 需要因素 need：sahealth 自评健康(1-5)、chronic 慢病种数\nlogit visit age female edu lnincome insured dist sahealth chronic, or","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1122}},{"id":"code-library:methods-54-58","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"F2 Grossman 健康需求模型","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-58","title":"F2 Grossman 健康需求模型","language":"stata","code":"* 健康需求简化式：H = f(工资率 w, 教育 E, 年龄 t, 医疗服务价格 p)\n* 预期：教育、工资系数为正（效率与投资回报），年龄为负（折旧），价格为负\nreg health lnwage edu age age2 lnprice\n* 教育→健康的机制检验：加入知识/行为中介后看 edu 系数变化\nreg health edu age\nreg health edu age knowl healthbehavior","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1142}},{"id":"code-library:methods-54-59","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"F3 健康信念模型 HBM","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-59","title":"F3 健康信念模型 HBM","language":"stata","code":"* HBM 维度量表（Likert 1-5，各维度多题取均值）与预防行为模型\n* perc_sus 感知易感性 | perc_sev 感知严重性 | perc_ben 感知收益 | perc_bar 感知障碍\n* cue 行动线索(0/1) | selfeff 自我效能\nlogit vaccinate perc_sus perc_sev perc_ben perc_bar cue selfeff, or","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1163}},{"id":"code-library:methods-54-60","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"F4 计划行为理论 TPB","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-60","title":"F4 计划行为理论 TPB","language":"stata","code":"* Stata sem：观测题项均值入模（单指标路径模型）\nsem (intention <- att sn pbc) (behavior <- intention pbc), standardized\nestat gof, stats(all)","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1182}},{"id":"code-library:methods-54-61","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"F4 计划行为理论 TPB","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-61","title":"F4 计划行为理论 TPB","language":"r","code":"## R lavaan 等价写法（多题项做潜变量时用）\nlibrary(lavaan)\nmodel <- 'intention ~ att + sn + pbc\n          behavior ~ intention + pbc'\nfit <- sem(model, data = d)\nsummary(fit, standardized = TRUE, fit.measures = TRUE)","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1188}},{"id":"code-library:methods-54-62","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"F5 社会生态学模型","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-62","title":"F5 社会生态学模型","language":"stata","code":"* 个体层：age sex edu | 人际层：fam_support 家庭支持(0-3) | 社区层：park_density 绿地密度\n* 随机截距两水平模型（个体嵌套于社区）\nmixed behavior age sex edu fam_support park_density || community: , mle\n* 跨层交互：社区环境调节个体因素\nmixed behavior age sex edu fam_support c.fam_support#c.park_density || community: , mle","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1209}},{"id":"code-library:methods-54-63","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"F6 健康公平与正义理论（Rawls / Sen 可行能力）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-63","title":"F6 健康公平与正义理论（Rawls / Sen 可行能力）","language":"stata","code":"* 集中指数 CI（便利回归法）：按收入排序后，2*σ²_rank*(h/μ) 对 rank 回归\nsort income\ngen rank = _n / _N\nquietly summarize h\nlocal mu = r(mean)\nquietly summarize rank\ngen lhs = 2 * r(Var) * (h / `mu')\nreg lhs rank\n* _b[rank] 即 CI（正=亲富，负=亲穷）；SSC 版：ssc install conindex; conindex h, rankvar(income)","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1229}},{"id":"code-library:methods-54-64","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"F7 健康社会决定因素框架（CSDH）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-64","title":"F7 健康社会决定因素框架（CSDH）","language":"stata","code":"* 结构层：ses 教育年限/收入/职业 | 中间层：housing 居住条件、smoke 吸烟、stress 心理压力 | 结局：health\n* Oaxaca-Blinder 分解：高/低 SES 组健康差距分解为「禀赋效应 + 系数效应」\nssc install oaxaca\noaxaca health housing smoke stress, by(ses_group)","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1253}},{"id":"code-library:methods-54-65","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"F8 委托代理与供方诱导需求（SID）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-65","title":"F8 委托代理与供方诱导需求（SID）","language":"stata","code":"* 供方诱导需求简化估计式：y = β0 + β1·beds + β2·need + β3·price + ε\n* β1 > 0 且在控制需要与价格后仍显著 → 诱导需求的证据\nreg visits beds_per1000 chronic lnprice insurance age\n* 稳健性：换供给指标（医生密度）重复\nreg visits doctors_per1000 chronic lnprice insurance age","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1272}},{"id":"code-library:methods-54-66","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"F9 市场失灵与政府干预（公共品 / 外部性 / 信息不对称）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-66","title":"F9 市场失灵与政府干预（公共品 / 外部性 / 信息不对称）","language":"text","code":"论文应用模板：市场失灵 → 对应卫生服务 → 干预手段（论证链）\n· 公共品：传染病监测、控烟立法（非竞争、非排他）→ 政府提供或强制\n· 正外部性：疫苗接种（私人收益 < 社会收益，私人最优接种量低于社会最优）→ 补贴、免疫规划\n· 信息不对称①：医患（供方诱导需求）→ 支付方式改革、监管、信息公开\n· 信息不对称②：保险市场（逆选择、道德风险）→ 强制参保、共付与起付线设计","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1292}},{"id":"code-library:methods-54-67","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"F10 制度变迁与路径依赖（医改分析）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-67","title":"F10 制度变迁与路径依赖（医改分析）","language":"text","code":"论文应用模板：医改制度变迁分析提纲\n1) 锁定起点：初始制度安排（如公费/劳保/新农合分轨起点）与关键节点（2009 新医改）\n2) 自我强化机制：规模报酬递增、沉没成本、利益集团、适应性预期\n3) 锁定证据：政策时间线——改革方案反复收敛于既有路径（如碎片化格局持续）\n4) 变迁触发：外部冲击（疫情、财政压力）或内部矛盾积累 → 制度突破/渐进调整","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1312}},{"id":"code-library:methods-54-68","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"G1 扎根理论（Grounded Theory）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-68","title":"G1 扎根理论（Grounded Theory）","language":"r","code":"library(quanteda)\ncorp <- corpus(data.frame(text = interviews$text, doc_id = interviews$id))\ndfm(corp, remove = stopwords(\"zh\"), remove_punct = TRUE) |>\n  textstat_frequency(n = 30)          # 高频词提示开放编码线索","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1337}},{"id":"code-library:methods-54-69","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"G2 结构方程模型（SEM）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-69","title":"G2 结构方程模型（SEM）","language":"stata","code":"* Stata：三潜变量 SEM\nsem (HL -> x1 x2 x3) (SE -> y1 y2 y3) (AD -> z1 z2 z3) (HL -> SE) (SE -> AD), method(mlmv)\nestat gof","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1356}},{"id":"code-library:methods-54-70","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"G2 结构方程模型（SEM）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-70","title":"G2 结构方程模型（SEM）","language":"r","code":"# R：lavaan 等价写法\nlibrary(lavaan)\nmodel <- ' HL =~ x1 + x2 + x3\n           SE =~ y1 + y2 + y3\n           AD =~ z1 + z2 + z3\n           SE ~ HL\n           AD ~ SE '\nfit <- sem(model, data = dat)\nfitMeasures(fit, c(\"cfi\",\"tli\",\"rmsea\",\"srmr\"))","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1362}},{"id":"code-library:methods-54-71","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"G3 多层 / 多重嵌套 SEM（Multilevel SEM）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-71","title":"G3 多层 / 多重嵌套 SEM（Multilevel SEM）","language":"stata","code":"* Stata：gsem 多层（医院随机截距）\ngsem (HL -> x1 x2 x3) (SE -> y1 y2 y3) (SE <- HL M1[hospital]), vce(cluster hospital)","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1386}},{"id":"code-library:methods-54-72","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"G3 多层 / 多重嵌套 SEM（Multilevel SEM）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-72","title":"G3 多层 / 多重嵌套 SEM（Multilevel SEM）","language":"r","code":"# R：lavaan 双层写法\nlibrary(lavaan)\nmodel2 <- '\n  level: 1\n    SEw =~ y1 + y2 + y3\n    SEw ~ HLw\n  level: 2\n    SEb =~ y1 + y2 + y3\n    SEb ~ HLb '\nfit2 <- sem(model2, data = dat, cluster = \"hospital\")","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1391}},{"id":"code-library:methods-54-73","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"G4 fsQCA 模糊集定性比较分析","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-73","title":"G4 fsQCA 模糊集定性比较分析","language":"r","code":"library(QCA)\ndat2 <- data.frame(成效 = calibrate(score, c(60, 75, 90)),\n                   资源 = calibrate(res, c(10, 20, 30)),\n                   人员 = calibrate(staff, c(5, 10, 15)),\n                   激励 = calibrate(inc, c(0.2, 0.5, 0.8)),\n                   信息化 = calibrate(it, c(1, 3, 5)))\ntt <- truthTable(dat2, outcome = \"成效\", n.cut = 1, incl.cut = 0.8)\nminimize(tt, include = \"?\")            # 中间解","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1416}},{"id":"code-library:methods-54-74","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"G4 fsQCA 模糊集定性比较分析","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"methods-54-74","title":"G4 fsQCA 模糊集定性比较分析","language":"stata","code":"* Stata：SSC 的 fuzzy 包做校准等（以 help fuzzy 为准）\nssc install fuzzy\nfzcalc zy, low(60) cross(75) high(90)","source":"skills/references/methods-54.md","status":"模板；未逐项执行","line":1427}},{"id":"code-library:academic-copyediting-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"输出格式","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"academic-copyediting-skill-1","title":"输出格式","language":"text","code":"【修改后全文】\n……\n\n【修改说明】\n1. 第 X 段：……（原因：……）\n2. ……\n\n【请作者人工复核】\n- 事实与数据（润色不改事实，需作者核对）\n- 引用出处\n- AI 使用披露是否已写入稿件","source":"skills/academic-copyediting-skill.md","status":"模板；未逐项执行","line":46}},{"id":"code-library:active-health-hiap-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"板块 1：理论框架（怎么写进论文与标书）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"active-health-hiap-skill-1","title":"板块 1：理论框架（怎么写进论文与标书）","language":"r","code":"library(quanteda)\ndocs <- readtext::readtext(\"policy_texts/*.txt\")          # 政策文件目录\ncorp <- corpus(docs)\ntoks <- tokens(corp, remove_punct = TRUE)\ndfmat <- dfm(toks)\nkw <- c(\"健康\", \"预防\", \"公平\", \"环境\", \"医疗\", \"保障\")\ndfm_select(dfmat, pattern = kw) |> textstat_frequency()    # 政策文本中的健康话语频次","source":"skills/active-health-hiap-skill.md","status":"模板；未逐项执行","line":48}},{"id":"code-library:active-health-hiap-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"板块 2：健康影响评估（HIA）方法","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"active-health-hiap-skill-2","title":"板块 2：健康影响评估（HIA）方法","language":"stata","code":"* Stata：PAF 与可避免负担的情景计算\nlocal p  0.35            // 基线暴露率\nlocal RR 1.45            // 暴露-反应 RR（示例，来自 Meta 分析）\nlocal paf = (`p'*(`RR'-1))/(`p'*(`RR'-1)+1)\ndi \"PAF = \" %5.3f `paf'\nlocal B 12000            // 基线年死亡数（示例）\ndi \"基线归因死亡 = \" %9.0f (`B'*`paf')\n* 干预使暴露率降至 0.25\nlocal p2 0.25\nlocal paf2 = (`p2'*(`RR'-1))/(`p2'*(`RR'-1)+1)\ndi \"干预后可避免死亡 = \" %9.0f (`B'*(`paf'-`paf2'))\n\n* 公平性：集中指数（协方差法）\negen r = rank(y)         // y 为健康结局，按经济状况排序后的秩\nsum y\ngen lhs = 2*y*r/r(mean)\nquietly reg lhs r\ndi \"CI = \" %5.3f _b[r]","source":"skills/active-health-hiap-skill.md","status":"模板；未逐项执行","line":72}},{"id":"code-library:active-health-hiap-skill-3","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"板块 2：健康影响评估（HIA）方法","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"active-health-hiap-skill-3","title":"板块 2：健康影响评估（HIA）方法","language":"r","code":"# R：同一计算 + 集中指数 + 情景比较\npaf <- function(p, RR) p*(RR-1)/(p*(RR-1)+1)\nB <- 12000\nbase  <- B * paf(0.35, 1.45)\nafter <- B * paf(0.25, 1.45)\navoid <- base - after\ncat(sprintf(\"基线归因死亡 %.0f；干预后可避免死亡 %.0f\\n\", base, avoid))\n\n# 集中指数\nci <- function(y, x) 2 * cov(y, rank(x)) / mean(y)\nci(health_y, income_x)","source":"skills/active-health-hiap-skill.md","status":"模板；未逐项执行","line":93}},{"id":"code-library:active-health-hiap-skill-4","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"板块 3：数据源（健康融入万策的证据底座）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"active-health-hiap-skill-4","title":"板块 3：数据源（健康融入万策的证据底座）","language":"r","code":"library(readxl)\ndf <- read_excel(\"GBD_results.xlsx\")     # 从 GBD Results Tool 导出的 CSV/XLSX\nagg <- aggregate(deaths ~ year + location, data = df, sum)\n# 与政策时点对齐：政策前后负担变化（配合 ITS/DID skill 做因果评估）","source":"skills/active-health-hiap-skill.md","status":"模板；未逐项执行","line":115}},{"id":"code-library:active-health-hiap-skill-5","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"输出格式（政策健康影响评估报告模板）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"active-health-hiap-skill-5","title":"输出格式（政策健康影响评估报告模板）","language":"text","code":"【政策与背景】\n政策名称、出台部门、目标与「健康融入万策」的关联点。\n\n【HIA 界定】\n范围、人群（含易受影响人群）、时间范围、评估者与利益相关者。\n\n【基线】\n暴露率、疾病负担、公平性现状（附数据源与版本）。\n\n【影响评估】\n暴露-反应证据（RR 与来源）；PAF 与可避免负担的情景计算；公平性影响（CI 与分解）；不确定性说明。\n\n【建议】\n可执行的政策修改建议（按证据强度分级）。\n\n【监测与评价】\n指标、数据来源与复查时点。","source":"skills/active-health-hiap-skill.md","status":"模板；未逐项执行","line":166}},{"id":"code-library:dce-design-assistant-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"软件准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-1","title":"软件准备","language":"stata","code":"* Stata（联网安装一次）\nssc install mixlogit, replace\nssc install lclogit2, replace\nssc install dcreate,  replace","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":25}},{"id":"code-library:dce-design-assistant-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"软件准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-2","title":"软件准备","language":"r","code":"# R\ninstall.packages(c(\"DoE.base\", \"AlgDesign\", \"support.CEs\", \"mlogit\", \"gmnl\"))","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":32}},{"id":"code-library:dce-design-assistant-skill-3","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 1 步：确定属性与水平","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-3","title":"第 1 步：确定属性与水平","language":"stata","code":"* Stata：生成 2^4 全因子（16 行），用于观察所有组合\nclear\nset obs 16\ngen a = mod(_n-1, 2)              // 0/1\ngen b = mod(int((_n-1)/2), 2)\ngen c = mod(int((_n-1)/4), 2)\ngen d = mod(int((_n-1)/8), 2)\nlist","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":49}},{"id":"code-library:dce-design-assistant-skill-4","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 1 步：确定属性与水平","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-4","title":"第 1 步：确定属性与水平","language":"r","code":"# R：属性水平表与全因子规模\nlevels <- list(费用 = c(10, 30, 50),   # 元/次\n               候诊 = c(1, 3, 7),      # 天\n               连续性 = c(0, 1),       # 0=不同医生 1=同一医生\n               目录 = c(0, 1))         # 0=基础目录 1=扩展目录\nsapply(levels, length)                 # 各属性水平数\nfull <- expand.grid(levels)            # 全因子组合\nnrow(full)                             # 3*3*2*2 = 36","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":60}},{"id":"code-library:dce-design-assistant-skill-5","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 2 步：正交设计与 D-效率设计","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-5","title":"第 2 步：正交设计与 D-效率设计","language":"stata","code":"* Stata：2^(4-1) 分辨率 IV 正交主效应设计（8 行，生成元 d = a*b*c 模 2）\nclear\nset obs 16\ngen a = mod(_n-1, 2)\ngen b = mod(int((_n-1)/2), 2)\ngen c = mod(int((_n-1)/4), 2)\ngen d = mod(int((_n-1)/8), 2)\nkeep if mod(a+b+c+d, 2) == 0\nlist, sep(0)\n\n* Stata：3^4 的 L9 正交设计（9 行）\nclear\nset obs 27\ngen a = mod(_n-1, 3)\ngen b = mod(int((_n-1)/3), 3)\ngen c = mod(int((_n-1)/9), 3)\nkeep if mod(a+b+c, 3) == 0\n\n* Stata：D-效率设计（dcreate，SSC）。示例：3 个属性、每选择集 2 个方案、12 个选择集\nmatrix b0 = J(1, 3, 0)                    // 先验参数（默认 0）\ndcreate i.x1 i.x2 i.x3, nalt(2) nset(12) bmat(b0) seed(2026)\n* 注：dcreate 要求因子变量写法；详细选项以 help dcreate 为准","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":83}},{"id":"code-library:dce-design-assistant-skill-6","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 2 步：正交设计与 D-效率设计","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-6","title":"第 2 步：正交设计与 D-效率设计","language":"r","code":"# R：正交主效应设计（DoE.base）\nlibrary(DoE.base)\ndes24 <- oa.design(nlevels = c(2, 2, 2, 2))    # 2^4 → 8 行 L8\ndes33 <- oa.design(nlevels = c(3, 3, 3, 3))    # 3^4 → 9 行 L9\nas.data.frame(des24)\n\n# R：D-效率设计（AlgDesign）\nlibrary(AlgDesign)\nfull36 <- expand.grid(x1 = c(0, 1, 2), x2 = c(0, 1, 2), x3 = c(0, 1), x4 = c(0, 1))\ndopt <- optFederov(~ ., data = full36, nTrials = 16, criterion = \"D\")\ndopt$design                                  # 选中的 16 个 profile\ndopt$D                                       # D-效率值","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":108}},{"id":"code-library:dce-design-assistant-skill-7","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 3 步：检查设计质量","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-7","title":"第 3 步：检查设计质量","language":"stata","code":"* Stata：水平平衡 + 两两相关\ntab1 a b c d\npwcorr a b c d","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":131}},{"id":"code-library:dce-design-assistant-skill-8","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 3 步：检查设计质量","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-8","title":"第 3 步：检查设计质量","language":"r","code":"# R：水平平衡、相关矩阵与设计效率\napply(des24, 2, table)                       # 水平平衡\nround(cor(as.data.frame(des24)), 3)          # 正交性：非对角应 ≈ 0\neval.design(~ ., dopt$design)                # AlgDesign：方差膨胀、D/G 效率","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":137}},{"id":"code-library:dce-design-assistant-skill-9","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 4 步：构造选择集与分块","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-9","title":"第 4 步：构造选择集与分块","language":"r","code":"# R：随机配对、分块\nlibrary(dplyr)\nset.seed(2026)\nprof  <- as.data.frame(des24) |> sample_frac(1)      # 随机打乱\npairs <- matrix(seq_len(nrow(prof)), ncol = 2, byrow = TRUE)\nnset  <- nrow(pairs)                                  # 选择集数\nnblock <- 2; per <- 2                                 # 2 个块、每块 2 题\nblock  <- rep(1:nblock, each = per, length.out = nset)\nout <- data.frame(choice_set = rep(1:nset, each = 2),\n                  block = rep(block, each = 2),\n                  alt = rep(1:2, times = nset),\n                  prof[as.vector(t(pairs)), ])\n# opt-out 在问卷中额外提供“都不选”；数据里记 alt == 3、各属性 = 0","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":152}},{"id":"code-library:dce-design-assistant-skill-10","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"opt-out 在问卷中额外提供“都不选”；数据里记 alt == 3、各属性 = 0","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-10","title":"opt-out 在问卷中额外提供“都不选”；数据里记 alt == 3、各属性 = 0","language":"stata","code":"* Stata：为每个选择集追加 opt-out 行（alt == 3，属性全为 0）\nlocal nset 4\nforvalues s = 1/`nset' {\n    set obs `=_N+1'\n    replace choice_set = `s' if _n == _N\n    replace alt = 3 if _n == _N\n    foreach v in a b c d {\n        replace `v' = 0 if _n == _N\n    }\n}","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":168}},{"id":"code-library:dce-design-assistant-skill-11","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 5 步：问卷与预测试","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-11","title":"第 5 步：问卷与预测试","language":"stata","code":"* Stata：预测试数据体检——受访者选择 opt-out 的次数分布\negen n_optout = total(choice == 1 & alt == 3), by(id)\ntab n_optout\n* 提示：某受访者所有题都选 opt-out，说明属性或任务设计可能有问题","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":189}},{"id":"code-library:dce-design-assistant-skill-12","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 5 步：问卷与预测试","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-12","title":"第 5 步：问卷与预测试","language":"r","code":"# R：同一检查\nlibrary(dplyr)\ndat |> group_by(id) |>\n  summarise(n_optout = sum(choice == 1 & alt == 3),\n            n_A      = sum(choice == 1 & alt == 1),\n            n_total  = n_distinct(choice_set))","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":196}},{"id":"code-library:dce-design-assistant-skill-13","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 6 步：样本量与 long 数据整理","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-13","title":"第 6 步：样本量与 long 数据整理","language":"stata","code":"* Stata：示例 long 数据（1 名受访者、2 个选择集、各 3 个方案）\nclear\ninput id choice_set alt choice a b c d\n1 1 1 1 0 0 1 1\n1 1 2 0 1 1 0 0\n1 1 3 0 0 0 0 0\n1 2 1 0 1 0 1 0\n1 2 2 1 0 1 0 1\n1 2 3 0 0 0 0 0\nend\n\n* 宽表 → 长表（宽表每行一个选择集，选项变量带后缀 _1/_2/_3）\nreshape long choice_ a_ b_ c_ d_, i(id choice_set) j(alt)\nrename (choice_ a_ b_ c_ d_) (choice a b c d)\n\n* effects coding 示例：三水平属性 a（0/1/2 → -1/0/1）\nrecode a (0 = -1) (1 = 0) (2 = 1)","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":217}},{"id":"code-library:dce-design-assistant-skill-14","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 6 步：样本量与 long 数据整理","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-14","title":"第 6 步：样本量与 long 数据整理","language":"r","code":"# R：dfidx 构造选择数据（mlogit 用）\nlibrary(mlogit)\nmd <- dfidx(dat, idx = c(\"id\", \"alt\"), choice = \"choice\")","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":237}},{"id":"code-library:dce-design-assistant-skill-15","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"7.1 条件 logit（`clogit`）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-15","title":"7.1 条件 logit（`clogit`）","language":"stata","code":"clogit choice a b c d, group(choice_set) vce(cluster id)\nest store clogit\n\n* WTP：d 为费用属性时的属性 b 的 WTP（delta 方法，含 95% CI）\nnlcom wtp_b: -_b[b] / _b[d]\n\n* IIA 直觉检验：删除 opt-out 后系数应大体稳定\nclogit choice a b c d if alt != 3, group(choice_set) vce(cluster id)","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":253}},{"id":"code-library:dce-design-assistant-skill-16","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"7.1 条件 logit（`clogit`）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-16","title":"7.1 条件 logit（`clogit`）","language":"r","code":"# R：clogit（survival）与 mlogit 等价实现\nlibrary(survival)\nclogit(choice ~ a + b + c + d + strata(choice_set), data = dat)\n\nlibrary(mlogit)\nm1 <- mlogit(choice ~ a + b + c + d | 0, data = md)\nsummary(m1)","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":264}},{"id":"code-library:dce-design-assistant-skill-17","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"7.2 混合 logit（`mixlogit`）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-17","title":"7.2 混合 logit（`mixlogit`）","language":"stata","code":"* 随机系数：a、b、c 正态，d 固定\nmixlogit choice a b c d, group(choice_set) id(id) rand(a b c) nrep(500)\nest store mixl\nfitstat                                     // AIC/BIC，与 clogit 比较\n\n* 对数正态费用 + 相关随机参数（ln(1) = rand() 中第 1 个随机参数取对数正态）\nmixlogit choice a b c d, group(choice_set) id(id) rand(a b c) ln(1) corr nrep(500)\n\n* WTP：mixlwtp（SSC）或 delta 方法；具体语法以 help mixlwtp 为准","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":280}},{"id":"code-library:dce-design-assistant-skill-18","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"7.2 混合 logit（`mixlogit`）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-18","title":"7.2 混合 logit（`mixlogit`）","language":"r","code":"# R：混合 logit（mlogit rpar）\nm2 <- mlogit(choice ~ a + b + c + d | 0, data = md,\n             rpar = c(a = \"n\", b = \"n\", c = \"n\"),\n             R = 500, halton = NA, panel = TRUE)\nsummary(m2)","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":292}},{"id":"code-library:dce-design-assistant-skill-19","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"7.3 潜类别 logit（latent class，`lclogit2`）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-19","title":"7.3 潜类别 logit（latent class，`lclogit2`）","language":"stata","code":"lclogit2 choice a b c d, group(choice_set) id(id) nclasses(2) seed(2026)\nlclogit2, nclasses(3) seed(2026)            // 比较 BIC/AIC 选类别数\nest store lc3\nlclogitprob, cpprob(3)                      // 各类别平均成员概率\nestat lcmean, post                          // 各类别协变量均值（刻画类别特征）\n\n* 类成员协变量模型（membership 方程放入 age、income）\nlclogitml choice a b c d, group(choice_set) id(id) nclasses(3) membership(age income)\n\n* WTP：lcwtp（lclogit2 附带），语法以 help lcwtp 为准；\n* 或手工取各类别系数比值并配合 delta 方法","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":306}},{"id":"code-library:dce-design-assistant-skill-20","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"7.3 潜类别 logit（latent class，`lclogit2`）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-20","title":"7.3 潜类别 logit（latent class，`lclogit2`）","language":"r","code":"# R：潜类别 logit（gmnl）\nlibrary(gmnl)\nlc <- gmnl(choice ~ a + b + c + d | 0 | 0 | 0 | 1, data = md,\n           model = \"lc\", panel = TRUE, Q = 3)\nsummary(lc)","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":320}},{"id":"code-library:dce-design-assistant-skill-21","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 8 步：结果解读与报告","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-21","title":"第 8 步：结果解读与报告","language":"stata","code":"* WTP 与置信区间（clogit，delta 方法）\nnlcom wtp_b: -_b[b] / _b[d], post\nestimates store wtp_b\n\n* 三模型并列输出\nestimates table clogit mixl lc3, b(%9.3f) se stats(N aic bic)","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":341}},{"id":"code-library:dce-design-assistant-skill-22","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 8 步：结果解读与报告","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-22","title":"第 8 步：结果解读与报告","language":"r","code":"# R：delta 方法 WTP（car::deltaMethod）\nlibrary(car)\ndeltaMethod(coef(m1), \"-b/d\")               # 按实际系数名替换 b、d","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":350}},{"id":"code-library:dce-design-assistant-skill-23","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"输出格式（分析报告模板）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"dce-design-assistant-skill-23","title":"输出格式（分析报告模板）","language":"text","code":"【设计与数据】\n属性/水平表；设计类型（正交 / D-效率）与效率值；选择集与块数；预测试结果；样本量与法则依据。\n\n【模型估计】\n表 1：clogit 主结果；表 2：mixlogit 均值 + SD；表 3：潜类别各类别系数与成员概率（附 AIC/BIC 选 K 的依据）。\n\n【WTP】\nWTP = -β_x / β_cost 及 95% CI（注明 delta / Krinsky-Robb 方法）。\n\n【结论与局限】\n方向性结论 + 偏好异质性 + 局限（IIA、分布假设、设计假设、外部效度）。","source":"skills/dce-design-assistant-skill.md","status":"模板；未逐项执行","line":371}},{"id":"code-library:ddd-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"软件准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"ddd-skill-1","title":"软件准备","language":"stata","code":"* Stata（联网安装一次）\nssc install reghdfe, replace\n* 若 ftools 未被自动装上：ssc install ftools, replace","source":"skills/ddd-skill.md","status":"模板；未逐项执行","line":25}},{"id":"code-library:ddd-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"软件准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"ddd-skill-2","title":"软件准备","language":"r","code":"# R\ninstall.packages(\"fixest\")","source":"skills/ddd-skill.md","status":"模板；未逐项执行","line":31}},{"id":"code-library:ddd-skill-3","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 0 步：构造模拟面板数据（贯穿全流程）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"ddd-skill-3","title":"第 0 步：构造模拟面板数据（贯穿全流程）","language":"stata","code":"* ============ 第 0 步：模拟面板数据（贯穿全流程） ============\nclear all\nset seed 2026\n\n* 60 家医院：id 1-30 公立（treated = 1），31-60 民营（treated = 0）\nset obs 60\ngen id = _n\ngen treated = (id <= 30)          // 处理维度：1 = 公立医院（受集采约束）\ngen u_i = rnormal(0, 4)           // 医院层面随机效应（制造院内相关，聚类依据）\n\n* 每家医院 2 类药品：group = 1 集采品种，0 非集采品种\nexpand 2\nbysort id: gen group = _n - 1\n\n* 每个（医院 × 品种）2 期：post = 0 政策前，1 政策后\nexpand 2\nbysort id group: gen post = _n - 1\ngen time = post                   // 供 reghdfe absorb() 使用\n\n* ---------- 真实数据生成过程（DGP，内置真实 DDD 效应 δ = -8） ----------\n* y = γ0 + γ1·treated + γ2·post + γ3·group\n*     + γ12·treated×post + γ13·treated×group + γ23·post×group\n*     + δ·treated×post×group + u_i + ε\nlocal g0 100                        // 基组均值（民营 × 非集采 × 政策前）\nlocal g1 10                         // 公立医院整体费用水平更高\nlocal g2 -5                         // 全体时间趋势（普遍控费）\nlocal g3 20                         // 集采品种用量大、费用更高\nlocal g12 -6                        // 偏误 1：公立医院特有趋势（DRG / 取消加成等）\nlocal g13 5                         // 公立医院对集采品种的固定偏好\nlocal g23 -4                        // 偏误 2：集采品种特有趋势（专利到期等）\nlocal delta -8                      // 真实 DDD 效应：集采使公立医院集采品种额外下降 8 万元/月\n\ngen y = `g0' + `g1'*treated + `g2'*post + `g3'*group + ///\n        `g12'*treated*post + `g13'*treated*group + `g23'*post*group + ///\n        `delta'*treated*post*group + u_i + rnormal(0, 2)\n\n* ---------- 稳健性结局（第 4 步用） ----------\n* y_qty：集采品种采购数量（相关结局，真实效应较小 δ = -3）\ngen y_qty = 60 + 8*treated - 4*post + 18*group - 3*treated*post + ///\n            4*treated*group - 2*post*group - 3*treated*post*group + ///\n            u_i + rnormal(0, 3)\n* y_dev：与药品无关的耗材费用（安慰剂结局，真实 δ = 0）\ngen y_dev = 40 + 5*treated - 2*post + 2*group + 1*treated*post - ///\n            1*treated*group + 0.5*post*group + 0*treated*post*group + ///\n            0.5*u_i + rnormal(0, 5)\n\n* 数据体检：8 组均值表（Stata 17+；16 及更早用 bysort treated post group: summarize y）\ntable treated post group, stat(mean y) stat(sd y) nototals\n* 期望组均值（噪声平均掉后）：民营 非集采 前 100 / 后 95；民营 集采 前 120 / 后 111；\n*                             公立 非集采 前 110 / 后 99；公立 集采 前 135 / 后 112","source":"skills/ddd-skill.md","status":"模板；未逐项执行","line":48}},{"id":"code-library:ddd-skill-4","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 0 步：构造模拟面板数据（贯穿全流程）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"ddd-skill-4","title":"第 0 步：构造模拟面板数据（贯穿全流程）","language":"r","code":"# ============ 第 0 步：模拟面板数据（贯穿全流程） ============\nset.seed(2026)\nN <- 60                                     # 60 家医院\ndat <- expand.grid(id = 1:N, group = c(0, 1), post = c(0, 1))\ndat$treated <- as.integer(dat$id <= 30)     # 处理维度：1 = 公立医院\ndat$u_i     <- rnorm(N, 0, 4)[dat$id]       # 医院层面随机效应（聚类依据）\ndat$time    <- dat$post                     # 供 fixest 吸收固定效应\n\n# 真实参数（内置真实 DDD 效应 delta = -8）\ng0 <- 100; g1 <- 10; g2 <- -5; g3 <- 20\ng12 <- -6; g13 <- 5; g23 <- -4; delta <- -8\n\ndat$y <- with(dat, g0 + g1*treated + g2*post + g3*group +\n                   g12*treated*post + g13*treated*group + g23*post*group +\n                   delta*treated*post*group + u_i + rnorm(nrow(dat), 0, 2))\n\n# 稳健性结局：y_qty（相关结局，真实 δ = -3）；y_dev（安慰剂结局，真实 δ = 0）\ndat$y_qty <- with(dat, 60 + 8*treated - 4*post + 18*group - 3*treated*post +\n                      4*treated*group - 2*post*group - 3*treated*post*group +\n                      u_i + rnorm(nrow(dat), 0, 3))\ndat$y_dev <- with(dat, 40 + 5*treated - 2*post + 2*group + 1*treated*post -\n                      1*treated*group + 0.5*post*group + 0*treated*post*group +\n                      0.5*u_i + rnorm(nrow(dat), 0, 5))\n\n# 8 组均值表（应与下方期望值接近）\naggregate(y ~ treated + post + group, data = dat, FUN = mean)","source":"skills/ddd-skill.md","status":"模板；未逐项执行","line":101}},{"id":"code-library:ddd-skill-5","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 1 步：从 DID 到 DDD：动机","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"ddd-skill-5","title":"第 1 步：从 DID 到 DDD：动机","language":"stata","code":"* 图形直觉：四组均值线（先平均到 8 个单元格）\npreserve\ncollapse (mean) y, by(treated post group)\ntwoway (connected y post if treated==1 & group==1, sort) ///\n       (connected y post if treated==1 & group==0, sort) ///\n       (connected y post if treated==0 & group==1, sort) ///\n       (connected y post if treated==0 & group==0, sort), ///\n       legend(order(1 \"公立·集采\" 2 \"公立·非集采\" 3 \"民营·集采\" 4 \"民营·非集采\")) ///\n       xtitle(\"post（0 政策前 / 1 政策后）\") ytitle(\"月均药品费用（万元）\")\nrestore","source":"skills/ddd-skill.md","status":"模板；未逐项执行","line":152}},{"id":"code-library:ddd-skill-6","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 1 步：从 DID 到 DDD：动机","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"ddd-skill-6","title":"第 1 步：从 DID 到 DDD：动机","language":"r","code":"# 图形直觉：四组均值线（两条 DID 线的斜率差之差 = DDD）\ncell <- aggregate(y ~ treated + post + group, data = dat, FUN = mean)\nplot(NA, xlim = c(0, 1), ylim = range(cell$y),\n     xlab = \"post（0 政策前 / 1 政策后）\", ylab = \"月均药品费用 y（万元）\",\n     main = \"四组均值线：DDD = 实线斜率差 - 虚线斜率差\")\nlines(y ~ post, data = cell, subset = treated == 1 & group == 1, type = \"b\", col = \"red\",  lty = 1)\nlines(y ~ post, data = cell, subset = treated == 1 & group == 0, type = \"b\", col = \"blue\", lty = 1)\nlines(y ~ post, data = cell, subset = treated == 0 & group == 1, type = \"b\", col = \"red\",  lty = 2)\nlines(y ~ post, data = cell, subset = treated == 0 & group == 0, type = \"b\", col = \"blue\", lty = 2)\nlegend(\"topright\", c(\"公立·集采\", \"公立·非集采\", \"民营·集采\", \"民营·非集采\"),\n       col = c(\"red\", \"blue\", \"red\", \"blue\"), lty = c(1, 1, 2, 2), bty = \"n\")","source":"skills/ddd-skill.md","status":"模板；未逐项执行","line":165}},{"id":"code-library:ddd-skill-7","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 2 步：模型设定与估计（Stata / R 对应）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"ddd-skill-7","title":"第 2 步：模型设定与估计（Stata / R 对应）","language":"stata","code":"* ---------- 模型 A：pooled OLS + 医院聚类稳健标准误 ----------\nreg y i.treated##i.post##i.group, vce(cluster id)\nest store ddd_ols\n\n* ---------- 模型 B：医院 + 时间固定效应（reghdfe） ----------\nreghdfe y i.treated##i.post##i.group, absorb(id time) vce(cluster id)\nest store ddd_fe\n* 说明：treated、post 主效应被吸收（与 id / time 固定效应共线，输出中标注）；\n*       两两交互与三重交互仍可估，δ 与模型 A 相同","source":"skills/ddd-skill.md","status":"模板；未逐项执行","line":213}},{"id":"code-library:ddd-skill-8","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 2 步：模型设定与估计（Stata / R 对应）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"ddd-skill-8","title":"第 2 步：模型设定与估计（Stata / R 对应）","language":"r","code":"library(fixest)\n\n# 模型 A：pooled OLS + 医院聚类稳健标准误（不吸收固定效应）\nm_ols <- feols(y ~ treated*post*group, cluster = ~id, data = dat)\nsummary(m_ols)\n\n# 模型 B：医院 + 时间固定效应（treated、post 因共线被自动移除）\nm_fe <- feols(y ~ treated*post*group | id + time, cluster = ~id, data = dat)\nsummary(m_fe)\n\n# 三重交互系数 δ（fixest 中交互用 \":\" 连接，对应 Stata 的 #）\ncoef(m_fe)[\"treated:post:group\"]","source":"skills/ddd-skill.md","status":"模板；未逐项执行","line":225}},{"id":"code-library:ddd-skill-9","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 3 步：核心系数 δ 的解读与分解验证","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"ddd-skill-9","title":"第 3 步：核心系数 δ 的解读与分解验证","language":"stata","code":"* ---------- 3.1 两个 2×2 DID（品种类型 DID，在公立/民营切片上） ----------\nreg y i.post##i.group if treated == 1, vce(cluster id)\nlocal did1 = _b[1.post#1.group]          // 期望 ≈ -12（= γ23 + δ）\nreg y i.post##i.group if treated == 0, vce(cluster id)\nlocal did2 = _b[1.post#1.group]          // 期望 ≈ -4（= γ23）\n\n* ---------- 3.2 分解：DDD = DID1 - DID2 ----------\ndisplay \"DID1(品种|公立) = \" %5.2f `did1'\ndisplay \"DID2(品种|民营) = \" %5.2f `did2'\ndisplay \"DDD = DID1 - DID2 = \" %5.2f (`did1' - `did2')\n\n* ---------- 3.3 镜像分解（医院类型 DID，在集采/非集采切片上） ----------\nreg y i.treated##i.post if group == 1, vce(cluster id)\nlocal did1b = _b[1.treated#1.post]       // 期望 ≈ -14（= γ12 + δ）\nreg y i.treated##i.post if group == 0, vce(cluster id)\nlocal did2b = _b[1.treated#1.post]       // 期望 ≈ -6（= γ12）\ndisplay \"镜像分解：DID(医院|集采) - DID(医院|非集采) = \" %5.2f (`did1b' - `did2b')\n\n* ---------- 3.4 与主回归三重交互系数对照（期望 ≈ -8） ----------\nreg y i.treated##i.post##i.group, vce(cluster id)\ndisplay \"主回归三重交互系数 δ = \" %5.2f _b[1.treated#1.post#1.group]","source":"skills/ddd-skill.md","status":"模板；未逐项执行","line":253}},{"id":"code-library:ddd-skill-10","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 3 步：核心系数 δ 的解读与分解验证","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"ddd-skill-10","title":"第 3 步：核心系数 δ 的解读与分解验证","language":"r","code":"# 3.1 两个 2×2 DID（品种类型 DID 在公立/民营切片上）\ndid1 <- coef(feols(y ~ post*group | id, cluster = ~id, data = dat[dat$treated == 1, ]))[\"post:group\"]\ndid2 <- coef(feols(y ~ post*group | id, cluster = ~id, data = dat[dat$treated == 0, ]))[\"post:group\"]\nc(DID1 = unname(did1), DID2 = unname(did2), DDD = unname(did1 - did2))\n# 期望：DID1 ≈ -12，DID2 ≈ -4，DDD ≈ -8 = δ\n\n# 3.2 镜像分解（医院类型 DID 在集采/非集采切片上），期望 ≈ -8\nd1 <- coef(feols(y ~ treated*post | id, cluster = ~id, data = dat[dat$group == 1, ]))[\"treated:post\"]\nd2 <- coef(feols(y ~ treated*post | id, cluster = ~id, data = dat[dat$group == 0, ]))[\"treated:post\"]\nunname(d1 - d2)\n\n# 3.3 与主回归三重交互系数对照（期望 ≈ -8）\ncoef(m_fe)[\"treated:post:group\"]","source":"skills/ddd-skill.md","status":"模板；未逐项执行","line":277}},{"id":"code-library:ddd-skill-11","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 4 步：平行趋势与稳健性（识别假设、动态检验、安慰剂、替换结局）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"ddd-skill-11","title":"第 4 步：平行趋势与稳健性（识别假设、动态检验、安慰剂、替换结局）","language":"stata","code":"* ---------- 动态检验思路（示意，需多期数据；本模拟两期无法执行） ----------\n* 假设 time = -2..2，政策时点 = 0，基准期 = -1：\n* reghdfe y ib(-1).time##i.treated##i.group, absorb(id) vce(cluster id)\n* 关注各期 1.time#1.treated#1.group 系数（政策前应 ≈ 0）\n* 画图：ssc install coefplot; coefplot, keep(*.time#1.treated#1.group) vertical yline(0)\n* 具体 keep 匹配规则以 help coefplot 为准\n\n* ---------- 安慰剂 1：伪第三维度（Stata permute，200 次） ----------\n* 随机置换 group，破坏品种维度结构；真实 δ = -8 应落在置换分布左尾之外\npermute group delta = _b[1.treated#1.post#1.group], ///\n        reps(200) seed(2026): reg y i.treated##i.post##i.group, vce(cluster id)\n\n* ---------- 安慰剂 2：伪时点（示意，需 time = 0..3 的多期数据） ----------\n* gen post_fake = (time == 1)        // 把政策提前到 t=1，只用真政策前数据\n* reg y i.treated##i.post_fake##i.group if time < 2, vce(cluster id)  // 期望 δ ≈ 0\n\n* ---------- 替换结局 ----------\nreghdfe y_qty i.treated##i.post##i.group, absorb(id time) vce(cluster id)  // 相关结局：期望 δ ≈ -3\nreghdfe y_dev i.treated##i.post##i.group, absorb(id time) vce(cluster id)  // 安慰剂结局：期望 δ ≈ 0\n\n* ---------- 聚类层级敏感性：多向聚类（reghdfe 支持） ----------\nreghdfe y i.treated##i.post##i.group, absorb(id time) vce(cluster id time)","source":"skills/ddd-skill.md","status":"模板；未逐项执行","line":313}},{"id":"code-library:ddd-skill-12","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 4 步：平行趋势与稳健性（识别假设、动态检验、安慰剂、替换结局）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"ddd-skill-12","title":"第 4 步：平行趋势与稳健性（识别假设、动态检验、安慰剂、替换结局）","language":"r","code":"# ---------- 安慰剂 1（R 版）：伪第三维度，医院内随机交换品种标签，200 次 ----------\nset.seed(2026)\nperm_delta <- replicate(200, {\n  d <- dat\n  d$group_p <- ave(d$group, d$id, FUN = sample)      # 每家医院内部重排，保留医院结构\n  coef(feols(y ~ treated*post*group_p | id + time, data = d))[\"treated:post:group_p\"]\n})\nhist(perm_delta, main = \"伪第三维度：δ 的置换分布（200 次）\")\nabline(v = -8, col = \"red\", lwd = 2)                 # 真实 δ = -8 应落在左尾之外\nmean(perm_delta <= -8)                               # 置换单侧 p 值（期望接近 0）\n\n# ---------- 替换结局 ----------\ncoef(feols(y_qty ~ treated*post*group | id + time, cluster = ~id, data = dat))[\"treated:post:group\"]  # 期望 ≈ -3\ncoef(feols(y_dev ~ treated*post*group | id + time, cluster = ~id, data = dat))[\"treated:post:group\"]  # 期望 ≈ 0\n\n# 伪时点（示意，需 ≥3 期数据）：把政策时点前移后用真政策前数据估计 δ，期望 ≈ 0；\n# 写法同上，仅替换 post 变量与子样本（以 fixest 帮助文档为准）","source":"skills/ddd-skill.md","status":"模板；未逐项执行","line":338}},{"id":"code-library:ddd-skill-13","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 6 步：报告清单","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"ddd-skill-13","title":"第 6 步：报告清单","language":"stata","code":"* 主回归表：逐步（仅主效应 → +两两交互 → +三重交互）\nreg y i.treated i.post i.group, vce(cluster id)\nest store m1\nreg y i.treated i.post i.group i.treated#i.post i.treated#i.group i.post#i.group, vce(cluster id)\nest store m2\nreg y i.treated##i.post##i.group, vce(cluster id)\nest store m3\nestimates table m1 m2 m3, b(%9.2f) se stats(N)\n* 报告时另附 95% CI（reg 输出自带）与聚类数（60 家医院）","source":"skills/ddd-skill.md","status":"模板；未逐项执行","line":379}},{"id":"code-library:ddd-skill-14","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 6 步：报告清单","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"ddd-skill-14","title":"第 6 步：报告清单","language":"r","code":"# 主回归表：逐步（fixest 的 etable；模型 SE 已在估计时按 id 聚类）\nm1 <- feols(y ~ treated + post + group, cluster = ~id, data = dat)\nm2 <- feols(y ~ treated*post + treated*group + post*group, cluster = ~id, data = dat)\nm3 <- feols(y ~ treated*post*group, cluster = ~id, data = dat)\netable(m1, m2, m3)          # 选项以 ?etable 为准","source":"skills/ddd-skill.md","status":"模板；未逐项执行","line":391}},{"id":"code-library:ddd-skill-15","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"输出格式（DDD 报告模板）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"ddd-skill-15","title":"输出格式（DDD 报告模板）","language":"text","code":"【三维定义】\n时间维度（政策时点及文件依据）；处理维度（受政策约束 vs 不受约束）；第三维度（分层依据）；\n第三维度选择论证（外生性、偏误同质性理由）。\n\n【描述统计】\n表 1：2×2×2 八个单元格的均值、SD、N（公立/民营 × 集采/非集采 × 政策前/后）。\n\n【主回归】\n表 2：逐步回归（主效应 → +两两交互 → +三重交互），报 δ、聚类稳健 SE、95% CI、N、聚类数；\n固定效应版本（reghdfe / feols）与 pooled OLS 并列。\n\n【DDD 分解】\n表 3：DID1（维度 A 的 2×2 DID，第三维度切片 1）− DID2（第三维度切片 0）＝ δ；\n标注两个偏误的方向与大小；附镜像分解。\n\n【稳健性】\n表 4：伪第三维度（置换 p 值）、伪时点（若多期）、替换结局、事件研究图（若多期）、\n聚类层级敏感性（多向聚类）。\n\n【局限】\n识别假设（偏误同质）论证；第三维度与政策相关性；溢出可能（价格联动、替代效应）；\n8 单元格样本均衡性；外部效度。","source":"skills/ddd-skill.md","status":"模板；未逐项执行","line":418}},{"id":"code-library:did-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"软件准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-1","title":"软件准备","language":"stata","code":"* Stata（联网安装一次）\nssc install csdid,    replace    // 交错处理 DID（Callaway-Sant'Anna）\nssc install drdid,    replace    // 双重稳健 DID（csdid 的依赖）\nssc install reghdfe,  replace    // 高维固定效应（事件研究回归用）\nssc install ftools,   replace    // reghdfe 的依赖\nssc install coefplot, replace    // 系数图\nssc install estout,   replace    // 结果表\nssc install ritest,   replace    // 随机化推断（安慰剂检验备选）\nssc install psmatch2, replace    // 倾向得分匹配","source":"skills/did-skill.md","status":"模板；未逐项执行","line":27}},{"id":"code-library:did-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"软件准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-2","title":"软件准备","language":"r","code":"# R\ninstall.packages(c(\"fixest\", \"did\", \"MatchIt\", \"ggplot2\", \"cobalt\", \"modelsummary\"))","source":"skills/did-skill.md","status":"模板；未逐项执行","line":39}},{"id":"code-library:did-skill-3","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 0 步：生成贯穿全文的模拟面板数据","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-3","title":"第 0 步：生成贯穿全文的模拟面板数据","language":"stata","code":"* ============================================================\n* 模拟面板：60 地区 × 4 期；30 个处理组交错入组；真实 ATT = -150\n* ============================================================\nclear all\nset seed 2026\nset obs 60\ngen id = _n\n* 处理时点 g（0 = 永不受理）\ngen g = 0\nreplace g = 2 if inrange(id, 1, 10)\nreplace g = 3 if inrange(id, 11, 20)\nreplace g = 4 if inrange(id, 21, 30)\ngen treated = (g > 0)\n* 地区特征（时不变）：固定效应、自身趋势、可观测协变量\ngen alpha = rnormal(900, 50)        // 地区平均次均费用水平\ngen beta  = rnormal(-8, 4)          // 地区自身趋势（元/期）\ngen x1 = rnormal(0.3*treated, 1)    // 协变量 1：与入选相关\ngen x2 = rnormal(0, 1)              // 协变量 2\n* 4 期面板\nexpand 4\nbysort id: gen t = _n\ngen lambda = -30 * (t - 1)          // 共同时间趋势\ngen D = (t >= g & g > 0)            // 处理指示\ngen y   = alpha + beta*(t-1) + lambda + (-150)*D + rnormal(0, 40)           // 次均药品费用\ngen adm = 0.20 - 0.01*(t-1) + (alpha-900)/3000 + (-0.02)*D + rnormal(0, 0.04)  // 住院率\nxtset id t\nsave \"did_demo.dta\", replace\n* 数据体检\nxtdes                               // 面板结构：60 个地区 × 4 期、无缺失\ntab g                               // 队列规模：g=2/3/4 各 10，g=0 共 30\ntabstat y if t == 1, by(treated) stat(n mean sd)   // 干预前基线比较","source":"skills/did-skill.md","status":"模板；未逐项执行","line":66}},{"id":"code-library:did-skill-4","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 0 步：生成贯穿全文的模拟面板数据","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-4","title":"第 0 步：生成贯穿全文的模拟面板数据","language":"r","code":"# ============================================================\n# 模拟面板：60 地区 × 4 期；30 个处理组交错入组；真实 ATT = -150\n# ============================================================\nset.seed(2026)\nN <- 60; Tmax <- 4\nid  <- 1:N\ng   <- c(rep(2, 10), rep(3, 10), rep(4, 10), rep(0, 30))   # 处理时点；0 = 永不受理\ntreated <- as.integer(g > 0)\nalpha <- rnorm(N, 900, 50)          # 地区固定效应\nbeta  <- rnorm(N, -8, 4)            # 地区自身趋势\nx1 <- rnorm(N, 0.3 * treated, 1)    # 协变量 1：与入选相关\nx2 <- rnorm(N, 0, 1)                # 协变量 2\n\ndat <- expand.grid(id = id, t = 1:Tmax)\ndat$g <- g[dat$id]; dat$treated <- treated[dat$id]\ndat$alpha <- alpha[dat$id]; dat$beta <- beta[dat$id]\ndat$x1 <- x1[dat$id]; dat$x2 <- x2[dat$id]\ndat$D <- as.integer(dat$t >= dat$g & dat$g > 0)\ndat$y <- dat$alpha + dat$beta * (dat$t - 1) - 30 * (dat$t - 1) +\n         (-150) * dat$D + rnorm(N * Tmax, 0, 40)                       # 次均药品费用\ndat$adm <- 0.20 - 0.01 * (dat$t - 1) + (dat$alpha - 900) / 3000 +\n           (-0.02) * dat$D + rnorm(N * Tmax, 0, 0.04)                  # 住院率\n\ntable(dat$g)                        # 队列规模\naggregate(y ~ treated + t, data = dat, FUN = mean)     # 各期均值（干预前应接近）","source":"skills/did-skill.md","status":"模板；未逐项执行","line":100}},{"id":"code-library:did-skill-5","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 1 步：研究设计与识别假设","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-5","title":"第 1 步：研究设计与识别假设","language":"stata","code":"* 干预前基线平衡表（t=1 期）\ntabstat y adm x1 x2 if t == 1, by(treated) stat(n mean sd)\n* 各期结局均值图（第一道识别直觉：干预前两组路径应平行）\negen ybar = mean(y), by(treated t)\ntwoway (line ybar t if treated == 1) (line ybar t if treated == 0), ///\n    legend(order(1 \"处理组\" 2 \"对照组\")) xline(2.5, lp(dash)) ///\n    title(\"各期次均药品费用：处理组 vs 对照组（干预前应平行）\")\ndrop ybar","source":"skills/did-skill.md","status":"模板；未逐项执行","line":142}},{"id":"code-library:did-skill-6","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 1 步：研究设计与识别假设","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-6","title":"第 1 步：研究设计与识别假设","language":"r","code":"# 干预前基线平衡表\naggregate(cbind(y, adm, x1, x2) ~ treated, data = subset(dat, t == 1), FUN = mean)\n# 各期均值图\nmeans <- aggregate(y ~ treated + t, data = dat, FUN = mean)\nwith(means, {\n    plot(t, y, type = \"n\", xlab = \"t\", ylab = \"次均药品费用\")\n    lines(t[treated == 1], y[treated == 1], col = \"red\",  lwd = 2)\n    lines(t[treated == 0], y[treated == 0], col = \"blue\", lwd = 2)\n    abline(v = 2.5, lty = 2)\n    legend(\"topright\", c(\"处理组\", \"对照组\"), col = c(\"red\", \"blue\"), lwd = 2)\n})","source":"skills/did-skill.md","status":"模板；未逐项执行","line":153}},{"id":"code-library:did-skill-7","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 2 步：经典 2×2 DID","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-7","title":"第 2 步：经典 2×2 DID","language":"stata","code":"* 两期子样本：t=1 干预前、t=2 干预后；处理组 = t=2 期入组队列\npreserve\nkeep if inlist(t, 1, 2)\ngen post = (t == 2)\ngen treated2 = (g == 2)                // 10 个处理组；g=3/4 在 t<=2 尚未处理，作为对照\n* 经典 2×2（OLS + 交互项）\nreg y i.treated2##i.post, vce(cluster id)\nest store m_2x2\n* 系数解释：1.treated2 = 基线差异；1.post = 共同时间变化；\n*           1.treated2#1.post = ATT ≈ -150（集采使次均药品费用下降约 150 元）\n\n* 等价：双向固定效应（系数相同）\nreg y i.treated2#i.post i.id i.t, vce(cluster id)\n\n* 官方命令（Stata 17+）：didregress（自动 FE 与 ATET 报告）\ndidregress (y) (treated2), group(id) time(t) vce(cluster id)\n* 注：didregress 假设处理组单一入组时点；交错处理请用第 4 步的 csdid\nrestore","source":"skills/did-skill.md","status":"模板；未逐项执行","line":179}},{"id":"code-library:did-skill-8","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 2 步：经典 2×2 DID","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-8","title":"第 2 步：经典 2×2 DID","language":"r","code":"library(fixest)\nd2 <- subset(dat, t %in% 1:2)\nd2$post <- as.integer(d2$t == 2)\nd2$treated2 <- as.integer(d2$g == 2)\n\n# 双向固定效应 DID：组/期主效应被 FE 吸收，交互项 = ATT\nm_2x2 <- feols(y ~ treated2 * post | id + t, data = d2, cluster = ~id)\nsummary(m_2x2)                       # treated2:post ≈ -150\n\n# 等价：不含 FE 的经典 2×2（两期时系数相同，方便逐项解释）\nm_ols <- feols(y ~ treated2 * post, data = d2, cluster = ~id)\nsummary(m_ols)\n# 系数：treated2 = 基线差异；post = 共同时间变化；treated2:post = ATT","source":"skills/did-skill.md","status":"模板；未逐项执行","line":200}},{"id":"code-library:did-skill-9","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 3 步：平行趋势检验","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-9","title":"第 3 步：平行趋势检验","language":"stata","code":"* 事件研究（手工交互虚拟变量，基准 = 干预前一期 rel = -1）\npreserve\nkeep if inlist(g, 0, 3)\ngen treated3 = (g == 3)\ngen rel = t - 3                         // rel = -2,-1,0,1\ngen ev_m2 = (rel == -2 & treated3 == 1)\ngen ev_m1 = (rel == -1 & treated3 == 1) // 基准（回归中省略）\ngen ev_0  = (rel ==  0 & treated3 == 1)\ngen ev_1  = (rel ==  1 & treated3 == 1)\nreghdfe y ev_m2 ev_0 ev_1, absorb(id t) vce(cluster id)\ntest ev_m2 = 0                          // 正式检验：干预前系数 = 0\ncoefplot, keep(ev_*) vertical yline(0, lcolor(red)) xline(1.5, lcolor(gs8) lp(dash))\n* 图：ev_m2（干预前）应 ≈ 0；ev_0、ev_1（干预后）应 ≈ -150\n* 注：本例受 4 期数据限制只有 1 个前趋势系数；真实研究应有 3 期以上干预前数据，\n*     并联合检验全部前趋势系数（test ev_m2 ev_m3 ... = 0）\n\n* 官方替代（Stata 17+）：didregress + 预趋势检验\ndidregress (y) (treated3), group(id) time(t) vce(cluster id)\nestat trendplots                        // 各期处理-对照差异图（含置信区间）\nestat ptrends                           // 平行趋势联合检验（H0：干预前各期系数均为 0）\nrestore","source":"skills/did-skill.md","status":"模板；未逐项执行","line":230}},{"id":"code-library:did-skill-10","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 3 步：平行趋势检验","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-10","title":"第 3 步：平行趋势检验","language":"r","code":"# 事件研究（fixest 的 i() 交互；基准 rel = -1）\nlibrary(fixest)\nes <- subset(dat, g %in% c(0, 3))\nes$treated3 <- as.integer(es$g == 3)\nes$rel <- es$t - 3\nm_es <- feols(y ~ i(rel, treated3, ref = -1) | id + t, data = es, cluster = ~id)\nsummary(m_es)\niplot(m_es)                             # 事件研究图：干预前系数应 ≈ 0\n# 正式检验：干预前系数（本例为 rel = -2）\ncoef(m_es)                              # 系数名形如 rel::-2:treated3（以实际输出为准）\nwald(m_es, \"rel::-2:treated3\")          # 名称以 coef(m_es) 输出为准\n# 亦可用 coefplot(m_es)（fixest 自带）；选项以 help(coefplot.fixest) 为准\n# 交错处理时可用 did::pre.test()（对 CS 估计做干预前联合检验，以 help(pre.test) 为准）","source":"skills/did-skill.md","status":"模板；未逐项执行","line":254}},{"id":"code-library:did-skill-11","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 4 步：多期 / 交错处理 DID（Callaway & Sant'Anna, 2021）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-11","title":"第 4 步：多期 / 交错处理 DID（Callaway & Sant'Anna, 2021）","language":"stata","code":"* Stata 18+：官方 csdid；Stata 17 及更早：ssc install csdid, replace + ssc install drdid, replace\n* 全样本：30 个处理组在 t=2/3/4 交错入组（g=2/3/4），30 个永不受理（g=0）\ncsdid y, ivar(id) time(t) gvar(g)\n* 聚合（官方 18：estat simple/event/group/calendar；SSC 版：csdid_estat simple ...）\nestat simple                  // 总体 ATT ≈ -150\nestat event                   // 事件时间聚合（供第 5 步作图）\nestat group                   // 按队列聚合\n* 选项（method、对照组选择、权重自举等）以 help csdid 为准\n\n* 官方替代（Stata 18+）：xthdidregress，处理时点由 D 自动识别\nxthdidregress ra (y) (D), group(id) time(t)\n* 聚合与作图：estat aggregation / estat atetplot；语法以 help xthdidregress postestimation 为准","source":"skills/did-skill.md","status":"模板；未逐项执行","line":285}},{"id":"code-library:did-skill-12","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 4 步：多期 / 交错处理 DID（Callaway & Sant'Anna, 2021）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-12","title":"第 4 步：多期 / 交错处理 DID（Callaway & Sant'Anna, 2021）","language":"r","code":"# Callaway-Sant'Anna（官方 R 实现）\nlibrary(did)\nout <- att_gt(yname = \"y\", tname = \"t\", idname = \"id\", gname = \"g\",\n              data = dat, control_group = \"notyettreated\")\nsummary(out)                            # ATT(g,t) 表：队列 g × 时期 t\nagg_simple <- aggte(out, type = \"simple\")    # 总体 ATT ≈ -150\nsummary(agg_simple)\nagg_group  <- aggte(out, type = \"group\")     # 按队列\nagg_cal    <- aggte(out, type = \"calendar\")  # 按日历期\n# 对照组设定、估计方法（est_method = \"dr\"/\"reg\"/\"ipw\"）等选项以 help(att_gt) 为准\n\n# 替代：Sun & Abraham（fixest）\nfeols(y ~ sunab(g, t) | id + t, data = dat, cluster = ~id)","source":"skills/did-skill.md","status":"模板；未逐项执行","line":300}},{"id":"code-library:did-skill-13","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 5 步：事件研究图","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-13","title":"第 5 步：事件研究图","language":"stata","code":"* 接第 4 步 csdid\nestat event                   // 官方 18；SSC 版为 csdid_estat event\ncsdid_plot                    // 事件研究图（官方 18 与 SSC 版均有）\n* 若版本支持，亦可用 marginsplot：estat event 后 marginsplot\n* （以 help csdid postestimation / help csdid_plot 为准）","source":"skills/did-skill.md","status":"模板；未逐项执行","line":326}},{"id":"code-library:did-skill-14","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 5 步：事件研究图","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-14","title":"第 5 步：事件研究图","language":"r","code":"# 接第 4 步\nagg_dyn <- aggte(out, type = \"dynamic\", min_e = -3, max_e = 2)\nsummary(agg_dyn)                    # 事件研究系数表（含各期置信区间）\nggdid(agg_dyn)                      # 事件研究图（需 ggplot2）\nggdid(agg_dyn, ylim = c(-300, 60), xlab = \"相对处理期\",\n      title = \"动态处理效应：集采对次均药品费用\")\n# ggdid 的图形选项以 help(ggdid) 为准","source":"skills/did-skill.md","status":"模板；未逐项执行","line":334}},{"id":"code-library:did-skill-15","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 6 步：稳健性检验","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-15","title":"第 6 步：稳健性检验","language":"stata","code":"* ---------- 6.1 伪处理组（随机重抽处理组身份，500 次） ----------\npreserve\nkeep if inlist(t, 1, 2)\ngen post = (t == 2)\ntempname pf\ntempfile pfres\npostfile `pf' b_did using `pfres', replace\nset seed 2026\nforvalues r = 1/500 {\n    * 在地区层面抽一个伪处理身份（同一地区两期身份一致）\n    bysort id: gen rnd_id = runiform() if _n == 1\n    bysort id (rnd_id): replace rnd_id = rnd_id[1]\n    egen prank = rank(rnd_id), unique\n    gen treatedF = (prank <= 10)          // 随机 10 个伪处理地区\n    quietly reg y i.treatedF##i.post, vce(cluster id)\n    post `pf' (_b[1.treatedF#1.post])\n    drop rnd_id prank treatedF\n}\npostclose `pf'\nuse `pfres', clear\nsummarize b_did, detail                  // 伪系数分布：中心应 ≈ 0\ncount if b_did <= -150                   // 伪系数 ≤ 真实系数（-150）的次数\ndi (r(N) + 1) / 501                      // 经验 p 值（单侧）\nhistogram b_did, normal xline(-150, lcolor(red))\n* 备选：ritest（SSC）做随机化推断，支持聚类层级（选项以 help ritest 为准）\nrestore\n\n* ---------- 6.2 伪时点（预期效应检验） ----------\n* 用 g=3 队列的干预前两期（t=1,2），人为假设 t=2 已干预\npreserve\nkeep if inlist(g, 0, 3) & inlist(t, 1, 2)\ngen post = (t == 2)\ngen treated3 = (g == 3)\nreg y i.treated3##i.post, vce(cluster id)     // 预期系数 ≈ 0\nrestore\n\n* ---------- 6.3 剔除部分样本 ----------\n* (a) 剔除 y 上下 1% 极端值后重估 2×2\npreserve\nkeep if inlist(t, 1, 2)\ngen post = (t == 2)\ngen treated2 = (g == 2)\nquietly summarize y, detail\ndrop if y < r(p1) | y > r(p99)\nreg y i.treated2##i.post, vce(cluster id)\nrestore\n* (b) 对照只用永不受理组（排除尚未处理队列），多期 FE 估计\npreserve\nkeep if inlist(g, 0, 2)\ngen post = (t >= 2)\ngen treated2 = (g == 2)\nreghdfe y i.treated2#i.post, absorb(id t) vce(cluster id)\nrestore\n\n* ---------- 6.4 PSM-DID ----------\npreserve\nkeep if inlist(t, 1, 2)\ngen post = (t == 2)\ngen treated2 = (g == 2)\n* 方案 A（官方 teffects）：横截面 ATT 演示（把两期分别视为观测）\nteffects psmatch (y) (treated2 x1 x2), atet\n* 方案 B（psmatch2 + DID，SSC；实务常用）：按干预前协变量匹配后做 DID\npsmatch2 treated2 x1 x2, out(y) logit common\npstest x1 x2, both                        // 匹配后平衡性：标准化差异应 < 0.1\nreg y i.treated2##i.post [pw = _weight], vce(cluster id)\nest store m_psm\n* 注：psmatch2 生成的 _weight 为匹配权重；以 help psmatch2 为准\nrestore\n\n* ---------- 6.5 更换结局与聚类层级 ----------\npreserve\nkeep if inlist(t, 1, 2)\ngen post = (t == 2)\ngen treated2 = (g == 2)\nreg adm i.treated2##i.post, vce(cluster id)      // 结局换为住院率：预期 ≈ -0.02\nest store m_adm\n* 聚类层级敏感性：地区 → 更保守的层级（示例：每 3 个地区并为一组）\negen group3 = group(ceil(id/3))\nreg y i.treated2##i.post, vce(cluster group3)    // 聚类数变少，标准误通常变大\nrestore","source":"skills/did-skill.md","status":"模板；未逐项执行","line":358}},{"id":"code-library:did-skill-16","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 6 步：稳健性检验","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-16","title":"第 6 步：稳健性检验","language":"r","code":"# 接第 2 步的 d2（t ∈ {1,2}；post、treated2 已定义）\nlibrary(fixest)\n\n# ---------- 6.1 伪处理组（500 次随机重抽） ----------\nset.seed(2026)\nids <- unique(d2$id)\nb_null <- replicate(500, {\n    fake <- sample(ids, 10)                      # 随机 10 个伪处理地区\n    d2$treatedF <- as.integer(d2$id %in% fake)\n    m <- feols(y ~ treatedF * post | id + t, data = d2, cluster = ~id)\n    coef(m)[\"treatedF:post\"]                     # 系数名以 coef(m) 输出为准\n})\npval <- (sum(b_null <= -150) + 1) / 501          # 经验 p 值（单侧）\nhist(b_null, breaks = 30, main = \"伪处理组 DID 系数分布\")\nabline(v = -150, col = \"red\", lwd = 2)\n\n# ---------- 6.2 伪时点 ----------\nd_pre <- subset(dat, g %in% c(0, 3) & t %in% 1:2)\nd_pre$post <- as.integer(d_pre$t == 2)\nd_pre$treated3 <- as.integer(d_pre$g == 3)\nfeols(y ~ treated3 * post | id + t, data = d_pre, cluster = ~id)   # 预期 ≈ 0\n\n# ---------- 6.3 剔除极端值 ----------\nq <- quantile(d2$y, c(0.01, 0.99))\nd2t <- subset(d2, y > q[1] & y < q[2])\nfeols(y ~ treated2 * post | id + t, data = d2t, cluster = ~id)\n\n# ---------- 6.4 PSM-DID ----------\nlibrary(MatchIt)\nd2pre <- subset(d2, t == 1)\nm.out <- matchit(treated2 ~ x1 + x2, data = d2pre, method = \"nearest\", ratio = 1)\nsummary(m.out)                       # 匹配后标准化均值差应 < 0.1\n# library(cobalt); love.plot(m.out)  # 平衡性图\nmd  <- match.data(m.out)             # 匹配样本（含 weights）\nd2m <- subset(d2, id %in% unique(md$id))\nfeols(y ~ treated2 * post | id + t, data = d2m, cluster = ~id)\n\n# ---------- 6.5 更换结局与聚类层级 ----------\nfeols(adm ~ treated2 * post | id + t, data = d2, cluster = ~id)     # 住院率 ≈ -0.02\nd2$group3 <- ceiling(d2$id / 3)\nfeols(y ~ treated2 * post | id + t, data = d2, cluster = ~group3)   # 更保守聚类","source":"skills/did-skill.md","status":"模板；未逐项执行","line":441}},{"id":"code-library:did-skill-17","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 7 步：报告清单","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-17","title":"第 7 步：报告清单","language":"stata","code":"* 结果表（esttab，SSC；官方 etable 亦可）\nesttab m_2x2 m_psm m_adm, b(%9.2f) se(%9.2f) star(* 0.10 ** 0.05 *** 0.01) ///\n    mtitles(\"2x2 DID\" \"PSM-DID\" \"结局=住院率\") ///\n    stats(N N_clust, labels(\"观测数\" \"聚类数\"))\n* CS 聚合结果用 estat simple / estat event 直接导出；\n* esttab 与 etable 选项以 help esttab / help etable 为准","source":"skills/did-skill.md","status":"模板；未逐项执行","line":501}},{"id":"code-library:did-skill-18","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 7 步：报告清单","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-18","title":"第 7 步：报告清单","language":"r","code":"# 结果表（fixest::etable 或 modelsummary）\netable(list(m_2x2, m_psm, m_adm), cluster = ~id, fitstat = ~n)\n# library(modelsummary)\n# modelsummary(list(\"2x2\" = m_2x2, \"PSM-DID\" = m_psm, \"住院率\" = m_adm), stars = TRUE)","source":"skills/did-skill.md","status":"模板；未逐项执行","line":510}},{"id":"code-library:did-skill-19","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"输出格式（政策评估报告模板）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"did-skill-19","title":"输出格式（政策评估报告模板）","language":"text","code":"【研究设计与数据】\n- 政策背景与干预时点；处理组/对照组定义、规模与队列分布（表 0）\n- 数据来源、时期、结局定义（次均药品费用、住院率）；干预前基线平衡表\n\n【识别假设】\n- 平行趋势论证：图 1 事件研究图 + 干预前系数联合检验 p 值 + 制度背景\n- 共同冲击、无预期效应、无溢出的讨论；不满足时的处理策略\n\n【主要结果】\n- 表 1：2×2 DID（或 didregress ATET）：系数、聚类稳健 SE、95% CI、观测数与聚类数\n- 表 2（交错处理时）：Callaway-Sant'Anna 聚合 ATT（simple/dynamic/group，注明聚合类型与对照组）\n- 图 2：动态处理效应（事件研究）图，注明基准期与置信区间\n\n【稳健性】\n- 表 3：伪处理组系数分布与经验 p 值（附直方图）；伪时点系数\n- 表 4：剔除极端值/部分样本、PSM-DID、更换结局、更换聚类层级结果汇总\n\n【结论与局限】\n- 效应大小与政策含义（如集采使次均药品费用下降约 X 元、住院率变化）\n- 局限：平行趋势不可完全检验、溢出/一般均衡、同期政策干扰、外部效度","source":"skills/did-skill.md","status":"模板；未逐项执行","line":537}},{"id":"code-library:health-economics-tutorial-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"软件准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-1","title":"软件准备","language":"stata","code":"* Stata（本教程主要用官方命令；结果表导出可选用 estout，putexcel 为官方命令）\nssc install estout, replace","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":25}},{"id":"code-library:health-economics-tutorial-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"软件准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-2","title":"软件准备","language":"r","code":"# R\ninstall.packages(\"boot\")      # bootstrap 置信区间；其余均为基础 R","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":30}},{"id":"code-library:health-economics-tutorial-skill-3","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 1 步：提出研究问题与评价类型选择","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-3","title":"第 1 步：提出研究问题与评价类型选择","language":"text","code":"P（人群）：某慢性病患者（示例：2 型糖尿病、病程 ≥ 5 年、无严重并发症）\nI（干预）：新综合管理方案\nC（对照）：常规治疗\nO（结局）：QALY（及并发症发生率等自然单位结局）\nT（时间范围）：10 年\n研究问题：在 P 中，与 C 相比，I 在 T 年内对 O 产生的增量成本与增量效果如何？\n           ICER 是否低于支付意愿阈值 λ？","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":55}},{"id":"code-library:health-economics-tutorial-skill-4","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 1 步：提出研究问题与评价类型选择","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-4","title":"第 1 步：提出研究问题与评价类型选择","language":"stata","code":"* Stata：评价类型选择示意（把结局类型写入 local 后运行）\nlocal outcome \"qaly\"    // 可选：equivalent / natural / qaly / monetary\nif \"`outcome'\" == \"equivalent\" {\n    display \"结局已证明等效 → 用 CMA（最小成本分析）\"\n}\nelse if \"`outcome'\" == \"natural\" {\n    display \"结局为自然单位（血压、生命年等）→ 用 CEA（成本-效果分析）\"\n}\nelse if \"`outcome'\" == \"qaly\" {\n    display \"结局为偏好加权指标（QALY/DALY）→ 用 CUA（成本-效用分析）\"\n}\nelse {\n    display \"结局已货币化或需跨领域比较 → 用 CBA（成本-效益分析）\"\n}","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":67}},{"id":"code-library:health-economics-tutorial-skill-5","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 1 步：提出研究问题与评价类型选择","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-5","title":"第 1 步：提出研究问题与评价类型选择","language":"r","code":"# R：评价类型选择决策函数\npick_type <- function(outcome = c(\"equivalent\", \"natural\", \"qaly\", \"monetary\")) {\n  outcome <- match.arg(outcome)\n  switch(outcome,\n    equivalent = \"CMA：最小成本分析（结局等效时只比较成本）\",\n    natural    = \"CEA：成本-效果分析（自然单位结局）\",\n    qaly       = \"CUA：成本-效用分析（QALY/DALY 等偏好加权结局）\",\n    monetary   = \"CBA：成本-效益分析（成本与效果均货币化）\")\n}\npick_type(\"qaly\")     # 本算例用 QALY → CUA","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":84}},{"id":"code-library:health-economics-tutorial-skill-6","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 2 步：研究视角与时间范围","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-6","title":"第 2 步：研究视角与时间范围","language":"stata","code":"* Stata：成本条目按研究视角纳入的示意见表（1 = 纳入，0 = 不纳入）\n* 注：中文值需 Stata 17+（UTF-8）\nclear\ninput byte payer byte system byte society str50 item\n1 1 1 \"药品费用（医保报销部分）\"\n1 1 1 \"检查与住院（医保报销部分）\"\n0 1 1 \"政府财政补助与公共卫生投入\"\n0 1 1 \"患者自付医疗费用\"\n0 0 1 \"交通、陪护与食宿（患者家庭）\"\n0 0 1 \"因病损失的生产力\"\nend\nlabel var payer   \"医保支付方视角\"\nlabel var system  \"卫生体系视角\"\nlabel var society \"社会视角\"\nlist, sep(0) noobs","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":112}},{"id":"code-library:health-economics-tutorial-skill-7","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 2 步：研究视角与时间范围","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-7","title":"第 2 步：研究视角与时间范围","language":"r","code":"# R：不同研究视角纳入的成本条目（示例矩阵）\nitems <- data.frame(\n  条目 = c(\"药品费用（医保报销部分）\", \"检查与住院（医保报销部分）\", \"政府财政补助与公共卫生投入\",\n           \"患者自付医疗费用\", \"交通、陪护与食宿（患者家庭）\", \"因病损失的生产力\"),\n  医保支付方 = c(\"√\", \"√\", \"—\", \"—\", \"—\", \"—\"),\n  卫生体系   = c(\"√\", \"√\", \"√\", \"√\", \"—\", \"—\"),\n  社会视角   = c(\"√\", \"√\", \"√\", \"√\", \"√\", \"√\"))\nitems","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":130}},{"id":"code-library:health-economics-tutorial-skill-8","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 3 步：成本测算","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-8","title":"第 3 步：成本测算","language":"stata","code":"* Stata：直接医疗成本示例数据（元/人/年；与贯穿算例一致：对照均值 6040、干预均值 9180）\nclear\ninput byte group int cost\n0 6000\n0 5500\n0 6800\n0 5900\n0 6200\n0 6100\n0 5700\n0 6400\n0 5800\n0 6000\n1 9000\n1 8800\n1 9500\n1 9200\n1 8900\n1 9600\n1 9100\n1 9300\n1 9000\n1 9400\nend\nlabel define grp 0 \"对照\" 1 \"干预\"\nlabel values group grp\n\n* 均数与 95% CI（正态近似）\nmean cost, over(group)\nci means cost if group == 0\nci means cost if group == 1\n\n* bootstrap：两组成本之差的 95% 百分位区间与偏差校正（BC）区间\ncapture program drop diff_cost\nprogram define diff_cost, rclass\n    quietly summarize cost if group == 0\n    local m0 = r(mean)\n    quietly summarize cost if group == 1\n    local m1 = r(mean)\n    return scalar diff = `m1' - `m0'\nend\nbootstrap r(diff), reps(1000) seed(2026) nodots: diff_cost\nestat bootstrap, percentile bc","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":157}},{"id":"code-library:health-economics-tutorial-skill-9","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 3 步：成本测算","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-9","title":"第 3 步：成本测算","language":"r","code":"# R：直接医疗成本示例数据（贯穿算例：对照组均值 6040、干预组均值 9180）\ncost0 <- c(6000, 5500, 6800, 5900, 6200, 6100, 5700, 6400, 5800, 6000)\ncost1 <- c(9000, 8800, 9500, 9200, 8900, 9600, 9100, 9300, 9000, 9400)\nc(mean0 = mean(cost0), mean1 = mean(cost1))\nt.test(cost0)$conf.int          # 对照组 95% CI（正态近似）\nt.test(cost1)$conf.int          # 干预组 95% CI\n\n# bootstrap：两组成本之差的 95% 百分位 CI\nlibrary(boot)\nd <- data.frame(cost = c(cost0, cost1), group = rep(c(0, 1), each = 10))\ndiff_mean <- function(data, i) {\n  g <- data[i, ]\n  mean(g$cost[g$group == 1]) - mean(g$cost[g$group == 0])\n}\nset.seed(2026)\nb <- boot(d, diff_mean, R = 1000)\nboot.ci(b, type = \"perc\")       # 差值点估计 ≈ 9180 - 6040 = 3140 元","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":203}},{"id":"code-library:health-economics-tutorial-skill-10","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 4 步：效果与效用指标","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-10","title":"第 4 步：效果与效用指标","language":"stata","code":"* Stata：QALY 与增量 QALY（效用 × 生存年数，未贴现）\nscalar u0 = 0.70       // 对照年效用\nscalar u1 = 0.78       // 干预年效用\nscalar years = 10\ndisplay \"对照 QALY = \" u0 * years          // 7.0\ndisplay \"干预 QALY = \" u1 * years          // 7.8\ndisplay \"ΔQALY     = \" (u1 - u0) * years   // 0.8","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":240}},{"id":"code-library:health-economics-tutorial-skill-11","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 4 步：效果与效用指标","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-11","title":"第 4 步：效果与效用指标","language":"r","code":"# R：QALY 与增量 QALY（未贴现）\nu0 <- 0.70; u1 <- 0.78; years <- 10\nQ0 <- u0 * years        # 7.0\nQ1 <- u1 * years        # 7.8\ndQ <- Q1 - Q0           # 0.8 QALY\nlist(QALY_control = Q0, QALY_treat = Q1, dQALY = dQ)\n\n# R：DALY 简算示意：DALY = YLL + YLD\nYLL <- 10               # 早亡损失的生命年（示例）\nYLD <- 2.5              # 伤残状态生存年 × 伤残权重（示例）\nYLL + YLD               # 12.5 DALY\n\n# 提示：EQ-5D 状态（如 21121）须用已发表的中国积分体系系数换算效用值，\n# 勿自造系数；换算公式形如 U = 1 −（常数项 + 各维度离差项 + 交互/极端项）","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":250}},{"id":"code-library:health-economics-tutorial-skill-12","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 5 步：贴现","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-12","title":"第 5 步：贴现","language":"stata","code":"* Stata：贴现因子与 10 年年金贴现因子（r = 5%）\nscalar r = 0.05\nscalar df = 0\nforvalues t = 1/10 {\n    scalar df = df + 1 / (1 + r)^`t'\n}\ndisplay \"10 年、5% 的年金贴现因子 = \" %9.4f df      // 7.7217\n\n* 贴现后的增量成本与增量效果\nscalar dC = (12000 - 8000) * df\nscalar dE = (0.78 - 0.70) * df\ndisplay \"贴现后 ΔC = \" %9.2f dC                      // 30886.93 元\ndisplay \"贴现后 ΔE = \" %9.4f dE                      // 0.6177 QALY\n\n* 贴现率敏感性（0–8%）\nforeach rr in 0 0.03 0.05 0.08 {\n    scalar df = 0\n    forvalues t = 1/10 {\n        scalar df = df + 1 / (1 + `rr')^`t'\n    }\n    display \"r = \" `rr' \"：10 年年金贴现因子 = \" %9.4f df\n}","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":280}},{"id":"code-library:health-economics-tutorial-skill-13","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 5 步：贴现","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-13","title":"第 5 步：贴现","language":"r","code":"# R：贴现与贴现率敏感性\nr <- 0.05; t <- 1:10\ndisc <- 1 / (1 + r)^t\nsum(disc)                                   # 7.7217（10 年、5% 年金贴现因子）\ndC <- (12000 - 8000) * sum(disc)            # 30886.9（贴现后增量成本，元）\ndE <- (0.78 - 0.70) * sum(disc)             # 0.6177（贴现后增量 QALY）\n\n# 贴现率敏感性（0–8%）\nsapply(c(0, 0.03, 0.05, 0.08), function(rr) sum(1 / (1 + rr)^(1:10)))\n# 本算例年增量恒定 → 贴现率不改变 ICER（= 4000 / 0.08 = 50000）；\n# 成本与效果时间分布不同时，ICER 会随贴现率变化（见第 7 步）","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":305}},{"id":"code-library:health-economics-tutorial-skill-14","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 6 步：增量分析与 ICER","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-14","title":"第 6 步：增量分析与 ICER","language":"stata","code":"* Stata：ICER、净收益与四象限判断\nscalar dC = 30886.9\nscalar dE = 0.6177\nscalar lambda = 100000\ndisplay \"ICER = \" %9.1f dC / dE                    // ≈ 50003 元/QALY\ndisplay \"NB(λ=100000) = \" %9.1f lambda * dE - dC   // ≈ 30887 元\n\nif dC > 0 & dE > 0 {\n    display \"东北象限：更贵更有效 → 比较 ICER 与 λ\"\n    display cond(dC/dE < lambda, \"ICER < λ → 具有成本-效果\", \"ICER ≥ λ → 不具有成本-效果\")\n}\nelse if dC < 0 & dE > 0 {\n    display \"东南象限：更便宜更有效 → 优势策略，直接推荐\"\n}\nelse if dC > 0 & dE < 0 {\n    display \"西北象限：更贵更差 → 劣势策略，拒绝\"\n}\nelse {\n    display \"西南象限：更便宜更差 → 权衡节省与效果损失\"\n}","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":336}},{"id":"code-library:health-economics-tutorial-skill-15","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 6 步：增量分析与 ICER","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-15","title":"第 6 步：增量分析与 ICER","language":"r","code":"# R：ICER、净收益与四象限判断\ndC <- 30886.9; dE <- 0.6177\nlambda <- 100000\nICER <- dC / dE                                    # ≈ 50003 元/QALY\nNB   <- lambda * dE - dC                            # ≈ 30887 元（净货币收益）\nquadrant <- if (dC > 0 && dE > 0) \"NE：更贵更有效（与 λ 比较）\" else\n            if (dC < 0 && dE > 0) \"SE：更便宜更有效（优势）\" else\n            if (dC > 0 && dE < 0) \"NW：更贵更差（劣势）\" else\n            \"SW：更便宜更差（权衡）\"\nverdict <- ifelse(ICER < lambda, \"ICER < λ，具有成本-效果\", \"ICER ≥ λ，不具有成本-效果\")\nlist(ICER = ICER, NB = NB, quadrant = quadrant, verdict = verdict)","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":359}},{"id":"code-library:health-economics-tutorial-skill-16","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 7 步：不确定性分析","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-16","title":"第 7 步：不确定性分析","language":"stata","code":"* Stata：单因素敏感性分析（基线 ICER ≈ 50003 元/QALY）\nscalar dC = 30886.9\nscalar dE = 0.6177\ndisplay \"基线 ICER            = \" %9.1f dC / dE\ndisplay \"ΔE +20% 的 ICER      = \" %9.1f dC / (dE * 1.2)\ndisplay \"ΔE −20% 的 ICER      = \" %9.1f dC / (dE * 0.8)\ndisplay \"ΔC +20% 的 ICER      = \" %9.1f (dC * 1.2) / dE\ndisplay \"ΔC −20% 的 ICER      = \" %9.1f (dC * 0.8) / dE\n\n* 干预组年效用取 0.73 / 0.83（其余不变；贴现后 ΔE = Δu × 7.7217）\ndisplay \"干预效用 0.73 → ICER = \" %9.1f dC / (0.03 * 7.7217)\ndisplay \"干预效用 0.83 → ICER = \" %9.1f dC / (0.13 * 7.7217)\n* 解读：ICER 范围约 30769–133333 元/QALY，跨过 λ = 100000 →\n* 结论对效用参数敏感，报告中须突出说明","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":386}},{"id":"code-library:health-economics-tutorial-skill-17","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 7 步：不确定性分析","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-17","title":"第 7 步：不确定性分析","language":"r","code":"# R：概率敏感性分析（PSA）示意——直接从 ΔC、ΔE 的分布抽样；\n# 正式研究应从参数层面抽样（成本 gamma、概率 beta、RR lognormal），\n# 完整实现见《Markov 模型助手》skill。\nset.seed(2026)\nn <- 1000\ndC <- rnorm(n, mean = 30886.9, sd = 3200)     # 增量成本（元）\ndE <- rnorm(n, mean = 0.6177, sd = 0.05)      # 增量 QALY\n\n# 成本-效果平面散点图（λ = 100000 元/QALY 的斜率线）\nplot(dE, dC, xlab = \"ΔE（QALY）\", ylab = \"ΔC（元）\", pch = 20,\n     cex = 0.5, col = rgb(0, 0, 0, 0.3), main = \"PSA 成本-效果平面散点图\")\nabline(h = 0, v = 0, lty = 2)\nabline(a = 0, b = 100000, col = \"red\")        # 斜率 = λ\n\n# 各象限模拟点占比\nquad <- ifelse(dE > 0, ifelse(dC > 0, \"NE\", \"SE\"),\n               ifelse(dC > 0, \"NW\", \"SW\"))\ntable(quad) / n\n\n# 成本-效果可接受曲线（CEAC）：P(NMB(λ) > 0)\nlam  <- seq(0, 300000, by = 5000)\nceac <- sapply(lam, function(x) mean(x * dE - dC > 0))\nplot(lam, ceac, type = \"l\", xlab = \"支付意愿阈值 λ（元/QALY）\",\n     ylab = \"具有成本-效果的概率\", ylim = c(0, 1))\nabline(h = 0.5, lty = 3)","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":403}},{"id":"code-library:health-economics-tutorial-skill-18","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 8 步：报告规范（CHEERS 2022）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-18","title":"第 8 步：报告规范（CHEERS 2022）","language":"stata","code":"* Stata：把主要结果写入 Excel（putexcel，Stata 14+）\nputexcel set results.xlsx, replace\nputexcel A1 = \"指标\" B1 = \"数值\"\nputexcel A2 = \"贴现后 ΔC（元）\"        B2 = 30886.9\nputexcel A3 = \"贴现后 ΔE（QALY）\"      B3 = 0.6177\nputexcel A4 = \"ICER（元/QALY）\"        B4 = 50003\nputexcel A5 = \"阈值 λ（元/QALY）\"      B5 = 100000\nputexcel A6 = \"净收益 NB（元）\"        B6 = 30887","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":446}},{"id":"code-library:health-economics-tutorial-skill-19","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 8 步：报告规范（CHEERS 2022）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-19","title":"第 8 步：报告规范（CHEERS 2022）","language":"r","code":"# R：导出主要结果表\nres <- data.frame(\n  指标 = c(\"贴现后 ΔC（元）\", \"贴现后 ΔE（QALY）\", \"ICER（元/QALY）\",\n           \"阈值 λ（元/QALY）\", \"净收益 NB（元）\"),\n  数值 = c(30886.9, 0.6177, 50003, 100000, 30887))\nres\nwrite.csv(res, \"results.csv\", row.names = FALSE)","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":457}},{"id":"code-library:health-economics-tutorial-skill-20","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 9 步：研究工具箱指引（本仓库配套 Skill）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-20","title":"第 9 步：研究工具箱指引（本仓库配套 Skill）","language":"r","code":"# R：本仓库配套 Skill 一览（可复制到 README / 网页）\ntoolbox <- data.frame(\n  Skill = c(\"DCE 设计助手\", \"Markov 模型助手\", \"机器学习\", \"DID 双重差分\"),\n  什么时候用 = c(\n    \"测量偏好、效用值与支付意愿（EQ-5D 的补充或替代）\",\n    \"慢性病动态建模、时间外推与完整 PSA/CEAC\",\n    \"真实世界大数据下的费用预测与风险分层\",\n    \"用面板数据评估医保与支付改革政策的因果效果\"))\ntoolbox","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":482}},{"id":"code-library:health-economics-tutorial-skill-21","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"输出格式（研究方案 / 报告模板）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"health-economics-tutorial-skill-21","title":"输出格式（研究方案 / 报告模板）","language":"text","code":"【研究方案模板】\n1. 研究问题（PICO-T）与评价类型（CEA/CUA/CMA/CBA 及选择理由）\n2. 研究视角（社会/卫生体系/医保支付方）与时间范围（含理由）\n3. 成本：条目清单（识别→测量→估值）、单位成本来源与年份、间接成本方法（HCA/FCM）\n4. 效果/效用：结局指标、数据来源、效用积分体系（如 EQ-5D 中国积分体系及版本）\n5. 贴现：贴现率（指南 2020：5%，敏感性 0–8%）与基年\n6. 分析计划：ICER、阈值 λ 与依据、敏感性分析（单因素/场景/PSA 与参数分布）\n7. 自查：按 CHEERS 2022 清单逐条对照\n\n【报告模板】\n标题（注明评价类型，如「……的成本-效用分析」）\n摘要（视角、时间范围、贴现率、ΔC、ΔE、ICER、结论）\n1 背景与目的 2 方法（视角、人群、时间范围、成本、效果、贴现、分析计划）\n3 结果（成本表、效果表、ICER、tornado 图、PSA 散点图与 CEAC）\n4 讨论（与文献比较、阈值与不确定性、异质性与公平性、局限）\n5 结论（附：CHEERS 2022 对照自查表）","source":"skills/health-economics-tutorial-skill.md","status":"模板；未逐项执行","line":514}},{"id":"code-library:its-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"软件准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"its-skill-1","title":"软件准备","language":"stata","code":"* Stata（联网安装一次）\nssc install actest, replace\nssc install itsa,   replace\nssc install estout, replace","source":"skills/its-skill.md","status":"模板；未逐项执行","line":29}},{"id":"code-library:its-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"软件准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"its-skill-2","title":"软件准备","language":"r","code":"# R\ninstall.packages(c(\"lmtest\", \"sandwich\", \"car\", \"ggplot2\", \"texreg\"))","source":"skills/its-skill.md","status":"模板；未逐项执行","line":36}},{"id":"code-library:its-skill-3","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 1 步：适用条件与研究设计（含数据要求与模拟数据）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"its-skill-3","title":"第 1 步：适用条件与研究设计（含数据要求与模拟数据）","language":"stata","code":"* =====================================================================\n* 模拟数据：48 个月 = 干预前 24（第 1–24 月）+ 干预后 24（第 25–48 月）\n* 场景：某院 DRG 付费改革（第 25 个月实施）对次均住院费用（元）的影响\n* 内置真实干预效应：\n*   β0 = 12000（干预时点基线，元）  β1 = -50（干预前趋势，元/月）\n*   β2 = -600 （即时水平变化，元）  β3 = -30（斜率变化，元/月）\n* 误差：AR(1)，rho = 0.3，扰动标准差 200 元\n* =====================================================================\nclear all\nset seed 2026\nset obs 48\ngen time        = _n\ngen time_c      = time - 25               // 中心化：干预时点 = 0\ngen level       = time >= 25              // 干预指示：第 25 个月起 = 1\ngen trend_after = time_c * level          // 干预后斜率增量（干预前恒为 0）\n\n* AR(1) 误差\ngen e = rnormal(0, 200)\nforvalues t = 2/48 {\n    replace e = 0.3 * e[_n-1] + rnormal(0, 200) if time == `t'\n}\n\n* 结局：次均住院费用（元）\ngen y = 12000 - 50*time_c - 600*level - 30*trend_after + e\n\ntsset time\ntwoway (line y time), xline(24.5, lpattern(dash)) ///\n    title(\"模拟月度序列：次均住院费用\") ytitle(\"元\") xtitle(\"月份\")","source":"skills/its-skill.md","status":"模板；未逐项执行","line":58}},{"id":"code-library:its-skill-4","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 1 步：适用条件与研究设计（含数据要求与模拟数据）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"its-skill-4","title":"第 1 步：适用条件与研究设计（含数据要求与模拟数据）","language":"r","code":"# =====================================================================\n# 模拟数据（与 Stata 版同参数；随机数实现不同，不影响示例）\n# =====================================================================\nset.seed(2026)\nn           <- 48\ntime        <- 1:n\ntime_c      <- time - 25                 # 中心化：干预时点 = 0\nlevel       <- as.numeric(time >= 25)    # 干预指示：第 25 个月起 = 1\ntrend_after <- time_c * level            # 干预后斜率增量\n\n# AR(1) 误差：rho = 0.3，扰动 sd = 200\ne <- as.numeric(arima.sim(model = list(ar = 0.3), n = n, sd = 200))\n\n# 结局：次均住院费用（元），内置真实效应\ny <- 12000 - 50*time_c - 600*level - 30*trend_after + e\n\ndat <- data.frame(time, time_c, level, trend_after, y)\nplot(y ~ time, data = dat, type = \"o\", pch = 16, cex = 0.7,\n     main = \"模拟月度序列：次均住院费用\", xlab = \"月份\", ylab = \"元\")\nabline(v = 24.5, lty = 2, col = \"gray50\")","source":"skills/its-skill.md","status":"模板；未逐项执行","line":89}},{"id":"code-library:its-skill-5","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 2 步：分段回归模型","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"its-skill-5","title":"第 2 步：分段回归模型","language":"stata","code":"* 中心化版本（推荐）：β2 直接 = 干预时点即时效应\nregress y time_c level trend_after\n* 预计：_b[level] ≈ -600、_b[trend_after] ≈ -30（样本有噪声）\n\n* 对照：未中心化版本（time 与 time*level 交互）\ngen time_level = time * level\nregress y time level time_level\n* 此时 level 系数不再等于干预时点效应（是 time=0 处的跃变），须用组合计算","source":"skills/its-skill.md","status":"模板；未逐项执行","line":132}},{"id":"code-library:its-skill-6","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 2 步：分段回归模型","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"its-skill-6","title":"第 2 步：分段回归模型","language":"r","code":"# 中心化版本（推荐）\nm0 <- lm(y ~ time_c + level + trend_after, data = dat)\nsummary(m0)\n# 预计：level ≈ -600、trend_after ≈ -30（样本有噪声）\n\n# 组合效应（第 3 步换 HAC 协方差后正式报告）\nlibrary(car)\nlinearHypothesis(m0, \"time_c + trend_after = 0\")   # 干预后总斜率（β1+β3）\ndeltaMethod(m0, \"level + 23*trend_after\")          # 期末（time_c=23）总效应","source":"skills/its-skill.md","status":"模板；未逐项执行","line":143}},{"id":"code-library:its-skill-7","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 3 步：自相关处理","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"its-skill-7","title":"第 3 步：自相关处理","language":"stata","code":"* ① 残差诊断\nregress y time_c level trend_after\npredict r, residuals\ntsline r, yline(0) title(\"残差时序图\")\nac r, lags(12) title(\"残差 ACF\")\n\nestat dwatson                    // Durbin-Watson（一阶）\nactest, lags(4)                  // Cumby-Huizinga 一般检验（SSC；选项以 help actest 为准）\nwntestq r, lags(4)               // Ljung-Box Q 检验\n\n* 平稳性检查（结局本身）\ndfuller y, trend\n\n* ② Newey-West / HAC 标准误\nnewey y time_c level trend_after, lag(1)\n* 默认滞后 = floor(4*(T/100)^(2/9))（T=48 → 3）；建议对 lag(1)–lag(4) 做敏感性\n\n* ③ 备选：Prais-Winsten AR(1)（选项以 help prais 为准）\nprais y time_c level trend_after, rhotype(tscorr)","source":"skills/its-skill.md","status":"模板；未逐项执行","line":172}},{"id":"code-library:its-skill-8","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 3 步：自相关处理","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"its-skill-8","title":"第 3 步：自相关处理","language":"r","code":"# ① 残差诊断\nm0 <- lm(y ~ time_c + level + trend_after, data = dat)\nplot(resid(m0), type = \"o\", pch = 16, cex = 0.7,\n     xlab = \"月份\", ylab = \"残差\"); abline(h = 0)\nacf(resid(m0), lag.max = 12, main = \"残差 ACF\")\n\nlibrary(lmtest)\ndwtest(m0)                    # Durbin-Watson（一阶）\nbgtest(m0, order = 4)         # Breusch-Godfrey LM（高阶）\n\n# ② Newey-West / HAC 标准误\nlibrary(sandwich)\nsummary(m0)                                                    # 普通 SE（参考）\ncoeftest(m0, vcov = NeweyWest(m0, lag = 1, prewhite = FALSE))  # HAC SE，lag 固定为 1\ncoeftest(m0, vcov = NeweyWest(m0))                             # 默认：自动带宽 + prewhite\n# 注：NeweyWest 默认 prewhite = TRUE 且自动选带宽；与 Stata lag(1) 对照时\n#     用 lag = 1, prewhite = FALSE（以 help(NeweyWest) 为准）\n\n# ③ 备选：AR(1) 误差模型（nlme）\nlibrary(nlme)\ng1 <- gls(y ~ time_c + level + trend_after, data = dat,\n          correlation = corAR1(form = ~ time))\nsummary(g1)","source":"skills/its-skill.md","status":"模板；未逐项执行","line":194}},{"id":"code-library:its-skill-9","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 4 步：模型估计：Stata 与 R 实现（对照）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"its-skill-9","title":"第 4 步：模型估计：Stata 与 R 实现（对照）","language":"stata","code":"* ============ 4.1 手动分段回归 + Newey-West ============\nnewey y time_c level trend_after, lag(1) force\n* 注：任务书简写 newey y time level trend, lag(1) force；此处 time 为干预时点\n*     中心化的 time_c；force 允许未 tsset 的数据仍按时间序列估计（已 tsset 可省略）\nestimates store its_manual\n\n* 组合效应（HAC 标准误下的线性组合检验）\nlincom time_c + trend_after              // 干预后总斜率（β1+β3）\nlincom level + 23*trend_after            // 期末（第 48 月）总效应（β2+23·β3）\n\n* ============ 4.2 itsa 命令（SSC，Ariel Linden）============\n* ssc install itsa, replace\nitsa y, single treatperiod(25) posttrend\n* 注：任务书示例写法 itsa y, single treat(25) 中的选项名，实际对应\n*     treatperiod(25)，以 help itsa 为准；itsa 默认 OLS + Newey-West 标准误。\n* 带滞后因变量的动态版本：itsa y, single treatperiod(25) lag(1) posttrend\n* 出图：itsa y, single treatperiod(25) posttrend figure","source":"skills/its-skill.md","status":"模板；未逐项执行","line":238}},{"id":"code-library:its-skill-10","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 4 步：模型估计：Stata 与 R 实现（对照）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"its-skill-10","title":"第 4 步：模型估计：Stata 与 R 实现（对照）","language":"r","code":"# ============ R：lm + NeweyWest，与 Stata 对照 ============\nm <- lm(y ~ time_c + level + trend_after, data = dat)\n\nsummary(m)                                                    # 普通 SE（参考）\ntab_hac <- coeftest(m, vcov = NeweyWest(m, lag = 1, prewhite = FALSE))\nprint(tab_hac)                                                # HAC SE\ncoefci(tab_hac)                                               # 基于 HAC 的 95% CI\n\n# 组合效应（HAC 协方差下的线性组合与 delta 方法）\nlinearHypothesis(m, \"time_c + trend_after = 0\",\n                 vcov. = NeweyWest(m, lag = 1, prewhite = FALSE))   # 干预后总斜率\ndeltaMethod(m, \"level + 23*trend_after\",\n            vcov. = NeweyWest(m, lag = 1, prewhite = FALSE))         # 期末总效应","source":"skills/its-skill.md","status":"模板；未逐项执行","line":258}},{"id":"code-library:its-skill-11","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 5 步：图形呈现（原始序列 + 拟合 + 反事实）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"its-skill-11","title":"第 5 步：图形呈现（原始序列 + 拟合 + 反事实）","language":"stata","code":"newey y time_c level trend_after, lag(1)\npredict yhat, xb\ngen yhat_cf = _b[_cons] + _b[time_c]*time_c       // 反事实：延续干预前趋势\n\ntwoway (scatter y time, msize(small) mcolor(gs8)) ///\n       (line yhat time, lcolor(navy) lwidth(medthick)) ///\n       (line yhat_cf time if time >= 25, lcolor(red) lpattern(dash) lwidth(medthick)) ///\n       , xline(24.5, lcolor(gs10) lpattern(dash)) ///\n         xtitle(\"月份\") ytitle(\"次均费用（元）\") ///\n         title(\"ITS：DRG 改革对次均住院费用的影响\") ///\n         legend(order(1 \"观测值\" 2 \"模型拟合\" 3 \"反事实（无干预）\") position(6) rows(1))","source":"skills/its-skill.md","status":"模板；未逐项执行","line":284}},{"id":"code-library:its-skill-12","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 5 步：图形呈现（原始序列 + 拟合 + 反事实）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"its-skill-12","title":"第 5 步：图形呈现（原始序列 + 拟合 + 反事实）","language":"r","code":"# R（基础包）\nm <- lm(y ~ time_c + level + trend_after, data = dat)\ndat$fit <- fitted(m)\nb <- coef(m)\ndat$cf  <- b[\"(Intercept)\"] + b[\"time_c\"] * dat$time_c     # 反事实\n\nplot(y ~ time, data = dat, type = \"o\", pch = 16, cex = 0.7, col = \"gray50\",\n     main = \"ITS：DRG 改革对次均住院费用的影响\",\n     xlab = \"月份\", ylab = \"次均费用（元）\")\nabline(v = 24.5, lty = 3, col = \"gray40\")\nlines(dat$time, dat$fit, col = \"navy\", lwd = 2)\nlines(dat$time[dat$time >= 25], dat$cf[dat$time >= 25], col = \"red\", lwd = 2, lty = 2)\nlegend(\"bottomleft\", legend = c(\"观测值\", \"模型拟合\", \"反事实（无干预）\"),\n       col = c(\"gray50\", \"navy\", \"red\"), lty = c(1, 1, 2),\n       lwd = c(1, 2, 2), bty = \"n\")\n\n# ggplot2 等价写法（ggplot2 ≥ 3.4 用 linewidth，低版本用 size）\nlibrary(ggplot2)\nggplot(dat, aes(time, y)) +\n  geom_point(colour = \"grey50\") +\n  geom_vline(xintercept = 24.5, linetype = 3) +\n  geom_line(aes(y = fit), colour = \"navy\", linewidth = 1) +\n  geom_line(data = subset(dat, time >= 25), aes(y = cf),\n            colour = \"red\", linetype = 2, linewidth = 1) +\n  labs(x = \"月份\", y = \"次均费用（元）\",\n       title = \"ITS：DRG 改革对次均住院费用的影响\") +\n  theme_minimal()","source":"skills/its-skill.md","status":"模板；未逐项执行","line":298}},{"id":"code-library:its-skill-13","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 6 步：稳健性与检验","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"its-skill-13","title":"第 6 步：稳健性与检验","language":"stata","code":"* ---- 6.1 安慰剂时点：假设干预发生在第 13 个月（实际无干预）----\ngen time_c13 = time - 13\ngen level13  = time >= 13\ngen trend13  = time_c13 * level13\nnewey y time_c13 level13 trend13, lag(1)\n* 期望：level13、trend13 系数接近 0 且不显著\n\n* ---- 6.2 季节性：加入月度虚拟变量（参考月 = 1 月）----\ngen month = mod(time - 1, 12) + 1\ntab month, gen(md)                          // 生成 md1–md12\nnewey y time_c level trend_after md2-md12, lag(1)\n* 期望：β2、β3 与主模型接近（以实际结果为准）\n\n* ---- 6.3 控制组 ITS（CITS）：双地区面板示意 ----\n* 面板长表：id（地区）、time、y；treat = 1（干预地区，第 25 个月起受干预）\n* 多组 itsa（语法与选项以 help itsa 为准）：\n*     xtset id time\n*     itsa y, treatperiod(25) treatid(treat) posttrend\n* 手动 DID 型交互模型（示意）：\ngen t_c   = time - 25\ngen lev   = time >= 25\ngen tr_a  = t_c * lev\ngen g_lev = treat * lev        // 水平变化的组间差（政策即时效应，DID）\ngen g_tr  = treat * tr_a       // 斜率变化的组间差（政策趋势效应，DID）\nnewey y t_c treat lev tr_a g_lev g_tr, lag(1)\n* 关键系数：g_lev（相对对照组的即时水平变化）、g_tr（相对对照组的斜率变化）\n\n* ---- 6.4 异常值敏感性：1%/99% 缩尾 ----\nquietly summarize y, detail\ngen y_w = min(max(y, r(p1)), r(p99))\nnewey y_w time_c level trend_after, lag(1)","source":"skills/its-skill.md","status":"模板；未逐项执行","line":342}},{"id":"code-library:its-skill-14","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 6 步：稳健性与检验","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"its-skill-14","title":"第 6 步：稳健性与检验","language":"r","code":"# ---- 6.1 安慰剂时点：假设干预发生在第 13 个月 ----\ndat$time_c13 <- dat$time - 13\ndat$level13  <- as.numeric(dat$time >= 13)\ndat$trend13  <- dat$time_c13 * dat$level13\nm_placebo <- lm(y ~ time_c13 + level13 + trend13, data = dat)\ncoeftest(m_placebo, vcov = NeweyWest(m_placebo, lag = 1, prewhite = FALSE))\n# 期望：level13、trend13 接近 0 且不显著\n\n# ---- 6.2 季节性：加入月度虚拟变量 ----\ndat$month <- factor(((dat$time - 1) %% 12) + 1)\nm_season <- lm(y ~ time_c + level + trend_after + month, data = dat)\ncoeftest(m_season, vcov = NeweyWest(m_season, lag = 1, prewhite = FALSE))\n# 更平滑的季节建模可用正弦/余弦对（Fourier terms），以数据周期为准\n\n# ---- 6.3 控制组 ITS（CITS）：面板数据交互项 ----\n# 面板长表 panel：id、time、y、treat（1 = 干预地区）\npanel$t_c  <- panel$time - 25\npanel$lev  <- as.numeric(panel$time >= 25)\npanel$tr_a <- panel$t_c * panel$lev\nm_cits <- lm(y ~ t_c + treat + lev + tr_a + treat:lev + treat:tr_a, data = panel)\ncoeftest(m_cits, vcov = NeweyWest(m_cits, lag = 1, prewhite = FALSE))\n# 政策效应 = treat:lev（即时）与 treat:tr_a（斜率）的系数\n\n# ---- 6.4 异常值敏感性：1%/99% 缩尾 ----\nq <- quantile(dat$y, c(0.01, 0.99))\ndat$y_w <- pmin(pmax(dat$y, q[1]), q[2])\nm_w <- lm(y_w ~ time_c + level + trend_after, data = dat)\ncoeftest(m_w, vcov = NeweyWest(m_w, lag = 1, prewhite = FALSE))","source":"skills/its-skill.md","status":"模板；未逐项执行","line":376}},{"id":"code-library:its-skill-15","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 7 步：报告清单","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"its-skill-15","title":"第 7 步：报告清单","language":"stata","code":"* 主表：HAC 标准误（esttab，SSC estout）\nnewey y time_c level trend_after, lag(1)\nestimates store its_main\nesttab its_main, b(%9.2f) se(%9.2f) p(%9.3f) stats(N r2, fmt(%9.0f %9.3f)) ///\n    title(\"表 1：ITS 分段回归（Newey-West 标准误，lag(1)）\") ///\n    addnotes(\"干预时点：第 25 个月；time_c 为干预时点中心化的月份。\")","source":"skills/its-skill.md","status":"模板；未逐项执行","line":415}},{"id":"code-library:its-skill-16","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 7 步：报告清单","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"its-skill-16","title":"第 7 步：报告清单","language":"r","code":"# 主表：用 HAC 标准误覆盖（texreg；以包文档为准）\nlibrary(texreg)\nhac_se <- sqrt(diag(NeweyWest(m, lag = 1, prewhite = FALSE)))\nscreenreg(m, override.se = hac_se,\n          custom.model.names = \"ITS 分段回归（HAC 标准误）\")","source":"skills/its-skill.md","status":"模板；未逐项执行","line":424}},{"id":"code-library:its-skill-17","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"输出格式（ITS 报告模板）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"its-skill-17","title":"输出格式（ITS 报告模板）","language":"text","code":"【研究设计与数据】\n结局（次均住院费用，元/次）与时间单位（月）；序列区间（20XX 年 X 月–20XX 年 X 月，\n共 48 个月）；干预（DRG 付费改革）与实施时点（第 25 个月）；干预前后各 24 个月；\n数据来源、缺失/插补、异常值处理。\n模型：Y = β0 + β1·time_c + β2·level + β3·trend_after + ε；time_c 为干预时点中心化。\n\n【主要结果】\n- 序列描述：均值、SD、干预前趋势（β1）与干预时点基线（β0）。\n- 自相关诊断：残差 ACF、DW / actest（或 BG）结果 → 采用 Newey-West 修正的理由。\n- 表 1：ITS 分段回归（β0–β3，HAC 标准误、95% CI、p 值；注明滞后阶数及选择依据）。\n- 效应解读：β2 = 即时水平变化（元）；β3 = 斜率变化（元/月）；\n  干预后总斜率 = β1 + β3；期末总效应 = β2 + k·β3（注明 k）。\n- 图 1：原始序列 + 分段拟合 + 反事实虚线 + 干预时点竖线。\n\n【稳健性检验】\n- 表 2：安慰剂时点（第 X 个月）结果：β2、β3 应接近 0；\n- 季节调整（月度虚拟/Fourier）后核心系数变化；\n- 异常值敏感性（1%/99% 缩尾）；\n- CITS（如有对照）：treat:level 与 treat:trend_after 交互项系数。\n\n【结论与局限】\n- 政策含义：方向、大小、持续时间（如「DRG 改革使次均费用即时下降约 600 元，\n  且此后每月多降约 30 元」）。\n- 局限：单组设计无法排除同期事件；自相关结构与滞后选择；序列长度与检验力；\n  单一机构结果的外部效度。","source":"skills/its-skill.md","status":"模板；未逐项执行","line":451}},{"id":"code-library:machine-learning-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"软件准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"machine-learning-skill-1","title":"软件准备","language":"stata","code":"* Stata（联网安装一次）\nssc install rforest, replace","source":"skills/machine-learning-skill.md","status":"模板；未逐项执行","line":29}},{"id":"code-library:machine-learning-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"软件准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"machine-learning-skill-2","title":"软件准备","language":"r","code":"# R\ninstall.packages(c(\"glmnet\", \"ranger\", \"xgboost\", \"pROC\", \"shapviz\",\n                   \"caret\", \"rms\", \"mice\", \"Matrix\", \"DALEX\"))","source":"skills/machine-learning-skill.md","status":"模板；未逐项执行","line":34}},{"id":"code-library:machine-learning-skill-3","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 1 步：问题定义与数据准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"machine-learning-skill-3","title":"第 1 步：问题定义与数据准备","language":"r","code":"# ---- R：生成模拟数据（N = 1000，可全程复现）----\nset.seed(2026)\nn <- 1000\ndat <- data.frame(\n  id        = 1:n,\n  age       = round(rnorm(n, 65, 12)),        # 年龄\n  sex       = rbinom(n, 1, 0.52),             # 1 = 女\n  comorb    = rpois(n, 1.5),                  # 合并症计数\n  insurance = sample(c(\"城职\", \"城居\", \"新农合\"), n, replace = TRUE,\n                     prob = c(0.4, 0.3, 0.3)),\n  los       = rpois(n, 7) + 1                 # 住院天数\n)\ndat$age    <- pmin(pmax(dat$age, 18), 95)\ndat$comorb <- pmin(dat$comorb, 8)\nfor (k in 1:5) dat[[paste0(\"z\", k)]] <- rnorm(n)   # 纯噪声特征\n\n# 结局：同一套真实信号驱动两个结局（分类 + 连续）\nlp <- -5 + 0.03 * dat$age + 0.45 * dat$comorb + 0.12 * dat$los +\n      0.40 * (dat$insurance == \"城职\") + 0.25 * dat$sex\ndat$highcost <- rbinom(n, 1, plogis(lp))      # 高费用 yes/no（约 25%）\ndat$cost <- exp(7.5 + 0.01 * dat$age + 0.18 * dat$comorb + 0.05 * dat$los +\n                0.25 * (dat$insurance == \"城职\") + 0.9 * dat$highcost +\n                rnorm(n, 0, 0.5))             # 总费用（元，右偏）\ntable(dat$highcost); summary(dat)\n\n# ---- 人为制造 10% MCAR 缺失（演示缺失处理用）----\nset.seed(2)\ndat$comorb[sample(n, 100)] <- NA\n\n# ---- 70 / 15 / 15 拆分 ----\nset.seed(3)\ndat$split <- sample(rep(1:3, c(700, 150, 150)))   # 1=训练 2=验证 3=测试\ntr <- dat[dat$split == 1, ]; va <- dat[dat$split == 2, ]; te <- dat[dat$split == 3, ]\n\n# ---- 缺失值处理：用训练集中位数插补三组（演示；正式研究用 MICE 且在 CV 折内）----\nm_med <- median(tr$comorb, na.rm = TRUE)\ntr$comorb <- ifelse(is.na(tr$comorb), m_med, tr$comorb)\nva$comorb <- ifelse(is.na(va$comorb), m_med, va$comorb)\nte$comorb <- ifelse(is.na(te$comorb), m_med, te$comorb)\n\n# 多重插补示例（正式研究用；注意应在 CV 折内进行）\n# library(mice)\n# imp <- mice(dat[, c(\"age\",\"sex\",\"comorb\",\"los\",\"highcost\",\"cost\")], m = 5, seed = 4)\n# fit <- with(imp, glm(highcost ~ age + sex + comorb + los, family = binomial))\n# pool(fit)\n\n# ---- 标准化：只在训练集上计算均值 / SD，再套用到三组 ----\nfor (v in c(\"age\", \"comorb\", \"los\")) {\n  m <- mean(tr[[v]]); s <- sd(tr[[v]])\n  tr[[paste0(v, \"_s\")]] <- (tr[[v]] - m) / s\n  va[[paste0(v, \"_s\")]] <- (va[[v]] - m) / s\n  te[[paste0(v, \"_s\")]] <- (te[[v]] - m) / s\n}","source":"skills/machine-learning-skill.md","status":"模板；未逐项执行","line":59}},{"id":"code-library:machine-learning-skill-4","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"---- 标准化：只在训练集上计算均值 / SD，再套用到三组 ----","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"machine-learning-skill-4","title":"---- 标准化：只在训练集上计算均值 / SD，再套用到三组 ----","language":"stata","code":"* ---- Stata：生成同一套模拟数据 ----\nclear\nset seed 2026\nset obs 1000\ngen id = _n\ngen age = round(rnormal(65, 12))\nreplace age = min(max(age, 18), 95)\ngen sex = rbinomial(1, 0.52)\ngen comorb = min(rpoisson(1.5), 8)\ngen u = runiform()\ngen ins = cond(u < 0.4, 1, cond(u < 0.7, 2, 3))   // 1=城职 2=城居 3=新农合\nlabel define inslab 1 \"城职\" 2 \"城居\" 3 \"新农合\"\nlabel values ins inslab\ngen los = rpoisson(7) + 1\nforvalues k = 1/5 {\n    gen z`k' = rnormal()                         // 纯噪声特征\n}\ngen lp = -5 + 0.03*age + 0.45*comorb + 0.12*los + 0.40*(ins == 1) + 0.25*sex\ngen highcost = rbinomial(1, invlogit(lp))\ngen cost = exp(7.5 + 0.01*age + 0.18*comorb + 0.05*los + 0.25*(ins == 1) + 0.9*highcost + rnormal(0, 0.5))\ngen lncost = ln(cost)\ntab highcost\n\n* ---- 人为制造 10% MCAR 缺失 ----\nset seed 2026\nreplace comorb = . if runiform() < 0.10\n\n* ---- 70 / 15 / 15 拆分（1=训练 2=验证 3=测试）----\nsplitsample, generate(svar, replace) split(0.70 0.15 0.15) rseed(3)\ntab svar\n\n* ---- 缺失值处理：训练集中位数插补（演示；正式研究用 mi impute chained）----\nsummarize comorb if svar == 1, detail\nreplace comorb = r(p50) if missing(comorb)\n\n* 多重插补示例（正式研究用）\n* mi set mlong\n* mi register imputed comorb\n* mi impute chained (pmm) comorb = age sex los highcost cost, add(5) rseed(4)\n\n* ---- 标准化：以训练集均值 / SD 为基准 ----\nforeach v in age comorb los {\n    quietly summarize `v' if svar == 1\n    gen `v'_s = (`v' - r(mean)) / r(sd)\n}","source":"skills/machine-learning-skill.md","status":"模板；未逐项执行","line":115}},{"id":"code-library:machine-learning-skill-5","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 2 步：基线模型与变量筛选","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"machine-learning-skill-5","title":"第 2 步：基线模型与变量筛选","language":"r","code":"# ---- 模型矩阵（glmnet 不接受因子，需 model.matrix；含噪声变量观察收缩效果）----\nlibrary(glmnet)\nxvars <- c(\"age_s\", \"sex\", \"comorb_s\", \"los_s\", \"z1\", \"z2\", \"z3\", \"z4\", \"z5\", \"insurance\")\nf <- as.formula(paste(\"~\", paste(xvars, collapse = \" + \")))\nx_tr <- model.matrix(f, data = tr)[, -1]\nx_va <- model.matrix(f, data = va)[, -1]\nx_te <- model.matrix(f, data = te)[, -1]\ny_tr <- tr$highcost; y_te <- te$highcost\n\n# ---- 基线 logistic（不收缩，作为及格线）----\nbase <- glm(highcost ~ age_s + sex + comorb_s + los_s + insurance + z1 + z2 + z3 + z4 + z5,\n            data = tr, family = binomial)\nsummary(base)\n\n# 备选：逐步回归（仅作敏感性分析）\n# library(MASS)\n# stepAIC(base, direction = \"both\", trace = FALSE)\n\n# ---- 基线线性回归（连续费用，取对数）----\nlm_base <- lm(log(cost) ~ age_s + sex + comorb_s + los_s + insurance, data = tr)\nsummary(lm_base)\n\n# ---- LASSO（alpha = 1），10 折 CV 选 λ ----\nset.seed(7)\ncv_lasso <- cv.glmnet(x_tr, y_tr, family = \"binomial\", alpha = 1, nfolds = 10)\nplot(cv_lasso)                                # CV 误差曲线（两条虚线 = lambda.min / 1se）\ncoef(cv_lasso, s = \"lambda.min\")              # 噪声变量 z1–z5 应为 0\ncoef(cv_lasso, s = \"lambda.1se\")              # 更保守（1-SE 法则，模型更小）\n\n# ---- 弹性网（alpha = 0.5）----\nset.seed(8)\ncv_en <- cv.glmnet(x_tr, y_tr, family = \"binomial\", alpha = 0.5, nfolds = 10)\ncoef(cv_en, s = \"lambda.1se\")\n\n# ---- λ 路径图（系数随 log(λ) 增大被收缩至 0 的轨迹）----\nplot(cv_lasso$glmnet.fit, xvar = \"lambda\", label = TRUE)\nabline(v = log(cv_lasso$lambda.1se), lty = 2)\n\n# ---- 连续结局版：LASSO 回归（对数费用）----\nset.seed(9)\ncv_lasso_reg <- cv.glmnet(x_tr, log(tr$cost), alpha = 1, nfolds = 10)\ncoef(cv_lasso_reg, s = \"lambda.1se\")","source":"skills/machine-learning-skill.md","status":"模板；未逐项执行","line":177}},{"id":"code-library:machine-learning-skill-6","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"---- 连续结局版：LASSO 回归（对数费用）----","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"machine-learning-skill-6","title":"---- 连续结局版：LASSO 回归（对数费用）----","language":"stata","code":"* ---- 基线 logistic（含噪声变量，作为及格线）----\nlogit highcost age_s sex comorb_s los_s i.insurance z1-z5 if svar == 1\nest store base\n\n* ---- 基线线性回归（对数费用）----\nregress lncost age_s sex comorb_s los_s i.insurance if svar == 1\n\n* ---- LASSO（Stata 16+），CV 选 λ ----\nlasso logit highcost age_s sex comorb_s los_s i.insurance z1-z5 if svar == 1, selection(cv) rseed(7)\ncvplot                                     // CV 均方误差曲线\ncoefpath                                    // λ 路径（系数收缩轨迹）\nlassocoef, display(coef, standardized)      // 选后系数：噪声变量应被剔除\n\n* ---- 自适应 LASSO：先 CV 选 λ，再加自适应权重重估 ----\nlasso, select(adaptive)\nlassocoef, display(coef, standardized)\n\n* ---- 弹性网（Stata 17+）----\nelasticnet logit highcost age_s sex comorb_s los_s i.insurance z1-z5 if svar == 1, selection(cv) alpha(0.5) rseed(8)\nlassocoef, display(coef, standardized)\n\n* ---- 连续结局版：LASSO 回归 ----\nlasso linear lncost age_s sex comorb_s los_s i.insurance z1-z5 if svar == 1, selection(cv) rseed(9)\nlassocoef, display(coef, standardized)","source":"skills/machine-learning-skill.md","status":"模板；未逐项执行","line":222}},{"id":"code-library:machine-learning-skill-7","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 3 步：树模型与集成","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"machine-learning-skill-7","title":"第 3 步：树模型与集成","language":"r","code":"# ---- 统一建模数据框（特征 + 结局）----\nfeats <- c(\"age_s\", \"sex\", \"comorb_s\", \"los_s\", \"insurance\",\n           \"z1\", \"z2\", \"z3\", \"z4\", \"z5\", \"highcost\")\ntr_m <- tr[, feats]; va_m <- va[, feats]; te_m <- te[, feats]\n\n# ---- 随机森林（ranger；分类结局须为 factor）----\nlibrary(ranger)\nset.seed(11)\nrf <- ranger(factor(highcost) ~ ., data = tr_m,\n             num.trees = 500, mtry = 3, min.node.size = 10,\n             importance = \"impurity\", probability = TRUE)\nrf                                          # OOB 预测误差\nrf$variable.importance                      # 变量重要性\np_rf_va <- predict(rf, data = va_m)$predictions[, \"1\"]   # 验证集概率（调参用）\np_rf_te <- predict(rf, data = te_m)$predictions[, \"1\"]   # 测试集概率（仅评估一次）\n\n# ---- XGBoost：xgb.cv 单组参数 + 早停 ----\nlibrary(xgboost)\nlibrary(Matrix)\nmtr <- sparse.model.matrix(highcost ~ . - 1, data = tr_m)\nmva <- sparse.model.matrix(highcost ~ . - 1, data = va_m)\nmte <- sparse.model.matrix(highcost ~ . - 1, data = te_m)\ndtrain <- xgb.DMatrix(mtr, label = tr_m$highcost)\ndval   <- xgb.DMatrix(mva, label = va_m$highcost)\ndtest  <- xgb.DMatrix(mte, label = te_m$highcost)\n\nset.seed(12)\ncvx <- xgb.cv(params = list(objective = \"binary:logistic\", eval_metric = \"auc\",\n                            eta = 0.05, max_depth = 3,\n                            subsample = 0.8, colsample_bytree = 0.8),\n              data = dtrain, nrounds = 500, nfold = 5,\n              early_stopping_rounds = 20, verbose = 0)\ncvx$best_iteration                                   # 最佳轮数\nmax(cvx$evaluation_log$test_auc_mean)                # CV AUC\n\n# ---- XGBoost：手动小网格（验证集早停选参）----\ngrid <- expand.grid(eta = c(0.05, 0.1), max_depth = c(2, 3, 4))\nres <- lapply(seq_len(nrow(grid)), function(i) {\n  p <- list(objective = \"binary:logistic\", eval_metric = \"auc\",\n            eta = grid$eta[i], max_depth = grid$max_depth[i],\n            subsample = 0.8, colsample_bytree = 0.8)\n  xgb.train(params = p, data = dtrain, nrounds = 500,\n            watchlist = list(val = dval), early_stopping_rounds = 20, verbose = 0)\n})\nva_auc <- sapply(res, function(m) m$best_score)       # 各组合验证集 AUC\nbest_i <- which.max(va_auc)\ngrid[best_i, ]                                        # 最优参数\nbest_n <- res[[best_i]]$best_iteration                # 最优轮数\n\n# 用最优参数 + 最优轮数在「训练+验证」上重训，测试集只评估一次\nfull <- xgb.DMatrix(rbind(mtr, mva), label = c(tr_m$highcost, va_m$highcost))\nbst <- xgb.train(params = res[[best_i]]$params, data = full, nrounds = best_n)\np_xgb_te <- predict(bst, dtest)\nxgb.importance(model = bst)                           # Gain / Cover 重要性\n\n# ---- 等价写法：caret 网格调参（二选一）----\nlibrary(caret)\nset.seed(13)\ncg <- expand.grid(nrounds = 300, max_depth = c(2, 3, 4), eta = c(0.05, 0.1),\n                  gamma = 0, colsample_bytree = 0.8,\n                  min_child_weight = 1, subsample = 0.8)\nctrl <- trainControl(method = \"cv\", number = 5, classProbs = TRUE,\n                     summaryFunction = twoClassSummary)\nfit_caret <- train(x = tr_m[, -ncol(tr_m)], y = factor(tr_m$highcost),\n                   method = \"xgbTree\", trControl = ctrl, tuneGrid = cg,\n                   metric = \"ROC\")\nfit_caret$bestTune","source":"skills/machine-learning-skill.md","status":"模板；未逐项执行","line":261}},{"id":"code-library:machine-learning-skill-8","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"---- 等价写法：caret 网格调参（二选一）----","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"machine-learning-skill-8","title":"---- 等价写法：caret 网格调参（二选一）----","language":"stata","code":"* ---- 随机森林（rforest，SSC；不支持因子变量写法，先造虚拟变量）----\ntab ins if svar == 1, gen(ins_)\nrforest highcost age_s sex comorb_s los_s ins_2 ins_3 z1-z5 if svar == 1, type(class) iterations(500) numvars(3) seed(11) vimp\n* 预测概率的变量命名与后处理以 help rforest 为准；\n* 调参（numvars / depth / lsize）应在验证集（svar == 2）上比较 AUC 后进行\n\n* ---- 官方替代：梯度提升 boosting（Stata 17+）、svmachines（Stata 18）----\n* boosting highcost age_s sex comorb_s los_s ins_2 ins_3 z1-z5 if svar == 1\n* 语法与调参选项以 help boosting 为准","source":"skills/machine-learning-skill.md","status":"模板；未逐项执行","line":331}},{"id":"code-library:machine-learning-skill-9","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 4 步：模型评估与比较","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"machine-learning-skill-9","title":"第 4 步：模型评估与比较","language":"r","code":"library(pROC)\n\n# ---- 各模型在测试集的预测概率（基线 / LASSO 出自 glm 与 glmnet）----\np_base_te  <- predict(base, newdata = te, type = \"response\")\np_lasso_te <- drop(predict(cv_lasso, newx = x_te, s = \"lambda.1se\", type = \"response\"))\n# p_rf_te、p_xgb_te 来自第 3 步\n\nroc_base  <- roc(y_te, p_base_te)\nroc_lasso <- roc(y_te, p_lasso_te)\nroc_rf    <- roc(y_te, p_rf_te)\nroc_xgb   <- roc(y_te, p_xgb_te)\n\nauc(roc_base); auc(roc_lasso); auc(roc_rf); auc(roc_xgb)\nci.auc(roc_rf)                                   # bootstrap 95% CI（默认 2000 次）\n\n# ---- 模型比较：DeLong 检验（相关样本）----\nroc.test(roc_rf, roc_base, method = \"delong\")    # RF vs 基线 logistic\nroc.test(roc_xgb, roc_rf, method = \"delong\")     # XGB vs RF\n\n# ---- ROC 曲线叠加 ----\nplot(roc_base, col = \"grey50\", lwd = 2)\nplot(roc_lasso, add = TRUE, col = \"steelblue\", lwd = 2)\nplot(roc_rf, add = TRUE, col = \"firebrick\", lwd = 2)\nplot(roc_xgb, add = TRUE, col = \"darkgreen\", lwd = 2)\nlegend(\"bottomright\", legend = c(\"logistic\", \"LASSO\", \"RF\", \"XGBoost\"),\n       col = c(\"grey50\", \"steelblue\", \"firebrick\", \"darkgreen\"), lwd = 2)\n\n# ---- 阈值（Youden）与敏感性 / 特异性 ----\ncoords(roc_xgb, \"best\", ret = c(\"threshold\", \"sensitivity\", \"specificity\"))\n\n# ---- 校准：Brier 分数 + 校准曲线（截距≈0、斜率≈1 为校准良好）----\nbrier <- function(p, y) mean((p - y)^2)\nbrier(p_base_te, y_te); brier(p_rf_te, y_te); brier(p_xgb_te, y_te)\n\nlibrary(rms)\nval.prob(p_xgb_te, y_te, statloc = FALSE)        # 校准图 + Brier + 校准截距/斜率\n# 校准差 → Platt scaling / isotonic regression 等再校准，或回退简单模型\n\n# ---- 回归（对数费用）：RMSE / MAE / R² ----\np_lm_te   <- predict(lm_base, newdata = te)\np_lreg_te <- drop(predict(cv_lasso_reg, newx = x_te, s = \"lambda.1se\"))\ny_cont <- log(te$cost)\nrmse <- function(p, y) sqrt(mean((p - y)^2))\nmae  <- function(p, y) mean(abs(p - y))\nrmse(p_lm_te, y_cont);   mae(p_lm_te, y_cont)\nrmse(p_lreg_te, y_cont); mae(p_lreg_te, y_cont)\n1 - sum((p_lreg_te - y_cont)^2) / sum((y_cont - mean(y_cont))^2)   # R²","source":"skills/machine-learning-skill.md","status":"模板；未逐项执行","line":356}},{"id":"code-library:machine-learning-skill-10","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"---- 回归（对数费用）：RMSE / MAE / R² ----","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"machine-learning-skill-10","title":"---- 回归（对数费用）：RMSE / MAE / R² ----","language":"stata","code":"* ---- 各模型预测概率（基线 / LASSO 用各自 predict 产出）----\nlogit highcost age_s sex comorb_s los_s i.insurance z1-z5 if svar == 1\npredict p_base, pr\nlasso logit highcost age_s sex comorb_s los_s i.insurance z1-z5 if svar == 1, selection(cv) rseed(7)\npredict p_lasso, pr\n* （rforest / boosting 的预测变量按各自 postestimation 命令生成）\n\n* ---- 单模型 ROC 与 AUC ----\nroctab highcost p_base if svar == 3, graph\n* logit 拟合后可用 lroc（含 AUC 与 ROC 图）\n\n* ---- 多模型比较（DeLong 法与 Hanley–McNeil 法，只评估测试集）----\nroccomp highcost p_base p_lasso p_rf p_xgb if svar == 3, graph\n\n* ---- 校准：重拟合检验截距/斜率（应接近 0/1）+ Hosmer–Lemeshow + Brier ----\nlogit highcost p_xgb if svar == 3\nestat gof, group(10)\ngen b2 = (highcost - p_xgb)^2 if svar == 3\nsummarize b2                                // r(mean) 即 Brier 分数\n\n* ---- 回归指标：RMSE / MAE（对数费用）----\ngen e2 = (lncost - p_lreg)^2 if svar == 3\nsummarize e2\ndisplay \"RMSE = \" sqrt(r(mean))\ngen ae = abs(lncost - p_lreg) if svar == 3\nsummarize ae\ndisplay \"MAE = \" r(mean)","source":"skills/machine-learning-skill.md","status":"模板；未逐项执行","line":406}},{"id":"code-library:machine-learning-skill-11","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 5 步：可解释性","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"machine-learning-skill-11","title":"第 5 步：可解释性","language":"r","code":"library(shapviz)\n\n# ---- SHAP 值（XGBoost，测试集）----\nsv <- shapviz(bst, X_pred = mte)\nsv_importance(sv, kind = \"both\")                 # 平均|SHAP|条形图 + 蜂群图\nsv_importance(sv, kind = \"beeswarm\")             # 蜂群图：颜色 = 特征取值方向\nsv_dependence(sv, v = \"age_s\")                   # 依赖图：年龄的非线性贡献\n\n# ---- 单个患者瀑布图 ----\ni_hi <- which.max(p_xgb_te)                      # 测试集中预测风险最高者\nsv_waterfall(sv, row_id = i_hi)\n\n# ---- 随机森林置换重要性（比杂质重要性更稳健）----\nset.seed(14)\nrf_p <- ranger(factor(highcost) ~ ., data = te_m, num.trees = 500,\n               importance = \"permutation\", probability = TRUE)\nrf_p$variable.importance\n\n# ---- 备选：DALEX（模型无关解释）----\n# library(DALEX)\n# ex <- explain(bst, data = as.data.frame(as.matrix(mte)), y = te_m$highcost)\n# plot(model_parts(ex))                        # 置换重要性","source":"skills/machine-learning-skill.md","status":"模板；未逐项执行","line":449}},{"id":"code-library:machine-learning-skill-12","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"plot(model_parts(ex))                        # 置换重要性","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"machine-learning-skill-12","title":"plot(model_parts(ex))                        # 置换重要性","language":"stata","code":"* ---- Stata 侧可解释性 ----\n* rforest 变量重要性：vimp 选项生成的 _rf_v_* 变量（见 help rforest）\n* lasso / elasticnet 的标准化系数即一种可解释性输出\nlassocoef, display(coef, standardized)\n* 局限：Stata 官方目前无 SHAP 实现；完整的 SHAP 分析建议在 R（shapviz）侧完成","source":"skills/machine-learning-skill.md","status":"模板；未逐项执行","line":476}},{"id":"code-library:machine-learning-skill-13","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"输出格式（预测建模报告模板）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"machine-learning-skill-13","title":"输出格式（预测建模报告模板）","language":"text","code":"【问题与数据】\n预测目标（分类/回归）、结局定义与观察时点、数据来源与样本量、拆分方案（70/15/15 或 CV）、\n缺失比例与处理方法、标准化说明。\n\n【模型构建】\n表 1：基线 logistic / 线性回归（OR 或系数 + 95% CI）；\n表 2：LASSO / 弹性网（λ 选择法则、入选变量与系数）；\n表 3：RF 与 XGBoost（超参数、调参方式、早停轮数）。\n\n【模型表现】\n表 4：测试集 AUC（95% CI）、Brier、校准截距/斜率（分类）或 RMSE / MAE / R²（回归）；\n模型比较（DeLong p 值或 bootstrap CI）。\n\n【解释】\n变量重要性排序（注明方法）；1–2 名患者的 SHAP 瀑布图与临床解读。\n\n【结论与落地】\nTRIPOD 条目对照、防泄漏清单核对结果、局限（内部 vs 外部验证）、\n部署与校准漂移监控计划、预测 ≠ 因果声明。","source":"skills/machine-learning-skill.md","status":"模板；未逐项执行","line":549}},{"id":"code-library:markov-model-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"软件准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-1","title":"软件准备","language":"stata","code":"* Stata：无需额外安装；确认版本 ≥ 17\nversion","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":25}},{"id":"code-library:markov-model-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"软件准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-2","title":"软件准备","language":"r","code":"# R\ninstall.packages(c(\"heemod\", \"expm\"))   # expm 可选","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":30}},{"id":"code-library:markov-model-skill-3","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 1 步：定义健康状态与模型结构","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-3","title":"第 1 步：定义健康状态与模型结构","language":"text","code":"无并发症 ──卒中(0.0198)──▶ 卒中后 ──死亡(0.0952)──▶ 死亡\n    │                         │\n    └─────背景死亡(0.010)─────┘\n（死亡为吸收态，无回退、无恢复；数字见第 2 步）","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":53}},{"id":"code-library:markov-model-skill-4","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 1 步：定义健康状态与模型结构","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-4","title":"第 1 步：定义健康状态与模型结构","language":"stata","code":"* Stata：模型结构骨架（数值在第 2–4 步给出）\n* 状态编号：1 = 无并发症  2 = 卒中后  3 = 死亡（吸收态）\n* 周期 = 1 年；时间范围 T = 30（敏感性情景 40）；起始分布 (1, 0, 0)","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":62}},{"id":"code-library:markov-model-skill-5","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 1 步：定义健康状态与模型结构","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-5","title":"第 1 步：定义健康状态与模型结构","language":"r","code":"# R：结构参数集中定义，供后续各步复用\nstates       <- c(\"无并发症\", \"卒中后\", \"死亡\")\ncycle_length <- 1        # 年\nT            <- 30       # 时间范围（年）；敏感性情景取 40\ninit         <- c(1, 0, 0)   # 起始：100% 无并发症\nnames(init)  <- states","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":68}},{"id":"code-library:markov-model-skill-6","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 2 步：转移概率的来源与估计","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-6","title":"第 2 步：转移概率的来源与估计","language":"stata","code":"* Stata：率 → 概率（t 为周期长度，按年）\ndisplay 1 - exp(-0.02*1)        // 卒中年发生率 0.02 → 年概率 = 0.0198013\ndisplay 1 - exp(-0.10*1)        // 卒中后死亡年率 0.10 → 年概率 = 0.0951626\ndisplay 1 - exp(-0.02*0.5)      // 半年概率 = 0.0099502\n* 概率 → 率（反向）\ndisplay -ln(1 - 0.0198013)/1\n* 由 5 年生存率 0.80 反推年率与年死亡概率（指数生存假设）\ndisplay -ln(0.80)/5             // 年死亡率 = 0.0446287\ndisplay 1 - exp(-(-ln(0.80)/5)) // 年死亡概率 = 0.0436481\n* 周期长度换算：1 年概率 0.0198013 → 3 个月（1/4 年）概率\ndisplay 1 - (1-0.0198013)^(1/4)","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":90}},{"id":"code-library:markov-model-skill-7","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 2 步：转移概率的来源与估计","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-7","title":"第 2 步：转移概率的来源与估计","language":"r","code":"# R：同样的换算\n1 - exp(-0.02*1)                    # 0.01980133\n1 - exp(-0.10*1)                    # 0.09516258\n-log(1 - 0.01980133)                # 概率 → 率\nS5 <- 0.80\nr_annual <- -log(S5)/5              # 5 年生存率反推年率\n1 - exp(-r_annual)                  # 年死亡概率\n# 周期长度换算\n1 - (1 - 0.01980133)^(1/4)","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":104}},{"id":"code-library:markov-model-skill-8","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 3 步：构建转移矩阵","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-8","title":"第 3 步：构建转移矩阵","language":"stata","code":"* Stata：转移矩阵（策略 A；行：无并发症 / 卒中后 / 死亡）\nmatrix PA = (0.9701987, 0.0198013, 0.0100000 \\ ///\n             0.0000000, 0.9048374, 0.0951626 \\ ///\n             0.0000000, 0.0000000, 1.0000000)\nmatrix list PA, format(%9.4f)\n\n* Mata 校验：行和与 20 年状态分布\nmata:\nPA = (0.9701987, 0.0198013, 0.010 \\ 0, 0.9048374, 0.0951626 \\ 0, 0, 1)\nrowsum(PA)                     // 应为 (1, 1, 1)'\n(1, 0, 0) * PA^20              // 20 年后分布：非负且和为 1\nend","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":134}},{"id":"code-library:markov-model-skill-9","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 3 步：构建转移矩阵","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-9","title":"第 3 步：构建转移矩阵","language":"r","code":"# R：转移矩阵（策略 A 与 B）\nPA <- matrix(c(0.9701987, 0.0198013, 0.0100000,\n               0.0000000, 0.9048374, 0.0951626,\n               0.0000000, 0.0000000, 1.0000000),\n             nrow = 3, byrow = TRUE)\nPB <- matrix(c(0.9760975, 0.0139025, 0.0100000,\n               0.0000000, 0.9048374, 0.0951626,\n               0.0000000, 0.0000000, 1.0000000),\n             nrow = 3, byrow = TRUE)\nrownames(PA) <- colnames(PA) <- c(\"无并发症\", \"卒中后\", \"死亡\")\nrownames(PB) <- colnames(PB) <- c(\"无并发症\", \"卒中后\", \"死亡\")\nrowSums(PA)                     # 1 1 1\nrowSums(PB)                     # 1 1 1\n# 20 年后分布（循环连乘；也可用 expm::`%^%`）\ntrace <- c(1, 0, 0)\nfor (i in 1:20) trace <- trace %*% PA\ntrace","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":149}},{"id":"code-library:markov-model-skill-10","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 4 步：成本与健康效用参数","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-10","title":"第 4 步：成本与健康效用参数","language":"stata","code":"* Stata：由均值 / 区间反推 PSA 分布参数（矩法）\n* beta：效用 0.85、sd 0.03 → alpha、beta\ndisplay 0.85*(0.85*(1-0.85)/0.03^2 - 1)          // alpha ≈ 119.57\ndisplay (1-0.85)*(0.85*(1-0.85)/0.03^2 - 1)      // beta  ≈ 21.10\n* gamma：成本 3000、sd 600 → shape、scale\ndisplay 3000^2/600^2                              // shape = 25\ndisplay 600^2/3000                                // scale = 120\n* lognormal：率 95% CI [0.015, 0.03] → sdlog\ndisplay (ln(0.03) - ln(0.015)) / (2*1.96)         // sdlog ≈ 0.177","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":181}},{"id":"code-library:markov-model-skill-11","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 4 步：成本与健康效用参数","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-11","title":"第 4 步：成本与健康效用参数","language":"r","code":"# R：同上（矩法）\nbeta_mom  <- function(m, sd) { k <- m*(1-m)/sd^2 - 1; c(alpha = m*k, beta = (1-m)*k) }\ngamma_mom <- function(m, sd) c(shape = m^2/sd^2, scale = sd^2/m)\nbeta_mom(0.85, 0.03)                     # alpha 119.57, beta 21.10\ngamma_mom(3000, 600)                     # shape 25, scale 120\n(log(0.03) - log(0.015)) / (2*1.96)      # sdlog 0.177","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":193}},{"id":"code-library:markov-model-skill-12","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"5.1 Stata 纯循环（完整代码）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-12","title":"5.1 Stata 纯循环（完整代码）","language":"stata","code":"* ============ 第 5 步：队列模拟（Stata 纯循环） ============\nlocal T = 30                     // 时间范围（年/周期）\n* 策略 A：常规治疗\nlocal p_hs1 = 1 - exp(-0.02)     // 无并发症→卒中（率 0.02/年）\nlocal p_hd1 = 0.010              // 无并发症→死亡\nlocal p_sd1 = 1 - exp(-0.10)     // 卒中后→死亡（率 0.10/年）\nlocal c_well1 = 3000             // 无并发症年成本\nlocal c_sick1 = 20000            // 卒中后年成本\nlocal u_well1 = 0.85             // 效用\nlocal u_sick1 = 0.62\n* 策略 B：干预（卒中风险降 30%）\nlocal p_hs2 = 1 - exp(-0.02*0.70)\nlocal p_hd2 = 0.010\nlocal p_sd2 = 1 - exp(-0.10)\nlocal c_well2 = 3500             // 无并发症年成本 + 500 元药费\nlocal c_sick2 = 20000\nlocal u_well2 = 0.85\nlocal u_sick2 = 0.62\n\nmatrix R = J(0, 4, .)            // 收集两策略结果：成本/QALY × (5%, 3%)\n\nforvalues s = 1/2 {\n    * ---- 状态占位（行 1..T+1；行 1 为起始分布） ----\n    clear\n    set obs `=`T'+1'\n    gen healthy = .\n    gen sick    = .\n    gen dead    = .\n    replace healthy = 1 in 1       // 起始：100% 无并发症\n    replace sick    = 0 in 1\n    replace dead    = 0 in 1\n    * ---- 转移循环：trace(t+1) = trace(t) × P ----\n    forvalues t = 2/`=`T'+1' {\n        replace healthy = healthy[`t'-1]*(1-`p_hs`s''-`p_hd`s'') in `t'\n        replace sick    = healthy[`t'-1]*`p_hs`s'' + sick[`t'-1]*(1-`p_sd`s'') in `t'\n        replace dead    = dead[`t'-1] + healthy[`t'-1]*`p_hd`s'' + sick[`t'-1]*`p_sd`s'' in `t'\n    }\n    * ---- 每周期成本与 QALY（期初状态） ----\n    gen cost_cyc = healthy*`c_well`s'' + sick*`c_sick`s''\n    gen qaly_cyc = healthy*`u_well`s'' + sick*`u_sick`s''\n    * ---- 贴现 5%：半周期校正权重 1/(1+d)^(n-0.5)，只累加第 1..T 周期 ----\n    gen w05 = 1/(1+0.05)^(_n-0.5) if _n <= `T'\n    gen cost05 = cost_cyc*w05\n    gen qaly05 = qaly_cyc*w05\n    qui sum cost05\n    local c05_`s' = r(sum)\n    qui sum qaly05\n    local q05_`s' = r(sum)\n    * ---- 贴现 3%（敏感性情景） ----\n    gen w03 = 1/(1+0.03)^(_n-0.5) if _n <= `T'\n    gen cost03 = cost_cyc*w03\n    gen qaly03 = qaly_cyc*w03\n    qui sum cost03\n    local c03_`s' = r(sum)\n    qui sum qaly03\n    local q03_`s' = r(sum)\n    * ---- 30 年死亡比例（行 T+1 的 dead） ----\n    local d30_`s' = dead[`=`T'+1']\n    matrix R = R \\ (`c05_`s'', `q05_`s'', `c03_`s'', `q03_`s'')\n}\nmatrix rownames R = A B\nmatrix colnames R = cost5 qaly5 cost3 qaly3\nmatrix list R, format(%12.2f)\ndisplay \"30 年死亡比例：A = \" %6.4f `d30_1' \"；B = \" %6.4f `d30_2'\n* 期望输出：A: cost5≈63186.10, qaly5≈10.7716；B: cost5≈63913.00, qaly5≈11.1113\n*           A: cost3≈80062.67, qaly3≈13.1935；B: cost3≈80588.47, qaly3≈13.6758\n*           30 年死亡比例：A≈0.4894，B≈0.4314","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":229}},{"id":"code-library:markov-model-skill-13","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"5.2 R 从头实现（完整代码）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-13","title":"5.2 R 从头实现（完整代码）","language":"r","code":"# ============ 第 5 步：队列模拟（R 从头写） ============\nT <- 30\np_hsA <- 1 - exp(-0.02)          # 无并发症→卒中（对照）\np_hsB <- 1 - exp(-0.02*0.70)     # 无并发症→卒中（干预）\np_hd  <- 0.010                   # 背景死亡\np_sd  <- 1 - exp(-0.10)          # 卒中后死亡\nPA <- matrix(c(1-p_hsA-p_hd, p_hsA, p_hd,\n               0,           1-p_sd, p_sd,\n               0,           0,      1), nrow = 3, byrow = TRUE)\nPB <- matrix(c(1-p_hsB-p_hd, p_hsB, p_hd,\n               0,           1-p_sd, p_sd,\n               0,           0,      1), nrow = 3, byrow = TRUE)\n\n# 队列模拟：trace(t+1) = trace(t) %*% P；奖励按周期期初状态、权重 1/(1+d)^(t-0.5)\nsim <- function(P, c_well, c_sick, u_well, u_sick, dr, T = 30) {\n  trace <- matrix(0, nrow = T + 1, ncol = 3)\n  trace[1, ] <- c(1, 0, 0)                      # 起始：100% 无并发症\n  for (t in seq_len(T)) trace[t + 1, ] <- trace[t, ] %*% P\n  w   <- 1 / (1 + dr)^(seq_len(T) - 0.5)        # 半周期校正 + 贴现权重\n  cyc <- trace[seq_len(T), ]                    # 第 1..T 周期的期初分布\n  cost <- sum((cyc[, 1] * c_well + cyc[, 2] * c_sick) * w)\n  qaly <- sum((cyc[, 1] * u_well + cyc[, 2] * u_sick) * w)\n  c(cost = cost, qaly = qaly, p_dead = trace[T + 1, 3])\n}\n\nresA5 <- sim(PA, 3000, 20000, 0.85, 0.62, 0.05, T)\nresB5 <- sim(PB, 3500, 20000, 0.85, 0.62, 0.05, T)\nresA3 <- sim(PA, 3000, 20000, 0.85, 0.62, 0.03, T)\nresB3 <- sim(PB, 3500, 20000, 0.85, 0.62, 0.03, T)\nround(rbind(\"A(5%)\" = resA5, \"B(5%)\" = resB5,\n            \"A(3%)\" = resA3, \"B(3%)\" = resB3), 2)\n# 期望输出（与 5.1 的 Stata 结果一致）：\n#   A(5%): cost=63186.10, qaly=10.77, p_dead=0.4894\n#   B(5%): cost=63913.00, qaly=11.11, p_dead=0.4314\n#   A(3%): cost=80062.67, qaly=13.19；B(3%): cost=80588.47, qaly=13.68","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":301}},{"id":"code-library:markov-model-skill-14","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"5.3 R heemod 等价实现","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-14","title":"5.3 R heemod 等价实现","language":"r","code":"# ============ 第 5 步（heemod 等价实现，heemod ≥ 1.0 API） ============\nlibrary(heemod)\n\np_hsA <- 1 - exp(-0.02); p_hsB <- 1 - exp(-0.02*0.70)\np_hd  <- 0.010; p_sd <- 1 - exp(-0.10)\n\n# 转移矩阵：C = 行内其余概率的补数（1 - 其他项之和），行和自动为 1\nmatA <- define_transition(\n  C, p_hsA, p_hd,\n  0, C,     p_sd,\n  0, 0,     C)\nmatB <- define_transition(\n  C, p_hsB, p_hd,\n  0, C,     p_sd,\n  0, 0,     C)\n\n# 状态值：discount() 在模型内按周期贴现；dispatch_strategy 区分策略成本\n# （策略名 std / intv 须与 run_model 中的名字一致）\nstate_well <- define_state(\n  cost    = discount(dispatch_strategy(std = 3000, intv = 3500), .05),\n  utility = 0.85)\nstate_sick <- define_state(\n  cost    = discount(20000, .05),\n  utility = 0.62)\nstate_dead <- define_state(\n  cost    = 0,\n  utility = 0)\n\nstratA <- define_strategy(transition = matA,\n                          state_well, state_sick, state_dead)\nstratB <- define_strategy(transition = matB,\n                          state_well, state_sick, state_dead)\n\nres <- run_model(\n  std  = stratA,\n  intv = stratB,\n  cycles = 30,\n  init   = c(1, 0, 0),      # 按 1 人队列（默认 1000 人）\n  cost   = cost,            # 成本取状态值 cost\n  effect = utility,         # 效果取状态值 utility（QALY）\n  method = \"life-table\")    # 半周期校正；默认 \"beginning\" 不做半周期校正\n\nsummary(res, threshold = c(100000, 300000))  # 阈值：1–3 倍人均 GDP（元/QALY）\nget_counts(res)                              # 各周期各状态人数\nplot(res, type = \"counts\", panels = \"by_strategy\")   # 状态分布图\nplot(res, type = \"ce\")                       # 成本-效果平面与效率前沿\n# 注：heemod 的贴现时点约定（discount() 默认首周期不打折）与 5.1/5.2 的\n# (t-0.5) 约定略有差异，结果会有小幅出入；报告时统一约定并注明实现。\n# heemod 1.0 起 run_model 已无 discount 参数，贴现写在 define_state 的\n# discount() 中（旧版 API 见 help(run_model)）。","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":341}},{"id":"code-library:markov-model-skill-15","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 6 步：计算 ICER","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-15","title":"第 6 步：计算 ICER","language":"stata","code":"* ---------- ICER 与 NMB（5% 贴现；R 第 1、2 列） ----------\ndisplay \"策略 A：成本 = \" %9.2f R[1,1] \" 元；QALY = \" %6.4f R[1,2]\ndisplay \"策略 B：成本 = \" %9.2f R[2,1] \" 元；QALY = \" %6.4f R[2,2]\ndisplay \"ΔC = \" %9.2f (R[2,1]-R[1,1]) \" 元；ΔE = \" %6.4f (R[2,2]-R[1,2]) \" QALY\"\ndisplay \"ICER(5%) = \" %9.2f ((R[2,1]-R[1,1])/(R[2,2]-R[1,2])) \" 元/QALY\"\ndisplay \"ICER(3%) = \" %9.2f ((R[2,3]-R[1,3])/(R[2,4]-R[1,4])) \" 元/QALY\"\n* NMB（λ = 100000，1 倍人均 GDP）\nlocal lam = 100000\ndisplay \"NMB_B - NMB_A (λ=100000) = \" %9.2f (`lam'*(R[2,2]-R[1,2]) - (R[2,1]-R[1,1]))\n* 期望输出：ΔC≈726.90，ΔE≈0.3397，ICER(5%)≈2139.80，ICER(3%)≈1090.04\n*           NMB 差 ≈ 33243（λ=100000）","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":411}},{"id":"code-library:markov-model-skill-16","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 6 步：计算 ICER","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-16","title":"第 6 步：计算 ICER","language":"r","code":"# ---------- ICER 与 NMB ----------\ndC   <- resB5[\"cost\"] - resA5[\"cost\"]\ndE   <- resB5[\"qaly\"] - resA5[\"qaly\"]\nc(dC = dC, dE = dE, ICER = dC / dE)   # 期望：dC≈726.90, dE≈0.3397, ICER≈2139.80\n\nlam <- 100000                          # 阈值（1 倍人均 GDP）\nNMB_A <- lam * resA5[\"qaly\"] - resA5[\"cost\"]\nNMB_B <- lam * resB5[\"qaly\"] - resB5[\"cost\"]\nc(NMB_A = NMB_A, NMB_B = NMB_B, dNMB = NMB_B - NMB_A)   # dNMB≈33243\n\n# 象限与决策规则\nif (dE > 0 & dC < 0) {\n  \"东南象限：占优（更便宜且更有效）→ 采纳\"\n} else if (dE < 0 & dC > 0) {\n  \"西北象限：被占优（更贵且更差）→ 拒绝\"\n} else if (dE > 0) {\n  ifelse(dC/dE < lam, \"东北象限：ICER < λ → 采纳\", \"东北象限：ICER ≥ λ → 拒绝\")\n} else {\n  ifelse(dC/dE > lam,\n         \"西南象限：ICER > λ（节省的钱高于损失 QALY 的价值）→ 可采纳\",\n         \"西南象限：ICER ≤ λ → 拒绝\")\n}\n# 本例期望输出：dC>0 且 dE>0 → 东北象限，ICER≈2140 < 100000 → 采纳","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":425}},{"id":"code-library:markov-model-skill-17","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"7.1 单因素敏感性分析（tornado）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-17","title":"7.1 单因素敏感性分析（tornado）","language":"stata","code":"* ============ 第 7.1 步：tornado（Stata/Mata） ============\n* 把第 5 步的循环封装成 Mata 函数（敏感性分析需反复调用，Mata 更快）\ncapture mata mata drop markov_ce()\nmata:\nreal colvector markov_ce(real scalar phs, real scalar phd, real scalar psd,\n                         real scalar cw, real scalar cs,\n                         real scalar uw, real scalar us,\n                         real scalar d, real scalar T)\n{\n    real matrix  P, trace, cyc\n    real colvector w, c, q\n    P = ((1-phs-phd, phs, phd) \\ (0, 1-psd, psd) \\ (0, 0, 1))\n    trace = J(T+1, 3, 0)\n    trace[1, 1] = 1\n    for (t = 1; t <= T; t++) trace[t+1, .] = trace[t, .] * P\n    w   = 1 :/ (1+d) :^ ((1::T) :- 0.5)\n    cyc = trace[1..T, .]\n    c   = colsum((cyc[,1]*cw + cyc[,2]*cs) :* w)\n    q   = colsum((cyc[,1]*uw + cyc[,2]*us) :* w)\n    return((c \\ q))\n}\nend\n\nmata:\nphsA = 1-exp(-0.02)\nphsB = 1-exp(-0.014)\nphd  = 0.010\npsd  = 1-exp(-0.10)\nbA = (phsA, phd, psd, 3000, 20000, 0.85, 0.62)    // 对照臂基线（1×7）\nbB = (phsB, phd, psd, 3500, 20000, 0.85, 0.62)    // 干预臂基线\nnm = (\"对照卒中率\", \"干预卒中率\", \"背景死亡\", \"卒中后死亡\",\n      \"无并发症成本\", \"卒中后成本\", \"效用-无并发症\", \"效用-卒中后\")\npair = J(0, 3, .)                                  // 参数序号、低值 ICER、高值 ICER\nfor (i = 1; i <= 8; i++) {\n    loA = bA; loB = bB; hiA = bA; hiB = bB\n    if (i == 1) {\n        loA[1] = bA[1]*0.8; hiA[1] = bA[1]*1.2      // 仅对照臂变\n    }\n    else if (i == 2) {\n        loB[1] = bB[1]*0.8; hiB[1] = bB[1]*1.2      // 仅干预臂变\n    }\n    else {\n        j = i - 1                                    // 共用参数：两臂同变\n        loA[j] = bA[j]*0.8; loB[j] = bB[j]*0.8\n        hiA[j] = bA[j]*1.2; hiB[j] = bB[j]*1.2\n    }\n    ra = markov_ce(loA[1], loA[2], loA[3], loA[4], loA[5], loA[6], loA[7], 0.05, 30)\n    rb = markov_ce(loB[1], loB[2], loB[3], loB[4], loB[5], loB[6], loB[7], 0.05, 30)\n    icer_lo = (rb[1]-ra[1]) / (rb[2]-ra[2])\n    ra = markov_ce(hiA[1], hiA[2], hiA[3], hiA[4], hiA[5], hiA[6], hiA[7], 0.05, 30)\n    rb = markov_ce(hiB[1], hiB[2], hiB[3], hiB[4], hiB[5], hiB[6], hiB[7], 0.05, 30)\n    icer_hi = (rb[1]-ra[1]) / (rb[2]-ra[2])\n    pair = pair \\ (i, icer_lo, icer_hi)\n}\nra = markov_ce(bA[1], bA[2], bA[3], bA[4], bA[5], bA[6], bA[7], 0.05, 30)\nrb = markov_ce(bB[1], bB[2], bB[3], bB[4], bB[5], bB[6], bB[7], 0.05, 30)\nicer0 = (rb[1]-ra[1]) / (rb[2]-ra[2])\n(nm, pair[,2], pair[,3])                           // 打印结果表\nst_matrix(\"TOR\", (pair[,2], pair[,3]))\nst_numscalar(\"r_icer0\", icer0)\nend\n* 期望输出（基例 ICER ≈ 2140）：对照卒中率变化最大（ICER 约 -4734 ~ 37574）；\n* 负值表示该情景下干预占优或落入其他象限，绘图与报告需注明。\n\n* ---- tornado 图（横条 + 基线竖线；y 轴序号对应上述参数顺序） ----\nsvmat TOR\ngen y = _n\ntwoway (rcap TOR1 TOR2 y, horizontal lcolor(navy)) ///\n    (scatter y TOR1) (scatter y TOR2), ///\n    xline(`=scalar(r_icer0)', lpattern(dash) lcolor(red)) ///\n    ylabel(1(1)8) xtitle(\"ICER（元/QALY）\") ytitle(\"参数序号\") ///\n    legend(off) name(tornado, replace)","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":462}},{"id":"code-library:markov-model-skill-18","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"7.1 单因素敏感性分析（tornado）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-18","title":"7.1 单因素敏感性分析（tornado）","language":"r","code":"# ============ 第 7.1 步：tornado（R） ============\nphsA <- 1-exp(-0.02); phsB <- 1-exp(-0.014)\nphd <- 0.010; psd <- 1-exp(-0.10)\nbA <- c(phsA, phd, psd, 3000, 20000, 0.85, 0.62)   # 对照臂基线\nbB <- c(phsB, phd, psd, 3500, 20000, 0.85, 0.62)   # 干预臂基线\nnms <- c(\"对照卒中率\", \"干预卒中率\", \"背景死亡\", \"卒中后死亡\",\n         \"无并发症成本\", \"卒中后成本\", \"效用-无并发症\", \"效用-卒中后\")\n\nmakeP <- function(p) matrix(c(1-p[1]-p[2], p[1], p[2],\n                              0, 1-p[3], p[3],\n                              0, 0, 1), nrow = 3, byrow = TRUE)\npair_icer <- function(pA, pB) {                    # 复用第 5.2 步的 sim()\n  rA <- sim(makeP(pA), pA[4], pA[5], pA[6], pA[7], 0.05, 30)\n  rB <- sim(makeP(pB), pB[4], pB[5], pB[6], pB[7], 0.05, 30)\n  (rB[\"cost\"] - rA[\"cost\"]) / (rB[\"qaly\"] - rA[\"qaly\"])\n}\nicer0 <- pair_icer(bA, bB)                         # ≈ 2139.80\n\ntor <- t(sapply(seq_along(nms), function(i) {\n  loA <- bA; loB <- bB; hiA <- bA; hiB <- bB\n  if (i == 1)      { loA[1] <- bA[1]*.8; hiA[1] <- bA[1]*1.2 }\n  else if (i == 2) { loB[1] <- bB[1]*.8; hiB[1] <- bB[1]*1.2 }\n  else {\n    j <- i - 1\n    loA[j] <- bA[j]*.8; loB[j] <- bB[j]*.8\n    hiA[j] <- bA[j]*1.2; hiB[j] <- bB[j]*1.2\n  }\n  c(lo = pair_icer(loA, loB), hi = pair_icer(hiA, hiB))\n}))\ntor_df <- data.frame(param = nms, lo = tor[, \"lo\"], hi = tor[, \"hi\"])\ntor_df\n\n# 基础 R tornado 图（横条 + 基线竖线）\ndotchart(tor_df$lo, labels = tor_df$param,\n         xlim = range(c(tor_df$lo, tor_df$hi, icer0)))\nsegments(tor_df$lo, seq_along(nms), tor_df$hi, seq_along(nms))\nabline(v = icer0, lty = 2, col = \"red\")\n# 负 ICER：表示该情景下落入了占优 / 西南象限，见第 6 步象限规则，勿直接比较大小","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":537}},{"id":"code-library:markov-model-skill-19","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"7.2 概率敏感性分析（PSA）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-19","title":"7.2 概率敏感性分析（PSA）","language":"stata","code":"* ============ 第 7.2 步：PSA（Stata：rbeta/rgamma/rnormal 循环） ============\n* 分布依据：概率→beta；率/RR→lognormal（exp(rnormal)）；成本→gamma；效用→beta\n* 注：1000 次 × 30 周期的 .do 循环较慢（数分钟）；可改调第 7.1 步的\n* Mata markov_ce() 提速（逻辑完全相同）。\nset seed 2026\nlocal N = 1000\nlocal T = 30\nmatrix PSA = J(0, 4, .)                    // 每行：cA qA cB qB\nforvalues i = 1/`N' {\n    * ---- 参数抽样 ----\n    local p_hsA = 1 - exp(-exp(rnormal(ln(0.02), 0.20)))\n    local p_hsB = 1 - exp(-exp(rnormal(ln(0.02), 0.20))*exp(rnormal(ln(0.70), 0.10)))\n    local p_hd  = rbeta(24.74, 2449.26)\n    local p_sd  = 1 - exp(-exp(rnormal(ln(0.10), 0.15)))\n    local cA0   = rgamma(25, 120)\n    local cB0   = `cA0' + rgamma(25, 20)\n    local cS    = rgamma(25, 800)\n    local uW    = rbeta(119.6, 21.1)\n    local uS    = rbeta(57.8, 35.4)\n    * ---- 对照臂队列模拟（同第 5.1 步循环） ----\n    clear\n    set obs `=`T'+1'\n    gen healthy = .\n    gen sick = .\n    gen dead = .\n    replace healthy = 1 in 1\n    replace sick = 0 in 1\n    replace dead = 0 in 1\n    forvalues t = 2/`=`T'+1' {\n        replace healthy = healthy[`t'-1]*(1-`p_hsA'-`p_hd') in `t'\n        replace sick    = healthy[`t'-1]*`p_hsA' + sick[`t'-1]*(1-`p_sd') in `t'\n        replace dead    = dead[`t'-1] + healthy[`t'-1]*`p_hd' + sick[`t'-1]*`p_sd' in `t'\n    }\n    gen w = 1/(1+0.05)^(_n-0.5) if _n <= `T'\n    gen cA_c = (healthy*`cA0' + sick*`cS')*w\n    gen qA_c = (healthy*`uW'  + sick*`uS')*w\n    qui sum cA_c\n    local cA_t = r(sum)\n    qui sum qA_c\n    local qA_t = r(sum)\n    * ---- 干预臂队列模拟 ----\n    replace healthy = 1 in 1\n    replace sick = 0 in 1\n    replace dead = 0 in 1\n    forvalues t = 2/`=`T'+1' {\n        replace healthy = healthy[`t'-1]*(1-`p_hsB'-`p_hd') in `t'\n        replace sick    = healthy[`t'-1]*`p_hsB' + sick[`t'-1]*(1-`p_sd') in `t'\n        replace dead    = dead[`t'-1] + healthy[`t'-1]*`p_hd' + sick[`t'-1]*`p_sd' in `t'\n    }\n    gen cB_c = (healthy*`cB0' + sick*`cS')*w\n    gen qB_c = (healthy*`uW'  + sick*`uS')*w\n    qui sum cB_c\n    local cB_t = r(sum)\n    qui sum qB_c\n    local qB_t = r(sum)\n    matrix PSA = PSA \\ (`cA_t', `qA_t', `cB_t', `qB_t')\n}\nmatrix colnames PSA = cA qA cB qB\n\n* ---- 成本-效果散点图 ----\nsvmat PSA, names(col)\ngen dC = cB - cA\ngen dE = qB - qA\ntwoway (scatter dC dE, msize(tiny) mcolor(gs8%35)), ///\n    yline(0, lpattern(dash)) xline(0, lpattern(dash)) ///\n    xtitle(\"增量 QALY\") ytitle(\"增量成本（元）\") ///\n    title(\"PSA 成本-效果散点图 (n=1000)\") name(ce_scatter, replace)\n\n* ---- CEAC ----\nmatrix CEAC = J(0, 2, .)\nforvalues lam = 0(20000)300000 {\n    count if dC - `lam'*dE < 0\n    local pce = r(N)/_N\n    matrix CEAC = CEAC \\ (`lam', `pce')\n}\nsvmat CEAC\nline CEAC2 CEAC1, xtitle(\"阈值 λ（元/QALY）\") ///\n    ytitle(\"P(具有成本效果)\") name(ceac, replace)\n* 期望（示意，与随机种子有关）：本示例各阈值下 P(CE) > 0.99，P(占优) ≈ 0.36","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":580}},{"id":"code-library:markov-model-skill-20","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"7.2 概率敏感性分析（PSA）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-20","title":"7.2 概率敏感性分析（PSA）","language":"r","code":"# ============ 第 7.2 步：PSA（R：基础循环 + 绘图） ============\nset.seed(2026)\nN <- 1000\npsa <- matrix(NA, nrow = N, ncol = 4,\n              dimnames = list(NULL, c(\"cA\", \"qA\", \"cB\", \"qB\")))\nfor (i in 1:N) {\n  r_hs <- rlnorm(1, meanlog = log(0.02), sdlog = 0.20)   # 卒中发生率\n  rr   <- rlnorm(1, meanlog = log(0.70), sdlog = 0.10)   # 干预 RR\n  p_hsA <- 1 - exp(-r_hs)\n  p_hsB <- 1 - exp(-r_hs * rr)\n  p_hd  <- rbeta(1, 24.74, 2449.26)                      # 背景死亡概率\n  p_sd  <- 1 - exp(-rlnorm(1, meanlog = log(0.10), sdlog = 0.15))\n  cA0   <- rgamma(1, shape = 25, scale = 120)            # 注意：scale= 必须显式\n  cB0   <- cA0 + rgamma(1, shape = 25, scale = 20)       #（默认第二参数是 rate）\n  cS    <- rgamma(1, shape = 25, scale = 800)\n  uW    <- rbeta(1, 119.6, 21.1)\n  uS    <- rbeta(1, 57.8, 35.4)\n  PAi <- matrix(c(1-p_hsA-p_hd, p_hsA, p_hd,\n                  0, 1-p_sd, p_sd,\n                  0, 0, 1), nrow = 3, byrow = TRUE)\n  PBi <- matrix(c(1-p_hsB-p_hd, p_hsB, p_hd,\n                  0, 1-p_sd, p_sd,\n                  0, 0, 1), nrow = 3, byrow = TRUE)\n  rA <- sim(PAi, cA0, cS, uW, uS, 0.05, 30)              # 复用第 5.2 步的 sim()\n  rB <- sim(PBi, cB0, cS, uW, uS, 0.05, 30)\n  psa[i, ] <- c(rA[\"cost\"], rA[\"qaly\"], rB[\"cost\"], rB[\"qaly\"])\n}\nhead(psa)\n\ndC <- psa[, \"cB\"] - psa[, \"cA\"]\ndE <- psa[, \"qB\"] - psa[, \"qA\"]\n\n# 成本-效果散点图（含象限线与阈值线）\nplot(dE, dC, xlab = \"增量 QALY\", ylab = \"增量成本（元）\",\n     main = \"PSA 成本-效果散点图 (n = 1000)\",\n     pch = 16, col = rgb(0, 0, 0, 0.25))\nabline(h = 0, lty = 2); abline(v = 0, lty = 2)\nabline(a = 0, b = 100000, col = \"red\")   # λ = 100000 元/QALY 阈值线\ntext(quantile(dE, 0.05), quantile(dC, 0.95),\n     paste0(\"P(占优) = \", round(mean(dC < 0 & dE > 0), 3)))\n\n# CEAC\nlam_grid <- seq(0, 300000, by = 5000)\nceac <- sapply(lam_grid, function(l) mean(dC - l*dE < 0))\nplot(lam_grid, ceac, type = \"l\", xlab = \"阈值 λ（元/QALY）\",\n     ylab = \"具有成本效果的概率\", main = \"CEAC\")\nabline(h = 0.5, lty = 3); abline(h = 0.95, lty = 3)\n# 期望（示意）：本示例各阈值下 P(CE) > 0.99，P(占优) ≈ 0.36；\n# heemod 集成版 PSA 见 vignette(\"e-probabilistic\", \"heemod\")（define_psa + run_psa）","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":662}},{"id":"code-library:markov-model-skill-21","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 8 步：报告与校验","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-21","title":"第 8 步：报告与校验","language":"stata","code":"mata:\n// ① 卒中率 → 0：两臂 QALY 应完全相同\nmarkov_ce(0, 0.010, 1-exp(-0.10), 3000, 20000, 0.85, 0.62, 0.05, 30)[2]\nmarkov_ce(0, 0.010, 1-exp(-0.10), 3500, 20000, 0.85, 0.62, 0.05, 30)[2]\n//   期望输出：两值相同（≈ 12.0320）\n// ② 卒中率 = 0 且死亡率 = 1：第 1 周期后全灭，仅剩第 1 周期奖励\nmarkov_ce(0, 1, 1, 3000, 20000, 0.85, 0.62, 0.05, 30)\n//   期望输出：cost ≈ 2927.70（3000×0.9759），qaly ≈ 0.8295（0.85×0.9759）\n// ③ 效用全 = 1：QALY 应等于贴现生命年（≈ 13.0549）\nmarkov_ce(1-exp(-0.02), 0.010, 1-exp(-0.10), 0, 0, 1, 1, 0.05, 30)[2]\nend","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":726}},{"id":"code-library:markov-model-skill-22","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 8 步：报告与校验","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-22","title":"第 8 步：报告与校验","language":"r","code":"# R：同样的极端值检验（复用 sim() 与 makeP()）\n# ① 卒中率 → 0：两臂应完全相同\nP0 <- makeP(c(0, phd, psd))\nr01 <- sim(P0, 3000, 20000, 0.85, 0.62, 0.05)\nr02 <- sim(P0, 3500, 20000, 0.85, 0.62, 0.05)\nround(c(qaly_A = r01[\"qaly\"], qaly_B = r02[\"qaly\"], equal = r01[\"qaly\"] == r02[\"qaly\"]), 4)\n# ② 卒中率 = 0 且死亡率 = 1：第 1 周期后全灭\nsim(makeP(c(0, 1, 1)), 3000, 20000, 0.85, 0.62, 0.05)\n# ③ 效用全 = 1：QALY = 贴现生命年\nsim(makeP(c(phsA, phd, psd)), 0, 0, 1, 1, 0.05)[\"qaly\"]","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":740}},{"id":"code-library:markov-model-skill-23","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"输出格式（分析报告模板）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"markov-model-skill-23","title":"输出格式（分析报告模板）","language":"text","code":"【模型结构】\n问题、视角（卫生体系）、人群、干预与对照、时间范围；状态列表与状态图；周期长度；起始分布；关键结构假设。\n\n【参数】\n表 2：转移概率（含来源与率→概率换算）；表 3：成本与效用（点估计、区间、PSA 分布、来源）。\n\n【基例结果】\n表 4：两策略总成本、总 QALY、ΔC、ΔE、ICER（注明贴现率 3%/5% 与半周期校正）；阈值区间（1–3 倍人均 GDP）与判定。\n\n【敏感性分析】\n表 5：tornado（每参数低/高值与对应 ICER）；图 1：PSA 散点图（1000 次，注明分布与相关性假设）；图 2：CEAC。\n\n【校验与局限】\nface validity、内部一致性（行和、极端值、交叉验证结果）；与已发表研究的差异说明；局限（结构简化、参数外推、未纳入的疾病进程）。","source":"skills/markov-model-skill.md","status":"模板；未逐项执行","line":791}},{"id":"code-library:meta-analysis-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"软件准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"meta-analysis-skill-1","title":"软件准备","language":"stata","code":"* Stata（联网安装一次；metafunnel 一般随 metan 附带，缺失时单独安装）\nssc install metan, replace\nssc install metareg, replace\nssc install metabias, replace","source":"skills/meta-analysis-skill.md","status":"模板；未逐项执行","line":25}},{"id":"code-library:meta-analysis-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"软件准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"meta-analysis-skill-2","title":"软件准备","language":"r","code":"# R\ninstall.packages(c(\"meta\", \"metafor\"))","source":"skills/meta-analysis-skill.md","status":"模板；未逐项执行","line":32}},{"id":"code-library:meta-analysis-skill-3","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 1 步：研究问题与检索（PICO + PRISMA）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"meta-analysis-skill-3","title":"第 1 步：研究问题与检索（PICO + PRISMA）","language":"text","code":"PRISMA 2020 流程示例（虚构数字，与后文 8 项研究自洽）：\n识别：数据库检索 n = 2347（PubMed 812；Embase 1030；Cochrane Central 301；CBM/CNKI/万方 204）\n      去重后 n = 1682\n筛选：标题/摘要初筛排除 n = 1548（与研究问题无关 1201；综述/评论 210；非目标人群 137）\n纳入资格：全文评估 n = 134，排除 n = 126\n      （非 RCT 21；不报告目标结局 58；重复发表 12；无法获取数据 17；其他 18）\n纳入：进入定量合成 n = 8","source":"skills/meta-analysis-skill.md","status":"模板；未逐项执行","line":55}},{"id":"code-library:meta-analysis-skill-4","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 1 步：研究问题与检索（PICO + PRISMA）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"meta-analysis-skill-4","title":"第 1 步：研究问题与检索（PICO + PRISMA）","language":"stata","code":"* Stata：示例数据录入（8 项 RCT，虚构演示数据）\n* et/nt = 试验组住院事件数/总数；ec/nc = 对照组住院事件数/总数\n* m_t/sd_t/m_c/sd_c = 两组 HbA1c 变化值(%)的均值/标准差\n* mode：1 = 个案管理+电话随访，0 = 单纯电话随访（第 5 步亚组用）\nclear\ninput str10 study year et nt ec nc m_t sd_t m_c sd_c mode\n\"Zhang\" 2018 45 150 68 150 -1.0  0.7  -0.30 0.8  1\n\"Li\"    2019 40 140 64 140 -1.1  0.75 -0.25 0.85 1\n\"Wang\"  2019 60 150 62 150 -0.4  0.8  -0.25 0.75 0\n\"Chen\"  2020 38 130 60 128 -0.9  0.7  -0.20 0.75 1\n\"Liu\"   2021 64 160 62 155 -0.35 0.85 -0.30 0.8  0\n\"Zhao\"  2022 36 100 37 100 -0.5  0.8  -0.30 0.8  0\n\"Sun\"   2023 48 130 50 130 -0.45 0.8  -0.25 0.75 0\n\"Xu\"    2024 42 135 63 132 -1.0  0.75 -0.25 0.8  1\nend\nlabel define mode_lb 1 \"个案管理+电话随访\" 0 \"单纯电话随访\"\nlabel values mode mode_lb","source":"skills/meta-analysis-skill.md","status":"模板；未逐项执行","line":65}},{"id":"code-library:meta-analysis-skill-5","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 1 步：研究问题与检索（PICO + PRISMA）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"meta-analysis-skill-5","title":"第 1 步：研究问题与检索（PICO + PRISMA）","language":"r","code":"# R：同一份示例数据\ndat <- data.frame(\n  study = c(\"Zhang\", \"Li\", \"Wang\", \"Chen\", \"Liu\", \"Zhao\", \"Sun\", \"Xu\"),\n  year  = c(2018, 2019, 2019, 2020, 2021, 2022, 2023, 2024),\n  et = c(45, 40, 60, 38, 64, 36, 48, 42),\n  nt = c(150, 140, 150, 130, 160, 100, 130, 135),\n  ec = c(68, 64, 62, 60, 62, 37, 50, 63),\n  nc = c(150, 140, 150, 128, 155, 100, 130, 132),\n  m_t  = c(-1.0, -1.1, -0.4, -0.9, -0.35, -0.5, -0.45, -1.0),\n  sd_t = c(0.7, 0.75, 0.8, 0.7, 0.85, 0.8, 0.8, 0.75),\n  m_c  = c(-0.3, -0.25, -0.25, -0.2, -0.3, -0.3, -0.25, -0.25),\n  sd_c = c(0.8, 0.85, 0.75, 0.75, 0.8, 0.8, 0.75, 0.8),\n  mode = c(1, 1, 0, 1, 0, 0, 0, 1))   # 1=个案管理+电话随访，0=单纯电话随访","source":"skills/meta-analysis-skill.md","status":"模板；未逐项执行","line":85}},{"id":"code-library:meta-analysis-skill-6","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 2 步：效应量与标准误计算","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"meta-analysis-skill-6","title":"第 2 步：效应量与标准误计算","language":"stata","code":"* ---- 手工计算（先核对，再交给批量命令）----\ngen a = et                      // 试验组事件\ngen b = nt - et                 // 试验组非事件\ngen c = ec                      // 对照组事件\ngen d = nc - ec                 // 对照组非事件\ngen lnOR   = ln((a*d)/(b*c))\ngen selnOR = sqrt(1/a + 1/b + 1/c + 1/d)\ngen OR     = exp(lnOR)\ngen loOR   = exp(lnOR - 1.96*selnOR)\ngen hiOR   = exp(lnOR + 1.96*selnOR)\ngen lnRR   = ln((a/nt)/(c/nc))\ngen selnRR = sqrt(1/a - 1/nt + 1/c - 1/nc)\n* 零单元处理：a b c d 各 +0.5 后按上式重算（或 metan ..., peto）\n\n* ---- 连续：MD 与 Hedges' g ----\ngen MD    = m_t - m_c\ngen seMD  = sqrt(sd_t^2/nt + sd_c^2/nc)\ngen sp    = sqrt(((nt-1)*sd_t^2 + (nc-1)*sd_c^2)/(nt+nc-2))\ngen J     = 1 - 3/(4*(nt+nc) - 9)\ngen g     = MD/sp*J\ngen seg   = sqrt((nt+nc)/(nt*nc) + g^2/(2*(nt+nc)))\nlist study year OR loOR hiOR MD seMD g seg\n\n* 单研究核对：esize 逐条 immediate 形式（语法以 help esize 为准）\nesize twosample 150 -1.0 0.7 150 -0.3 0.8, hedgesg","source":"skills/meta-analysis-skill.md","status":"模板；未逐项执行","line":119}},{"id":"code-library:meta-analysis-skill-7","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 2 步：效应量与标准误计算","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"meta-analysis-skill-7","title":"第 2 步：效应量与标准误计算","language":"r","code":"# R：手工计算（与 Stata 公式一致）\ndat$a <- dat$et; dat$b <- dat$nt - dat$et\ndat$c <- dat$ec; dat$d <- dat$nc - dat$ec\ndat$lnOR   <- with(dat, log((a*d)/(b*c)))\ndat$selnOR <- with(dat, sqrt(1/a + 1/b + 1/c + 1/d))\ndat$MD   <- dat$m_t - dat$m_c\ndat$seMD <- with(dat, sqrt(sd_t^2/nt + sd_c^2/nc))\nsp <- with(dat, sqrt(((nt-1)*sd_t^2 + (nc-1)*sd_c^2)/(nt+nc-2)))\nJ  <- with(dat, 1 - 3/(4*(nt+nc) - 9))\ndat$g   <- dat$MD/sp*J\ndat$seg <- with(dat, sqrt((nt+nc)/(nt*nc) + g^2/(2*(nt+nc))))\n\n# R：metafor::escalc（由原始数据直接计算，推荐；结果 yi/vi 与手工一致）\nlibrary(metafor)\ndat.or  <- escalc(measure = \"OR\",  ai = et, n1i = nt, ci = ec, n2i = nc,\n                  data = dat, slab = study)\ndat.rr  <- escalc(measure = \"RR\",  ai = et, n1i = nt, ci = ec, n2i = nc,\n                  data = dat, slab = study)\ndat.md  <- escalc(measure = \"MD\",  m1i = m_t, sd1i = sd_t, n1i = nt,\n                  m2i = m_c, sd2i = sd_c, n2i = nc, data = dat, slab = study)\ndat.smd <- escalc(measure = \"SMD\", m1i = m_t, sd1i = sd_t, n1i = nt,\n                  m2i = m_c, sd2i = sd_c, n2i = nc, data = dat, slab = study)\n# 注：escalc 的 \"SMD\" 默认即 Hedges' g（含小样本校正）；参数名以 ?escalc 为准","source":"skills/meta-analysis-skill.md","status":"模板；未逐项执行","line":147}},{"id":"code-library:meta-analysis-skill-8","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 3 步：固定效应与随机效应模型","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"meta-analysis-skill-8","title":"第 3 步：固定效应与随机效应模型","language":"stata","code":"* ---- 官方 meta 命令族（Stata 16+；fixed 为 common 的兼容旧称）----\nmeta set lnOR selnOR, studylabel(study) esize(OR)\nmeta summarize, fixed random                 // Q、I²、tau² + 合并估计\nmeta esize, fixed random(dlaird)             // 合并估计；REML 用 random(reml)\nmeta esize, random(reml)                     // 方法子选项以 help meta esize 为准\n\n* ---- SSC metan（经典）----\n* 二分类四变量顺序 = 试验组事件 试验组总数 对照组事件 对照组总数（不是四格表 a b c d！）\nmetan et nt ec nc, or fixed random label(namevar=study, yearvar=year) sortby(year)\n\n* 连续六变量 = n1 mean1 sd1 n2 mean2 sd2；md 或 hedges（选项以 help metan 为准）\nmetan nt m_t sd_t nc m_c sd_c, md fixed random label(namevar=study, yearvar=year)\nmetan nt m_t sd_t nc m_c sd_c, hedges fixed random label(namevar=study, yearvar=year)","source":"skills/meta-analysis-skill.md","status":"模板；未逐项执行","line":186}},{"id":"code-library:meta-analysis-skill-9","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 3 步：固定效应与随机效应模型","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"meta-analysis-skill-9","title":"第 3 步：固定效应与随机效应模型","language":"r","code":"# R：meta 包\nlibrary(meta)\nm.or <- metagen(TE = lnOR, seTE = selnOR, studlab = study, data = dat,\n                sm = \"OR\", fixed = TRUE, random = TRUE,\n                method.tau = \"REML\", prediction = TRUE)\nm.md <- metacont(n.e = nt, mean.e = m_t, sd.e = sd_t,\n                 n.c = nc, mean.c = m_c, sd.c = sd_c,\n                 studlab = study, data = dat, sm = \"MD\",\n                 fixed = TRUE, random = TRUE, method.tau = \"REML\")\nm.smd <- update(m.md, sm = \"SMD\")            // 默认 Hedges' g\nsummary(m.or)\n\n# 也可直接由四格表计数（metabin，参数名注意 event.e/n.e/event.c/n.c）\nm.or2 <- metabin(event.e = et, n.e = nt, event.c = ec, n.c = nc,\n                 studlab = study, data = dat, sm = \"OR\", method = \"MH\",\n                 fixed = TRUE, random = TRUE, method.tau = \"REML\")","source":"skills/meta-analysis-skill.md","status":"模板；未逐项执行","line":202}},{"id":"code-library:meta-analysis-skill-10","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 4 步：森林图","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"meta-analysis-skill-10","title":"第 4 步：森林图","language":"stata","code":"* 官方\nmeta forestplot, esrefline(1) fixed random          // OR 无效线 = 1\n* 连续结局：先 meta set g seg, studylabel(study) esize(hedgesg)，无效线 = 0\nmeta forestplot, esrefline(0) random\ngraph export forest_or.png, replace width(2000)     // 导出高清图\n\n* metan 自带森林图（lcols/rcols 控制列；favours 以 # 分隔左右文字）\nmetan et nt ec nc, or random label(namevar=study, yearvar=year) sortby(year) ///\n    forestplot(lcols(study year) rcols(OR loOR hiOR) favours(慢病管理降低住院 # 常规照护降低住院))","source":"skills/meta-analysis-skill.md","status":"模板；未逐项执行","line":229}},{"id":"code-library:meta-analysis-skill-11","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 4 步：森林图","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"meta-analysis-skill-11","title":"第 4 步：森林图","language":"r","code":"# R：meta 包\nforest(m.or, sortvar = year, leftcols = c(\"studlab\", \"year\"),\n       rightcols = c(\"effect\", \"ci\"), test.overall = TRUE)\n\n# R：metafor（atransf = exp 把对数尺度还原为 OR；refline = 1 为无效线）\nres <- rma(yi, vi, data = dat.or, method = \"REML\")\nforest(res, slab = dat.or$study, atransf = exp, refline = 1,\n       xlab = \"OR（对数尺度）\")","source":"skills/meta-analysis-skill.md","status":"模板；未逐项执行","line":241}},{"id":"code-library:meta-analysis-skill-12","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 5 步：异质性检验与亚组分析、Meta 回归","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"meta-analysis-skill-12","title":"第 5 步：异质性检验与亚组分析、Meta 回归","language":"stata","code":"* ---- 亚组（mode：1=个案管理+电话随访，0=单纯电话随访）----\n* 官方：subgroup() 森林图 + 分组合并\nmeta forestplot, subgroup(mode) addoverall\nmeta esize if mode == 1, random\nmeta esize if mode == 0, random\n* 或 SSC metan 的 by()\nmetan et nt ec nc, or random by(mode) label(namevar=study, yearvar=year)\n\n* ---- Meta 回归（协变量 year：发表年份趋势）----\nmeta regress year                     // 官方：REML + Knapp–Hartung（以 help meta regress 为准）\nmetareg lnOR year, wsse(selnOR) mm    // SSC metareg；REML 用 metareg ..., wsse(selnOR) reml\n* metareg 输出含 tau² 与 I²_res；语法细节以 help metareg 为准","source":"skills/meta-analysis-skill.md","status":"模板；未逐项执行","line":264}},{"id":"code-library:meta-analysis-skill-13","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 5 步：异质性检验与亚组分析、Meta 回归","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"meta-analysis-skill-13","title":"第 5 步：异质性检验与亚组分析、Meta 回归","language":"r","code":"# R：亚组（byvar 须在 metagen 的原始 data 中）\nupdate(m.or, byvar = mode)\nforest(update(m.or, byvar = mode))\n\n# R：meta 回归\nmetareg(m.or, ~ year)                                # meta 包（默认 REML，以 help(metareg) 为准）\nrma(yi, vi, mods = ~ year, data = dat.or, method = \"REML\")   # metafor\nrma(yi, vi, mods = ~ mode, data = dat.or, method = \"REML\")   # 二分类协变量等价于亚组","source":"skills/meta-analysis-skill.md","status":"模板；未逐项执行","line":279}},{"id":"code-library:meta-analysis-skill-14","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 6 步：发表偏倚评估","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"meta-analysis-skill-14","title":"第 6 步：发表偏倚评估","language":"stata","code":"* 官方（Stata 16+/17+/18+）\nmeta funnelplot, esrefline(1)\nconfunnel                       // 轮廓增强漏斗图（Stata 17+；选项以 help confunnel 为准）\nmeta bias, egger begg harbord   // Stata 18+；以 help meta bias 为准\n\n* SSC（metafunnel 一般随 metan 附带；以 ssc describe metan 为准）\nmetafunnel _ES _seES\nmetabias lnOR selnOR, egger     // Egger：效应量与 SE 两变量\nmetabias lnOR selnOR, begg      // Begg 秩相关（选项名以 help metabias 为准）\nmetabias a b c d, harbord       // Harbord：四格表单元 a b c d","source":"skills/meta-analysis-skill.md","status":"模板；未逐项执行","line":304}},{"id":"code-library:meta-analysis-skill-15","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 6 步：发表偏倚评估","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"meta-analysis-skill-15","title":"第 6 步：发表偏倚评估","language":"r","code":"# R：meta 包\nfunnel(m.or)\nmetabias(m.or, method.bias = \"linreg\")   // Egger（加权线性回归）\nmetabias(m.or, method.bias = \"rank\")     // Begg 秩相关\ntf <- trimfill(m.or); summary(tf); funnel(tf)\n\n# R：metafor\nfunnel(res)\nregtest(res, model = \"rma\")              // Egger（meta 回归法）\ntrimfill(res)                            // 输出 k0 与调整后估计","source":"skills/meta-analysis-skill.md","status":"模板；未逐项执行","line":317}},{"id":"code-library:meta-analysis-skill-16","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 7 步：报告清单与证据分级（PRISMA 2020 + GRADE）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"meta-analysis-skill-16","title":"第 7 步：报告清单与证据分级（PRISMA 2020 + GRADE）","language":"stata","code":"* 汇总表所需数字（填入报告模板）\nmeta summarize, fixed random\nmeta forestplot, fixed random            // 报告用图；graph export 导出高清格式","source":"skills/meta-analysis-skill.md","status":"模板；未逐项执行","line":342}},{"id":"code-library:meta-analysis-skill-17","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"第 7 步：报告清单与证据分级（PRISMA 2020 + GRADE）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"meta-analysis-skill-17","title":"第 7 步：报告清单与证据分级（PRISMA 2020 + GRADE）","language":"r","code":"# R：一键提取汇总表关键数字（meta 包）\nwith(m.or, c(Q = Q, df.Q = df.Q, p.Q = pval.Q, I2_pct = I2*100,\n             tau2 = tau2, tau = tau, OR_rand = exp(TE.random),\n             lo_rand = exp(lower.random), hi_rand = exp(upper.random),\n             p_rand = pval.random))\nm.or$w.random                            // 各研究随机效应权重（%）","source":"skills/meta-analysis-skill.md","status":"模板；未逐项执行","line":348}},{"id":"code-library:meta-analysis-skill-18","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"输出格式（Meta 分析报告模板）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"meta-analysis-skill-18","title":"输出格式（Meta 分析报告模板）","language":"text","code":"【Meta 分析报告模板】\n\n一、背景与问题：PICO 一句话 + 注册号（如 PROSPERO CRD…）\n二、方法：数据库与检索式（附录）；纳入/排除标准；筛选流程（PRISMA 2020 流程图）；\n     偏倚风险工具（RoB 2 / ROBINS-I）；效应量定义（OR/RR/MD/SMD）；\n     合成模型（固定/随机 + τ² 估计方法 DL/REML）；异质性、亚组/敏感性、发表偏倚计划\n三、结果：\n   表 1 纳入研究特征（作者、年份、国家、设计、样本量、干预/对照、结局定义、随访时长）\n   表 2 每项研究效应量（95% CI）与权重（固定/随机）\n   表 3 汇总（示例输出，以实际运行结果为准）：\n       结局        研究数  合并效应量(95% CI)   模型        Q(df), p      I²    τ²\n       住院(OR)    8       0.69 (0.53–0.88)     随机(REML)  14.6(7), 0.04  52%   0.07\n       HbA1c(MD)   8       …                   随机(REML)  …              …     …\n   图 1/图 2 森林图（标注方向、模型与权重）\n   表 4 亚组 / meta 回归（如有）：分组效应量、组间差异 p、回归系数(95% CI)、I²_res\n   图 3 漏斗图（可加轮廓增强）+ Egger/Begg p 值 + trim-and-fill 调整估计\n四、讨论：异质性来源、临床/政策含义、局限（检索语言、灰色文献、研究数少、混杂）\n五、GRADE：每结局确定性（高/中/低/极低）与逐条理由。\n   示例（住院结局）：8 项 RCT 起点「高」；\n   - 偏倚风险 −1（多数未报告分配隐藏/盲法）；\n   - 不一致性 −1（I² ≈ 52%，方向一致但幅度差异大）；\n   → 确定性「低」。\n六、利益冲突与资金来源","source":"skills/meta-analysis-skill.md","status":"模板；未逐项执行","line":378}},{"id":"code-library:sem-qca-qualitative-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"软件准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"sem-qca-qualitative-skill-1","title":"软件准备","language":"stata","code":"* Stata（联网安装一次）\nssc install fuzzy, replace","source":"skills/sem-qca-qualitative-skill.md","status":"模板；未逐项执行","line":25}},{"id":"code-library:sem-qca-qualitative-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"软件准备","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"sem-qca-qualitative-skill-2","title":"软件准备","language":"r","code":"# R\ninstall.packages(c(\"lavaan\", \"QCA\", \"quanteda\"))","source":"skills/sem-qca-qualitative-skill.md","status":"模板；未逐项执行","line":30}},{"id":"code-library:sem-qca-qualitative-skill-3","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"专题 1：扎根理论（Grounded Theory）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"sem-qca-qualitative-skill-3","title":"专题 1：扎根理论（Grounded Theory）","language":"r","code":"library(quanteda)\n# 示例访谈（虚构片段，演示用）\ninterviews <- data.frame(\n  id = 1:6,\n  text = c(\n    \"医保报销比例提高以后，我们村慢病患者愿意按时复诊了，负担轻了很多。\",\n    \"村卫生室药品不全，想开的药经常没有，只能去镇上，路远还要排队。\",\n    \"签约的家庭医生上门测血压，老人觉得被重视，依从性好多了。\",\n    \"镇卫生院设备老化，有些检查做不了，来回县里要一整天。\",\n    \"报销手续以前要跑好几趟，现在村卫生室直接结算，方便多了。\",\n    \"宣传不够，很多老人根本不知道慢病门诊能报销多少。\")\n)\ncorp <- corpus(interviews, text_field = \"text\", docid_field = \"id\")\ntoks <- tokens(corp, remove_punct = TRUE)\ndfmat <- dfm(toks)\ntextstat_frequency(dfmat, n = 20)               # 高频词 → 开放编码线索\nfcm(toks, context = \"window\", window = 5) |>    # 词共现 → 主轴编码线索\n  textplot_network(min_freq = 2)","source":"skills/sem-qca-qualitative-skill.md","status":"模板；未逐项执行","line":53}},{"id":"code-library:sem-qca-qualitative-skill-4","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"专题 2：结构方程模型（SEM）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"sem-qca-qualitative-skill-4","title":"专题 2：结构方程模型（SEM）","language":"r","code":"library(lavaan)\nset.seed(2026)\nn <- 300\nHL <- rnorm(n); SE <- 0.6 * HL + rnorm(n, 0, sqrt(1 - 0.36)); AD <- 0.5 * SE + rnorm(n, 0, sqrt(1 - 0.25))\nd <- data.frame(\n  x1 = 0.7 * HL + rnorm(n, 0, 0.7), x2 = 0.8 * HL + rnorm(n, 0, 0.6), x3 = 0.75 * HL + rnorm(n, 0, 0.65),\n  y1 = 0.7 * SE + rnorm(n, 0, 0.7), y2 = 0.8 * SE + rnorm(n, 0, 0.6), y3 = 0.75 * SE + rnorm(n, 0, 0.65),\n  z1 = 0.7 * AD + rnorm(n, 0, 0.7), z2 = 0.8 * AD + rnorm(n, 0, 0.6), z3 = 0.75 * AD + rnorm(n, 0, 0.65))\n\nmodel <- '\n  HL =~ x1 + x2 + x3\n  SE =~ y1 + y2 + y3\n  AD =~ z1 + z2 + z3\n  SE ~ HL\n  AD ~ SE\n'\nfit <- sem(model, data = d)\nsummary(fit, standardized = TRUE, fit.measures = TRUE)\nfitMeasures(fit, c(\"chisq\", \"df\", \"cfi\", \"tli\", \"rmsea\", \"srmr\"))","source":"skills/sem-qca-qualitative-skill.md","status":"模板；未逐项执行","line":91}},{"id":"code-library:sem-qca-qualitative-skill-5","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"专题 2：结构方程模型（SEM）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"sem-qca-qualitative-skill-5","title":"专题 2：结构方程模型（SEM）","language":"stata","code":"* Stata：等价写法（数据需先导入）\nsem (HL -> x1 x2 x3) (SE -> y1 y2 y3) (AD -> z1 z2 z3) ///\n    (SE <- HL) (AD <- SE), method(mlmv) standardized\nestat gof, stats(all)","source":"skills/sem-qca-qualitative-skill.md","status":"模板；未逐项执行","line":113}},{"id":"code-library:sem-qca-qualitative-skill-6","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"专题 3：多层 / 多重嵌套 SEM（Multilevel SEM）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"sem-qca-qualitative-skill-6","title":"专题 3：多层 / 多重嵌套 SEM（Multilevel SEM）","language":"r","code":"library(lavaan)\nset.seed(2026)\nH <- 30; m <- 20; n <- H * m\nd <- data.frame(hospital = rep(1:H, each = m))\nd$ueff <- rnorm(H, 0, 0.5)[d$hospital]          # 医院随机效应\nd$HL <- 0.4 * d$ueff + rnorm(n)\nd$SE <- 0.5 * d$HL + 0.3 * d$ueff + rnorm(n)\nd$x1 <- 0.7 * d$HL + rnorm(n, 0, 0.7); d$x2 <- 0.8 * d$HL + rnorm(n, 0, 0.6); d$x3 <- 0.75 * d$HL + rnorm(n, 0, 0.65)\nd$y1 <- 0.7 * d$SE + rnorm(n, 0, 0.7); d$y2 <- 0.8 * d$SE + rnorm(n, 0, 0.6); d$y3 <- 0.75 * d$SE + rnorm(n, 0, 0.65)\n\nmodel2 <- '\n  level: 1\n    HLw =~ x1 + x2 + x3\n    SEw =~ y1 + y2 + y3\n    SEw ~ HLw\n  level: 2\n    HLb =~ x1 + x2 + x3\n    SEb =~ y1 + y2 + y3\n    SEb ~ HLb\n'\nfit2 <- sem(model2, data = d, cluster = \"hospital\")\nsummary(fit2)","source":"skills/sem-qca-qualitative-skill.md","status":"模板；未逐项执行","line":134}},{"id":"code-library:sem-qca-qualitative-skill-7","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"专题 3：多层 / 多重嵌套 SEM（Multilevel SEM）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"sem-qca-qualitative-skill-7","title":"专题 3：多层 / 多重嵌套 SEM（Multilevel SEM）","language":"stata","code":"* Stata：gsem 多层（医院随机截距）；语法细节以 help gsem 为准\ngsem (HL -> x1 x2 x3) (SE -> y1 y2 y3) (SE <- HL M1[hospital]), vce(cluster hospital)\nestat icc","source":"skills/sem-qca-qualitative-skill.md","status":"模板；未逐项执行","line":159}},{"id":"code-library:sem-qca-qualitative-skill-8","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"专题 4：fsQCA 模糊集定性比较分析","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"sem-qca-qualitative-skill-8","title":"专题 4：fsQCA 模糊集定性比较分析","language":"r","code":"library(QCA)\nset.seed(2026)\nn <- 30\nd <- data.frame(\n  score = rnorm(n, 70, 15),          # 成效评分\n  res   = rnorm(n, 20, 8),           # 资源投入\n  staff = rnorm(n, 10, 4),           # 人员配置\n  inc   = rnorm(n, 0.5, 0.2),        # 激励机制\n  it    = rnorm(n, 3, 1.2))          # 信息化\nd <- data.frame(\n  成效   = calibrate(d$score, c(60, 75, 90)),\n  资源   = calibrate(d$res, c(10, 20, 30)),\n  人员   = calibrate(d$staff, c(5, 10, 15)),\n  激励   = calibrate(d$inc, c(0.2, 0.5, 0.8)),\n  信息化 = calibrate(d$it, c(1, 3, 5)))\n\ntt <- truthTable(d, outcome = \"成效\", n.cut = 1, incl.cut = 0.8)\ntt\nsol <- minimize(tt, include = \"?\")   # 中间解\nsol\nXYplot(tt$tt$资源, d$成效, xlab = \"资源\", ylab = \"成效\")   # 必要条件散点（对单一条件判断必要性）","source":"skills/sem-qca-qualitative-skill.md","status":"模板；未逐项执行","line":180}},{"id":"code-library:sem-qca-qualitative-skill-9","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"专题 4：fsQCA 模糊集定性比较分析","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"sem-qca-qualitative-skill-9","title":"专题 4：fsQCA 模糊集定性比较分析","language":"stata","code":"* Stata：SSC 的 fuzzy 包做模糊集校准与操作（以 help fuzzy 为准）\nssc install fuzzy\nfzcalc zy, low(60) cross(75) high(90)","source":"skills/sem-qca-qualitative-skill.md","status":"模板；未逐项执行","line":204}},{"id":"code-library:sem-qca-qualitative-skill-10","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"输出格式（专题报告模板）","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"sem-qca-qualitative-skill-10","title":"输出格式（专题报告模板）","language":"text","code":"【研究问题与方法选择】\n为什么用这个方法（数据形态 + 研究问题的匹配说明）。\n\n【数据与变量】\n样本来源、嵌套结构（多层）、校准锚点（fsQCA）、访谈资料（扎根）。\n\n【结果】\n专题 2/3：模型图、标准化载荷与路径系数表、拟合指标表（判断标准并列）。\n专题 1：编码表 + 理论模型图 + 饱和度说明。\n专题 4：真值表 + 组态解表（一致性/覆盖率）+ 必要性检验。\n\n【讨论与局限】\n结合理论的解释；方法局限（如截面数据因果弱、锚点敏感性）。","source":"skills/sem-qca-qualitative-skill.md","status":"模板；未逐项执行","line":222}},{"id":"code-library:network-meta-analysis-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"网状 Meta 分析 · R：可复现的玩具网络","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"network-meta-analysis-skill-1","title":"网状 Meta 分析 · R：可复现的玩具网络","language":"r","source":"skills/network-meta-analysis-skill.md","line":32,"code":"library(netmeta)\nd <- data.frame(\n  study = paste0(\"trial\", 1:6),\n  t1 = c(\"education\",\"digital\",\"family\",\"digital\",\"family\",\"family\"),\n  t2 = c(\"usual\",\"usual\",\"usual\",\"education\",\"education\",\"digital\"),\n  TE = log(c(.85,.72,.76,.87,.91,1.05)),\n  seTE = c(.14,.16,.17,.19,.20,.18)\n)\nfit <- netmeta(TE, seTE, t1, t2, studlab=study, data=d,\n  sm=\"OR\", reference.group=\"usual\", common=FALSE, random=TRUE,\n  method.tau=\"REML\", small.values=\"desirable\")\nsummary(fit)\nnetgraph(fit)\nnetleague(fit)\nnetsplit(fit)\ndecomp.design(fit)\nnetrank(fit, small.values=\"good\")\nsessionInfo()","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:umbrella-review-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"meta-meta / 伞状综述 · 重叠：CCA 的可运行计算","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"umbrella-review-skill-1","title":"meta-meta / 伞状综述 · 重叠：CCA 的可运行计算","language":"r","source":"skills/umbrella-review-skill.md","line":32,"code":"M <- matrix(c(1,1,0, 1,0,1, 0,1,1, 1,1,1), nrow=4, byrow=TRUE)\nrownames(M) <- paste0(\"study\",1:4)\ncolnames(M) <- paste0(\"review\",1:3)\nM <- M[rowSums(M)>0,,drop=FALSE]\nN <- sum(M); r <- nrow(M); c <- ncol(M)\ncca <- if(c>1) (N-r)/(r*c-r) else NA_real_\ncca\npair_overlap <- crossprod(M)\npair_overlap","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:umbrella-review-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"meta-meta / 伞状综述 · 重叠：CCA 的可运行计算","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"umbrella-review-skill-2","title":"meta-meta / 伞状综述 · 重叠：CCA 的可运行计算","language":"stata","source":"skills/umbrella-review-skill.md","line":44,"code":"* 当前数据：每行一个去重后的原始研究，rev1-rev3 为0/1\negen n_reviews = rowtotal(rev1-rev3)\nkeep if n_reviews > 0\nquietly count\nscalar r = r(N)\nquietly summarize n_reviews\nscalar N = r(sum)\nscalar c = 3\ndisplay (N-r)/(r*c-r)","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:target-trial-emulation-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"目标试验模拟 TTE · R：固定12个月结局的教学估计","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"target-trial-emulation-skill-1","title":"目标试验模拟 TTE · R：固定12个月结局的教学估计","language":"r","source":"skills/target-trial-emulation-skill.md","line":44,"code":"set.seed(20261023)\nn <- 2000\nd <- data.frame(age=rnorm(n,65,8), severity=rnorm(n))\nd$a <- rbinom(n,1,plogis(-.2+.02*(d$age-65)+.5*d$severity))\nd$y <- rbinom(n,1,plogis(-1+.35*d$a+.3*d$severity+.01*(d$age-65)))\npsfit <- glm(a~age+severity,binomial(),data=d)\nd$ps <- predict(psfit,type=\"response\")\nstopifnot(all(d$ps>0 & d$ps<1))\np <- mean(d$a)\nd$w <- ifelse(d$a==1,p/d$ps,(1-p)/(1-d$ps))\nprint(quantile(d$w,c(0,.01,.5,.99,1)))\nprint(with(d, tapply(ps,a,range)))\nprint(sum(d$w)^2/sum(d$w^2)) # 总体有效样本量摘要；也应分组检查\nlibrary(survey)\ndes <- svydesign(ids=~1,weights=~w,data=d)\nfit <- svyglm(y~a,design=des,family=quasibinomial(link=\"identity\"))\ncoef(fit)[\"a\"]\nconfint(fit)[\"a\",]\n# 研究分析还需检查加权前后平衡、权重重叠，并按估计流程考虑bootstrap。","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:target-trial-emulation-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"目标试验模拟 TTE · R：固定12个月结局的教学估计","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"target-trial-emulation-skill-2","title":"目标试验模拟 TTE · R：固定12个月结局的教学估计","language":"stata","source":"skills/target-trial-emulation-skill.md","line":66,"code":"* 真实固定时点数据需先明确基线、完整随访和混杂变量\nteffects ipw (y) (a age severity, logit), ate\ntebalance summarize","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:monte-carlo-simulation-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"蒙特卡洛模拟与 PSA · R：两组均值差的完整教学实验","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"monte-carlo-simulation-skill-1","title":"蒙特卡洛模拟与 PSA · R：两组均值差的完整教学实验","language":"r","source":"skills/monte-carlo-simulation-skill.md","line":33,"code":"set.seed(20261023)\nB <- 2000; n <- 100; truth <- 0.3\none <- function(){\n  y0 <- rnorm(n,0,1); y1 <- rnorm(n,truth,1.5)\n  z <- t.test(y1,y0) # Welch；估计方向为y1-y0\n  c(est=mean(y1)-mean(y0),lo=z$conf.int[1],hi=z$conf.int[2],p=z$p.value)\n}\nans <- t(replicate(B,one()))\nerr <- ans[,\"est\"]-truth\ncovered <- ans[,\"lo\"]<=truth & ans[,\"hi\"]>=truth\ncoverage <- mean(covered)\nc(bias=mean(err),MCSE_bias=sd(ans[,\"est\"])/sqrt(B),\n  RMSE=sqrt(mean(err^2)),coverage=coverage,\n  MCSE_coverage=sqrt(coverage*(1-coverage)/B),\n  power=mean(ans[,\"p\"]<.05))\n# 将truth设为0并重新生成数据，才能估计该场景的I类错误率。","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:monte-carlo-simulation-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"蒙特卡洛模拟与 PSA · R：两组均值差的完整教学实验","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"monte-carlo-simulation-skill-2","title":"蒙特卡洛模拟与 PSA · R：两组均值差的完整教学实验","language":"stata","source":"skills/monte-carlo-simulation-skill.md","line":52,"code":"clear\nset seed 20261023\ncapture program drop mc_mean\nprogram define mc_mean, rclass\n    drop _all\n    set obs 200\n    gen byte a = _n > 100\n    gen y = .3*a + rnormal(0,cond(a==1,1.5,1))\n    quietly regress y a, vce(robust)\n    return scalar est = _b[a]\n    return scalar se = _se[a]\n    return scalar covered = abs((_b[a]-.3)/_se[a]) <= invttail(e(df_r),.025)\nend\nsimulate est=r(est) se=r(se) covered=r(covered), reps(2000): mc_mean\nsummarize est se covered\n* Stata示例用稳健回归区间，R示例用Welch区间，二者并非完全同一方法。","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:monte-carlo-simulation-skill-3","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"蒙特卡洛模拟与 PSA · PSA：用增量净收益表达决策","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"monte-carlo-simulation-skill-3","title":"蒙特卡洛模拟与 PSA · PSA：用增量净收益表达决策","language":"r","source":"skills/monte-carlo-simulation-skill.md","line":73,"code":"set.seed(20261023)\nB <- 10000\ndelta_cost <- rnorm(B,1200,300)\ndelta_qaly <- rnorm(B,.08,.025)\nlambda <- seq(0,100000,by=5000) # 教学阈值范围，不是政策推荐\nceac <- sapply(lambda,function(k) mean(k*delta_qaly-delta_cost>0))\ndata.frame(lambda,Pr_cost_effective=ceac)","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:advanced-causal-inference-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"因果推断进阶 · SDID：空间与时间一起构造参照","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"advanced-causal-inference-skill-1","title":"因果推断进阶 · SDID：空间与时间一起构造参照","language":"r","source":"skills/advanced-causal-inference-skill.md","line":25,"code":"library(synthdid)\ndata(\"california_prop99\")\nz <- panel.matrices(california_prop99)\natt <- synthdid_estimate(z$Y,z$N0,z$T0)\nplot(att)\nse <- sqrt(vcov(att,method=\"placebo\"))\nc(estimate=as.numeric(att),SE=as.numeric(se))","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:advanced-causal-inference-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"因果推断进阶 · DML：灵活处理混杂，保留目标参数","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"advanced-causal-inference-skill-2","title":"因果推断进阶 · DML：灵活处理混杂，保留目标参数","language":"r","source":"skills/advanced-causal-inference-skill.md","line":41,"code":"library(DoubleML)\nlibrary(mlr3)\nlibrary(mlr3learners)\nset.seed(20261023)\nn <- 600\nd <- data.frame(x1=rnorm(n),x2=rnorm(n))\nd$a <- .5*d$x1+rnorm(n)\nd$y <- .8*d$a+d$x1+d$x2^2+rnorm(n)\nobj <- DoubleMLData$new(d,y_col=\"y\",d_cols=\"a\",x_cols=c(\"x1\",\"x2\"))\nfit <- DoubleMLPLR$new(obj,\n  ml_l=lrn(\"regr.rpart\"),ml_m=lrn(\"regr.rpart\"),n_folds=5)\nfit$fit()\nfit$summary()","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:advanced-causal-inference-skill-3","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"因果推断进阶 · IPW/MSM与g方法：看清时间顺序","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"advanced-causal-inference-skill-3","title":"因果推断进阶 · IPW/MSM与g方法：看清时间顺序","language":"stata","source":"skills/advanced-causal-inference-skill.md","line":63,"code":"* 仅基线处理、固定时点结局的教学接口，不是纵向MSM\nteffects aipw (y age severity) (a age severity), ate\ntebalance summarize","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:policy-text-analysis-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"卫生政策文本分析 · R：对人工/模型标签做基本核验","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"policy-text-analysis-skill-1","title":"卫生政策文本分析 · R：对人工/模型标签做基本核验","language":"r","source":"skills/policy-text-analysis-skill.md","line":36,"code":"# 0/1教学标签；真实任务应保留无法判断、缺失及多标签规则\ngold <- c(1,0,1,0,1,0,1,0)\npred <- c(1,0,0,0,1,1,1,0)\ntab <- table(factor(gold,levels=0:1),factor(pred,levels=0:1))\nprecision <- tab[2,2]/sum(tab[,2])\nrecall <- tab[2,2]/sum(tab[2,])\nc(precision=precision,recall=recall,F1=2*precision*recall/(precision+recall))\n# 稀有标签、空分母、地区/年份分层及区间需在正式分析中单独处理。","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:medical-ai-evaluation-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"医疗 AI 与人工评价 · R / Stata核心接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"medical-ai-evaluation-skill-1","title":"医疗 AI 与人工评价 · R / Stata核心接口","language":"r","source":"skills/medical-ai-evaluation-skill.md","line":32,"code":"library(irr)\nratings <- data.frame(r1=c(1,1,0,0,1,0),\n                      r2=c(1,0,0,0,1,1),r3=c(1,1,0,1,1,0))\nkappam.fleiss(ratings)\n# 两分类器在相同病例上正确/错误的配对表\nmcnemar.test(matrix(c(30,4,9,13),nrow=2))\n# 连续评分ICC需明确模型、绝对一致/一致性、单个/平均评分\n# irr::icc(score_matrix, model=\"twoway\",type=\"agreement\",unit=\"single\")","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:medical-ai-evaluation-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"医疗 AI 与人工评价 · R / Stata核心接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"medical-ai-evaluation-skill-2","title":"医疗 AI 与人工评价 · R / Stata核心接口","language":"stata","source":"skills/medical-ai-evaluation-skill.md","line":43,"code":"* 两位评审、名义分类\nkap rater1 rater2\n* 该接口是两位评审的Cohen Kappa，不是Fleiss Kappa。","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:longitudinal-public-data-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"公共数据库与纵向研究 · R：长表结构核验与纵向模型接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"longitudinal-public-data-skill-1","title":"公共数据库与纵向研究 · R：长表结构核验与纵向模型接口","language":"r","source":"skills/longitudinal-public-data-skill.md","line":27,"code":"# d为已由代码本核实的长表；此处不假定任何数据库原始变量名\nstopifnot(!anyDuplicated(d[c(\"id\",\"wave\")]))\nwith(d,table(wave,is.na(cognition)))\nlibrary(lme4)\nfit <- lmer(cognition ~ time*baseline_exposure+age0+sex+(1+time|id),data=d)\nsummary(fit)\n# 这是条件均值关联模型；baseline_exposure需先定义，不能直接称因果效应。","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:longitudinal-public-data-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"公共数据库与纵向研究 · R：长表结构核验与纵向模型接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"longitudinal-public-data-skill-2","title":"公共数据库与纵向研究 · R：长表结构核验与纵向模型接口","language":"stata","source":"skills/longitudinal-public-data-skill.md","line":37,"code":"isid id wave\nxtset id wave\nmixed cognition c.time##i.baseline_exposure age0 i.sex || id: time, covariance(unstructured)\n* 若目标是复杂抽样总体估计，需另核对设计权重和模型的兼容性。","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:simulation-models-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"模拟模型选型 · 一个很小的R排队实验","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"simulation-models-skill-1","title":"模拟模型选型 · 一个很小的R排队实验","language":"r","source":"skills/simulation-models-skill.md","line":29,"code":"set.seed(20261023)\nn <- 1000\narrival <- cumsum(rexp(n,rate=4))\nservice <- rexp(n,rate=5)\nstart <- finish <- numeric(n)\nfor(i in seq_len(n)){\n  start[i] <- max(arrival[i],if(i==1) 0 else finish[i-1])\n  finish[i] <- start[i]+service[i]\n}\nwait <- start-arrival\nc(mean_wait=mean(wait),p95_wait=unname(quantile(wait,.95)))","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:implementation-equity-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"实施科学与健康公平 · 公平性最小指标","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"implementation-equity-skill-1","title":"实施科学与健康公平 · 公平性最小指标","language":"r","source":"skills/implementation-equity-skill.md","line":28,"code":"# 教学数据：0/1结局且均值>0；fractional rank按收入从低到高\nd <- data.frame(income=c(1,2,3,4,5),use=c(0,0,1,0,1))\nn <- nrow(d)\nd$r <- (rank(d$income,ties.method=\"average\")-.5)/n\nmu <- mean(d$use)\ncov_pop <- mean((d$use-mu)*(d$r-mean(d$r)))\nCI <- 2*cov_pop/mu\nErreygers <- 4*mu*CI # 仅适用于0/1界限；未加权教学表达\nc(CI=CI,Erreygers=Erreygers)","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:propensity-score-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"PSM、匹配与加权 · R教学样例：匹配ATT与诊断","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"propensity-score-skill-1","title":"PSM、匹配与加权 · R教学样例：匹配ATT与诊断","language":"r","source":"skills/propensity-score-skill.md","line":37,"code":"library(MatchIt)\nlibrary(cobalt)\nset.seed(20261023)\nn <- 1000\nd <- data.frame(age=rnorm(n,65,8),severity=rnorm(n))\nd$a <- rbinom(n,1,plogis(-.3+.03*(d$age-65)+.5*d$severity))\nd$y <- 100+8*d$a+2*d$severity+.4*d$age+rnorm(n,0,10)\nm <- matchit(a~age+severity,data=d,method=\"nearest\",distance=\"glm\",\n  link=\"linear.logit\",estimand=\"ATT\",ratio=1,caliper=.2,std.caliper=TRUE)\nsummary(m)\nlove.plot(m,thresholds=c(m=.1))\nmd <- match.data(m)\nfit <- lm(y~a,data=md,weights=weights)\nlmtest::coeftest(fit,vcov.=sandwich::vcovCL(fit,cluster=~subclass))\n# 示例为无替换1:1匹配；复杂替换/聚类设计需重新选择推断。","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:propensity-score-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"PSM、匹配与加权 · R教学样例：匹配ATT与诊断","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"propensity-score-skill-2","title":"PSM、匹配与加权 · R教学样例：匹配ATT与诊断","language":"stata","source":"skills/propensity-score-skill.md","line":55,"code":"* ATT（Stata称ATET），a为处理，y为结局\nteffects psmatch (y) (a age severity, logit), atet nneighbor(1)\ntebalance summarize\n* 这是内置估计器示例，不与R卡钳匹配的全部细节完全等价。","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:staggered-did-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"交错 DID / 分批政策 · R：软件自带面板的C&S演示","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"staggered-did-skill-1","title":"交错 DID / 分批政策 · R：软件自带面板的C&S演示","language":"r","source":"skills/staggered-did-skill.md","line":31,"code":"library(did)\ndata(mpdta)\natt <- att_gt(yname=\"lemp\",tname=\"year\",idname=\"countyreal\",\n  gname=\"first.treat\",xformla=~1,data=mpdta,panel=TRUE,\n  control_group=\"nevertreated\",est_method=\"dr\",bstrap=TRUE,cband=TRUE)\nsummary(att)\ndyn <- aggte(att,type=\"dynamic\",min_e=-2,max_e=2)\nsummary(dyn)\nggdid(dyn)\naggte(att,type=\"group\")","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:staggered-did-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"交错 DID / 分批政策 · R：软件自带面板的C&S演示","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"staggered-did-skill-2","title":"交错 DID / 分批政策 · R：软件自带面板的C&S演示","language":"r","source":"skills/staggered-did-skill.md","line":46,"code":"library(fixest)\n# 教学接口：d中never-treated的g按sunab文档编码；不得直接复制did的0编码\n# d$g_sa <- ifelse(d$g==0, max(d$year)+100, d$g)\n# m <- feols(y ~ sunab(g_sa,year,ref.p=-1) | id+year,data=d,cluster=~id)\n# iplot(m)","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:staggered-did-skill-3","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"交错 DID / 分批政策 · R：软件自带面板的C&S演示","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"staggered-did-skill-3","title":"交错 DID / 分批政策 · R：软件自带面板的C&S演示","language":"stata","source":"skills/staggered-did-skill.md","line":56,"code":"* 第三方csdid；先核对所装版本help csdid，g=0为从未处理\n* ssc install csdid\n* ssc install drdid\ncsdid y x_pre, ivar(id) time(year) gvar(g) method(dripw) cluster(id)\nestat simple\nestat event, window(-2 2)","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:clustering-analysis-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"聚类分析与分型 · R教学样例","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"clustering-analysis-skill-1","title":"聚类分析与分型 · R教学样例","language":"r","source":"skills/clustering-analysis-skill.md","line":33,"code":"set.seed(20261023)\nX <- scale(iris[,1:4])\nkm <- kmeans(X,centers=3,nstart=50)\ntable(km$cluster)\naggregate(iris[,1:4],list(cluster=km$cluster),mean)\nlibrary(cluster)\nsil <- silhouette(km$cluster,dist(X))\nmean(sil[,\"sil_width\"])\n# iris物种标签未参与建模；本例仅示范接口，不作为患者分型依据。","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:clustering-analysis-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"聚类分析与分型 · R教学样例","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"clustering-analysis-skill-2","title":"聚类分析与分型 · R教学样例","language":"stata","source":"skills/clustering-analysis-skill.md","line":45,"code":"* 仅对选定连续特征标准化；先完成缺失与异常检查\negen z_age = std(age)\negen z_util = std(utilization)\negen z_cost = std(logcost)\nset seed 20261023\ncluster kmeans z_age z_util z_cost, k(3) name(km3)\ntabulate km3","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:econometric-modeling-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"卫生计量模型选型 · 两部模型的R接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"econometric-modeling-skill-1","title":"卫生计量模型选型 · 两部模型的R接口","language":"r","source":"skills/econometric-modeling-skill.md","line":35,"code":"# d: cost>=0，a为处理/分组，x为治疗前协变量\nd$anycost <- as.integer(d$cost>0)\npmod <- glm(anycost~a+x,family=binomial(),data=d)\ncmod <- glm(cost~a+x,family=Gamma(link=\"log\"),data=subset(d,cost>0))\nd1 <- d0 <- d; d1$a <- 1; d0$a <- 0\npred_mean <- function(nd) predict(pmod,nd,type=\"response\") *\n  predict(cmod,nd,type=\"response\")\nmean(pred_mean(d1)-pred_mean(d0))\n# 这只是标准化模型差异；因果解释另需识别假设。\n# 区间可对整个两部拟合与标准化过程适当重抽样；聚类数据按簇抽样。","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:econometric-modeling-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"卫生计量模型选型 · 两部模型的R接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"econometric-modeling-skill-2","title":"卫生计量模型选型 · 两部模型的R接口","language":"stata","source":"skills/econometric-modeling-skill.md","line":48,"code":"glm cost i.a c.x if cost>0, family(gamma) link(log) vce(robust)\nmargins, dydx(a)\npoisson visits i.a c.x, exposure(person_time) vce(robust)\nqreg cost i.a c.x, quantile(.5)","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:econometric-modeling-skill-3","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"卫生计量模型选型 · 面板FE、RE与CRE","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"econometric-modeling-skill-3","title":"卫生计量模型选型 · 面板FE、RE与CRE","language":"stata","source":"skills/econometric-modeling-skill.md","line":61,"code":"xtset id year\nxtreg y x i.year, fe vce(cluster id)\nxtreg y x i.year, re vce(cluster id)\nbysort id: egen xbar = mean(x)\nxtreg y x xbar i.year, re vce(cluster id)","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:econometric-modeling-skill-4","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"卫生计量模型选型 · IV、RDD与空间模型","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"econometric-modeling-skill-4","title":"卫生计量模型选型 · IV、RDD与空间模型","language":"stata","source":"skills/econometric-modeling-skill.md","line":75,"code":"ivregress 2sls y x (a=z), vce(robust)\nestat firststage","status":"教学模拟 / 数据接口；未在R或Stata执行，需核对依赖、数据与设计"}},{"id":"code-library:charls-database-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"CHARLS · 中国健康与养老追踪调查 · R与Stata：标准化层的最小接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"charls-database-skill-1","title":"CHARLS · 中国健康与养老追踪调查 · R与Stata：标准化层的最小接口","language":"r","source":"skills/charls-database-skill.md","line":40,"code":"# d: id=字符个人键, year=实际访谈年, y=已核对结局, x=预定协变量\nstopifnot(!anyNA(d$id), !anyDuplicated(d[c(\"id\", \"year\")]))\nd <- d[order(d$id, d$year), ]\n# 纵向条件关联；不自动等于政策因果效应\nm <- fixest::feols(y ~ x | id + year, cluster = ~id, data = d)\nsummary(m)\n# 按复杂抽样作描述应另用对应设计与权重，而非仅cluster=id。","status":"数据接口模板；未在R/Stata运行，必须先完成实际字段映射"}},{"id":"code-library:charls-database-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"CHARLS · 中国健康与养老追踪调查 · R与Stata：标准化层的最小接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"charls-database-skill-2","title":"CHARLS · 中国健康与养老追踪调查 · R与Stata：标准化层的最小接口","language":"stata","source":"skills/charls-database-skill.md","line":50,"code":"* id_s 是字符键，pid 仅作当前数据内面板编码，不输出映射。\nassert !missing(id_s, year)\nisid id_s year\negen long pid = group(id_s)\nxtset pid year\nxtreg y x i.year, fe vce(cluster pid)\n* 单波数据不运行此面板流程；先选择合适的survey描述/横断面模型。","status":"数据接口模板；未在R/Stata运行，必须先完成实际字段映射"}},{"id":"code-library:clhls-database-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"CLHLS / CLHLS-HF · 老年健康与家庭追踪 · R与Stata：标准化层的最小接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"clhls-database-skill-1","title":"CLHLS / CLHLS-HF · 老年健康与家庭追踪 · R与Stata：标准化层的最小接口","language":"r","source":"skills/clhls-database-skill.md","line":39,"code":"# d: id=字符个人键, year=实际访谈年, y=已核对结局, x=预定协变量\nstopifnot(!anyNA(d$id), !anyDuplicated(d[c(\"id\", \"year\")]))\nd <- d[order(d$id, d$year), ]\n# 纵向条件关联；不自动等于政策因果效应\nm <- fixest::feols(y ~ x | id + year, cluster = ~id, data = d)\nsummary(m)\n# 按复杂抽样作描述应另用对应设计与权重，而非仅cluster=id。","status":"数据接口模板；未在R/Stata运行，必须先完成实际字段映射"}},{"id":"code-library:clhls-database-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"CLHLS / CLHLS-HF · 老年健康与家庭追踪 · R与Stata：标准化层的最小接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"clhls-database-skill-2","title":"CLHLS / CLHLS-HF · 老年健康与家庭追踪 · R与Stata：标准化层的最小接口","language":"stata","source":"skills/clhls-database-skill.md","line":49,"code":"* id_s 是字符键，pid 仅作当前数据内面板编码，不输出映射。\nassert !missing(id_s, year)\nisid id_s year\negen long pid = group(id_s)\nxtset pid year\nxtreg y x i.year, fe vce(cluster pid)\n* 单波数据不运行此面板流程；先选择合适的survey描述/横断面模型。","status":"数据接口模板；未在R/Stata运行，必须先完成实际字段映射"}},{"id":"code-library:hrs-database-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"HRS · Health and Retirement Study · R与Stata：标准化层的最小接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"hrs-database-skill-1","title":"HRS · Health and Retirement Study · R与Stata：标准化层的最小接口","language":"r","source":"skills/hrs-database-skill.md","line":41,"code":"# d: id=字符个人键, year=实际访谈年, y=已核对结局, x=预定协变量\nstopifnot(!anyNA(d$id), !anyDuplicated(d[c(\"id\", \"year\")]))\nd <- d[order(d$id, d$year), ]\n# 纵向条件关联；不自动等于政策因果效应\nm <- fixest::feols(y ~ x | id + year, cluster = ~id, data = d)\nsummary(m)\n# 按复杂抽样作描述应另用对应设计与权重，而非仅cluster=id。","status":"数据接口模板；未在R/Stata运行，必须先完成实际字段映射"}},{"id":"code-library:hrs-database-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"HRS · Health and Retirement Study · R与Stata：标准化层的最小接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"hrs-database-skill-2","title":"HRS · Health and Retirement Study · R与Stata：标准化层的最小接口","language":"stata","source":"skills/hrs-database-skill.md","line":51,"code":"* id_s 是字符键，pid 仅作当前数据内面板编码，不输出映射。\nassert !missing(id_s, year)\nisid id_s year\negen long pid = group(id_s)\nxtset pid year\nxtreg y x i.year, fe vce(cluster pid)\n* 单波数据不运行此面板流程；先选择合适的survey描述/横断面模型。","status":"数据接口模板；未在R/Stata运行，必须先完成实际字段映射"}},{"id":"code-library:elsa-database-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"ELSA · English Longitudinal Study of Ageing · R与Stata：标准化层的最小接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"elsa-database-skill-1","title":"ELSA · English Longitudinal Study of Ageing · R与Stata：标准化层的最小接口","language":"r","source":"skills/elsa-database-skill.md","line":37,"code":"# d: id=字符个人键, year=实际访谈年, y=已核对结局, x=预定协变量\nstopifnot(!anyNA(d$id), !anyDuplicated(d[c(\"id\", \"year\")]))\nd <- d[order(d$id, d$year), ]\n# 纵向条件关联；不自动等于政策因果效应\nm <- fixest::feols(y ~ x | id + year, cluster = ~id, data = d)\nsummary(m)\n# 按复杂抽样作描述应另用对应设计与权重，而非仅cluster=id。","status":"数据接口模板；未在R/Stata运行，必须先完成实际字段映射"}},{"id":"code-library:elsa-database-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"ELSA · English Longitudinal Study of Ageing · R与Stata：标准化层的最小接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"elsa-database-skill-2","title":"ELSA · English Longitudinal Study of Ageing · R与Stata：标准化层的最小接口","language":"stata","source":"skills/elsa-database-skill.md","line":47,"code":"* id_s 是字符键，pid 仅作当前数据内面板编码，不输出映射。\nassert !missing(id_s, year)\nisid id_s year\negen long pid = group(id_s)\nxtset pid year\nxtreg y x i.year, fe vce(cluster pid)\n* 单波数据不运行此面板流程；先选择合适的survey描述/横断面模型。","status":"数据接口模板；未在R/Stata运行，必须先完成实际字段映射"}},{"id":"code-library:share-database-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"SHARE · Survey of Health, Ageing and Retirement in Europe · R与Stata：标准化层的最小接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"share-database-skill-1","title":"SHARE · Survey of Health, Ageing and Retirement in Europe · R与Stata：标准化层的最小接口","language":"r","source":"skills/share-database-skill.md","line":34,"code":"# d: id=字符个人键, year=实际访谈年, y=已核对结局, x=预定协变量\nstopifnot(!anyNA(d$id), !anyDuplicated(d[c(\"id\", \"year\")]))\nd <- d[order(d$id, d$year), ]\n# 纵向条件关联；不自动等于政策因果效应\nm <- fixest::feols(y ~ x | id + year, cluster = ~id, data = d)\nsummary(m)\n# 按复杂抽样作描述应另用对应设计与权重，而非仅cluster=id。","status":"数据接口模板；未在R/Stata运行，必须先完成实际字段映射"}},{"id":"code-library:share-database-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"SHARE · Survey of Health, Ageing and Retirement in Europe · R与Stata：标准化层的最小接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"share-database-skill-2","title":"SHARE · Survey of Health, Ageing and Retirement in Europe · R与Stata：标准化层的最小接口","language":"stata","source":"skills/share-database-skill.md","line":44,"code":"* id_s 是字符键，pid 仅作当前数据内面板编码，不输出映射。\nassert !missing(id_s, year)\nisid id_s year\negen long pid = group(id_s)\nxtset pid year\nxtreg y x i.year, fe vce(cluster pid)\n* 单波数据不运行此面板流程；先选择合适的survey描述/横断面模型。","status":"数据接口模板；未在R/Stata运行，必须先完成实际字段映射"}},{"id":"code-library:klosa-database-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"KLoSA · Korean Longitudinal Study of Aging · R与Stata：标准化层的最小接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"klosa-database-skill-1","title":"KLoSA · Korean Longitudinal Study of Aging · R与Stata：标准化层的最小接口","language":"r","source":"skills/klosa-database-skill.md","line":34,"code":"# d: id=字符个人键, year=实际访谈年, y=已核对结局, x=预定协变量\nstopifnot(!anyNA(d$id), !anyDuplicated(d[c(\"id\", \"year\")]))\nd <- d[order(d$id, d$year), ]\n# 纵向条件关联；不自动等于政策因果效应\nm <- fixest::feols(y ~ x | id + year, cluster = ~id, data = d)\nsummary(m)\n# 按复杂抽样作描述应另用对应设计与权重，而非仅cluster=id。","status":"数据接口模板；未在R/Stata运行，必须先完成实际字段映射"}},{"id":"code-library:klosa-database-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"KLoSA · Korean Longitudinal Study of Aging · R与Stata：标准化层的最小接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"klosa-database-skill-2","title":"KLoSA · Korean Longitudinal Study of Aging · R与Stata：标准化层的最小接口","language":"stata","source":"skills/klosa-database-skill.md","line":44,"code":"* id_s 是字符键，pid 仅作当前数据内面板编码，不输出映射。\nassert !missing(id_s, year)\nisid id_s year\negen long pid = group(id_s)\nxtset pid year\nxtreg y x i.year, fe vce(cluster pid)\n* 单波数据不运行此面板流程；先选择合适的survey描述/横断面模型。","status":"数据接口模板；未在R/Stata运行，必须先完成实际字段映射"}},{"id":"code-library:mhas-database-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"MHAS / ENASEM · Mexican Health and Aging Study · R与Stata：标准化层的最小接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"mhas-database-skill-1","title":"MHAS / ENASEM · Mexican Health and Aging Study · R与Stata：标准化层的最小接口","language":"r","source":"skills/mhas-database-skill.md","line":36,"code":"# d: id=字符个人键, year=实际访谈年, y=已核对结局, x=预定协变量\nstopifnot(!anyNA(d$id), !anyDuplicated(d[c(\"id\", \"year\")]))\nd <- d[order(d$id, d$year), ]\n# 纵向条件关联；不自动等于政策因果效应\nm <- fixest::feols(y ~ x | id + year, cluster = ~id, data = d)\nsummary(m)\n# 按复杂抽样作描述应另用对应设计与权重，而非仅cluster=id。","status":"数据接口模板；未在R/Stata运行，必须先完成实际字段映射"}},{"id":"code-library:mhas-database-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"MHAS / ENASEM · Mexican Health and Aging Study · R与Stata：标准化层的最小接口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"mhas-database-skill-2","title":"MHAS / ENASEM · Mexican Health and Aging Study · R与Stata：标准化层的最小接口","language":"stata","source":"skills/mhas-database-skill.md","line":46,"code":"* id_s 是字符键，pid 仅作当前数据内面板编码，不输出映射。\nassert !missing(id_s, year)\nisid id_s year\negen long pid = group(id_s)\nxtset pid year\nxtreg y x i.year, fe vce(cluster pid)\n* 单波数据不运行此面板流程；先选择合适的survey描述/横断面模型。","status":"数据接口模板；未在R/Stata运行，必须先完成实际字段映射"}},{"id":"code-library:nhanes-database-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"NHANES · 复杂抽样与健康检查实操 · R：仅示范2015—2016普通MEC成人BMI","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"nhanes-database-skill-1","title":"NHANES · 复杂抽样与健康检查实操 · R：仅示范2015—2016普通MEC成人BMI","language":"r","source":"skills/nhanes-database-skill.md","line":33,"code":"library(haven)\nlibrary(survey)\ndem <- read_xpt(\"DEMO_I.XPT\")\nbmx <- read_xpt(\"BMX_I.XPT\")\nstopifnot(!anyDuplicated(dem$SEQN), !anyDuplicated(bmx$SEQN))\nd <- merge(dem, bmx, by=\"SEQN\", all.x=TRUE, sort=FALSE)\nstopifnot(nrow(d)==nrow(dem))\n# BMI为MEC测量；此例不含实验室子样本或多周期。\nz <- subset(d, !is.na(WTMEC2YR) & WTMEC2YR>0 &\n               !is.na(SDMVPSU) & !is.na(SDMVSTRA))\ndes <- svydesign(ids=~SDMVPSU, strata=~SDMVSTRA,\n                 weights=~WTMEC2YR, nest=TRUE, data=z)\nadult <- subset(des, RIDAGEYR>=20)\nsvymean(~BMXBMI, adult, na.rm=TRUE)\nconfint(svymean(~BMXBMI, adult, na.rm=TRUE))\n# 补报未加权人数、BMI缺失率与估计可靠性；不将缺失直接当健康。","status":"数据接口模板；未在R/Stata运行，必须先完成实际字段映射"}},{"id":"code-library:nhanes-database-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"NHANES · 复杂抽样与健康检查实操 · Stata：同一周期的分析层","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"nhanes-database-skill-2","title":"NHANES · 复杂抽样与健康检查实操 · Stata：同一周期的分析层","language":"stata","source":"skills/nhanes-database-skill.md","line":54,"code":"* d 已在周期内按 SEQN 合并，字段经Doc File核对。\nisid SEQN\nsvyset SDMVPSU [pweight=WTMEC2YR], strata(SDMVSTRA)\ngen byte adult = RIDAGEYR>=20 if !missing(RIDAGEYR)\nsvy, subpop(adult): mean BMXBMI\n* 保留完整设计再subpop，不先只保留成人破坏设计方差。","status":"数据接口模板；未在R/Stata运行，必须先完成实际字段映射"}},{"id":"code-library:cross-cohort-harmonization-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"HRS家族 · 跨国协调与外部验证 · 推荐输出","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"cross-cohort-harmonization-skill-1","title":"HRS家族 · 跨国协调与外部验证 · 推荐输出","language":"r","source":"skills/cross-cohort-harmonization-skill.md","line":47,"code":"# 标准化层示意；不改变原始ID，不公开对应表。\nstopifnot(all(c(\"study\",\"id\",\"visit_date\") %in% names(d)))\nd$key <- paste(d$study, d$id, sep=\":\")\nstopifnot(!anyDuplicated(d[c(\"key\",\"visit_date\")]))\n# study内真实设计单元与权重需单独核对，key不是PSU。","status":"数据接口模板；未在R/Stata运行，必须先完成实际字段映射"}},{"id":"code-library:claims-clinical-database-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"医保结算、临床登记与MEPS · 费用和事件数据 · MEPS选型入口","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"claims-clinical-database-skill-1","title":"医保结算、临床登记与MEPS · 费用和事件数据 · MEPS选型入口","language":"r","source":"skills/claims-clinical-database-skill.md","line":43,"code":"# 已去标识的标准化示意：每次就诊费用先聚合，避免多表多行膨胀。\nstopifnot(!anyDuplicated(encounter$encounter_id))\ntot <- aggregate(amount ~ encounter_id, cost_detail, sum)\na <- merge(encounter, tot, by=\"encounter_id\", all.x=TRUE)\nstopifnot(nrow(a)==nrow(encounter))\n# 缺少明细先核查，不能不加判断地将NA替换为0。","status":"数据接口模板；未在R/Stata运行，必须先完成实际字段映射"}},{"id":"code-library:microsimulation-studio-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"个体微观模拟 · R教学模型","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"microsimulation-studio-1","title":"个体微观模拟 · R教学模型","language":"r","source":"skills/microsimulation-studio.md","line":42,"code":"# 虚构四状态教学模型，不读取任何受访者数据。\nsource(\"scripts/microsimulation_demo.R\")\ndemo <- run_demo(n = 5000, cycles = 20, reps = 8, psa_draws = 40,\n                 seed = 20261024, out = \"microsimulation-demo\")\ndemo$comparison\ndemo$mcse","status":"base R教学示例已运行，29项行为/不变量检查通过；未验证真实数据"}},{"id":"code-library:microsimulation-studio-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"个体微观模拟 · Stata模板","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"microsimulation-studio-2","title":"个体微观模拟 · Stata模板","language":"stata","source":"skills/microsimulation-studio.md","line":59,"code":"* 虚构参数；此模板尚未在Stata执行，使用前核对版本和路径。\ndo \"scripts/microsimulation_demo.do\" \"microsimulation-demo-stata\"","status":"Stata模板；尚未在Stata执行"}},{"id":"code-library:standardized-patient-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"模拟与标准化病人 SP / USP · 机构层级分析 · R","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"standardized-patient-skill-1","title":"模拟与标准化病人 SP / USP · 机构层级分析 · R","language":"r","source":"skills/standardized-patient-skill.md","line":88,"code":"# Original teaching simulation. No patient data and no clinical recommendations.\n# Balanced cases/actors at every clinic; inference is conditional on this set.\nset.seed(20261024)\nclinics <- data.frame(clinic_id=1:36, arm=sample(rep(0:1, each=18)))\nvisits <- merge(clinics, expand.grid(case_id=1:3, actor_id=1:4))\nclinic_effect <- rnorm(36, 0, 0.45)\np <- plogis(-0.55 + 0.5*visits$arm + clinic_effect[visits$clinic_id] +\n             c(-0.2,0,0.2)[visits$case_id])\nvisits$correct <- rbinom(nrow(visits), 1, p)\nvisits$detected <- rbinom(nrow(visits), 1, 0.07)\nstopifnot(nrow(visits)==432, all(table(visits$clinic_id)==12))\nclinic_scores <- aggregate(correct ~ clinic_id + arm, visits, mean)\n# Clinic allocation means visits are not independent trial units.\ncomparison <- t.test(clinic_scores$correct[clinic_scores$arm==1],\n                     clinic_scores$correct[clinic_scores$arm==0])\nrd <- unname(diff(rev(comparison$estimate)))\nstopifnot(abs(rd - (mean(clinic_scores$correct[clinic_scores$arm==1]) -\n                   mean(clinic_scores$correct[clinic_scores$arm==0]))) < 1e-12)\nprint(data.frame(estimand='Equal-clinic mean risk difference (arm 1 minus 0)',\n                 estimate=rd, lower=comparison$conf.int[1], upper=comparison$conf.int[2]))\n# Detection is reported, not silently deleted; exclusions may select on treatment.\nprint(aggregate(detected ~ arm, visits, mean))\nprint(aggregate(correct ~ arm + case_id, visits, mean))\n# For observational samples these contrasts are descriptive, not causal effects.\n# Unbalanced scripts/actors, few clusters, or cross-classified random actors need\n# a prespecified adjustment model and suitable uncertainty estimation.\nstopifnot(all(visits$correct %in% 0:1), all(is.finite(comparison$conf.int)))\ncat('PASS: 432 visits; 36 randomized clinics; balanced scripts; finite clinic-level interval\\n')","status":"R 4.6.1 已运行教学模拟与断言；未验证真实研究数据"}},{"id":"code-library:standardized-patient-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"模拟与标准化病人 SP / USP · 机构层级分析 · STATA","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"standardized-patient-skill-2","title":"模拟与标准化病人 SP / USP · 机构层级分析 · STATA","language":"stata","source":"skills/standardized-patient-skill.md","line":123,"code":"* Original teaching simulation; not run in Stata in this release.\nclear all\nset seed 20261024\nset obs 36\ngen clinic_id = _n\ngen allocation_order = runiform()\nsort allocation_order\ngen arm = (_n > 18)\ngen clinic_effect = rnormal(0, .45)\nexpand 12\nbysort clinic_id: gen visit = _n\ngen case_id = mod(visit-1,3)+1\ngen actor_id = ceil(visit/3)\ngen p = invlogit(-.55 + .5*arm + clinic_effect + .2*(case_id-2))\ngen correct = (runiform() < p)\ngen detected = (runiform() < .07)\nassert inlist(correct,0,1)\nassert _N == 432\ntabstat detected, by(arm) stat(mean n)\n* Equal clinic weighting; case and actor assignments are balanced by construction.\npreserve\ncollapse (mean) correct, by(clinic_id arm)\nassert _N == 36\nregress correct i.arm, vce(robust)\nlincom 1.arm\nrestore\n* The individual-visit model is an alternative, not an independent replication.\nregress correct i.arm i.case_id i.actor_id, vce(cluster clinic_id)\nlincom 1.arm\n* Few clusters, unbalanced case mixes and observational allocation require\n* separate design-based decisions. Do not delete detected visits without a plan.","status":"Stata教学模板；未执行"}},{"id":"code-library:implementation-science-skill-1","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"实施科学：从框架到研究设计 · 机构层级分析 · R","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"implementation-science-skill-1","title":"实施科学：从框架到研究设计 · 机构层级分析 · R","language":"r","source":"skills/implementation-science-skill.md","line":102,"code":"# Original teaching simulation: same clinical service, two implementation strategies.\nset.seed(20261025)\nd <- data.frame(site_id=1:40, strategy=sample(rep(0:1,each=20)))\nd$eligible <- sample(50:100, 40, replace=TRUE)\nd$adopted <- rbinom(40, 1, plogis(-0.1 + 0.6*d$strategy))\nd$reached <- ifelse(d$adopted==1,\n                    rbinom(40, d$eligible, plogis(-0.3 + 0.3*d$strategy)), 0)\nd$completed <- rbinom(40, d$reached, 0.8)\nsafe_ratio <- function(num,den) ifelse(den>0,num/den,NA_real_)\nd$reach <- safe_ratio(d$reached,d$eligible)\nd$fidelity <- safe_ratio(d$completed,d$reached)\nd$implementation_cost <- 500 + 15*d$reached\nsummary_by_arm <- do.call(rbind,lapply(split(d,d$strategy), function(x)\n data.frame(strategy=x$strategy[1], sites=nrow(x),\n adoption=mean(x$adopted), reach_person_weighted=sum(x$reached)/sum(x$eligible),\n reach_site_weighted=mean(x$reach), fidelity_among_reached=safe_ratio(sum(x$completed),sum(x$reached)),\n cost_per_reached=safe_ratio(sum(x$implementation_cost),sum(x$reached)))))\nprint(summary_by_arm)\n# Primary example: strategy effect on equal-site reach, at one prespecified time.\nfit <- t.test(d$reach[d$strategy==1],d$reach[d$strategy==0])\nprint(data.frame(reach_difference=unname(fit$estimate[1]-fit$estimate[2]),\n                 lower=fit$conf.int[1],upper=fit$conf.int[2]))\n# Fidelity conditions on reach. Its between-arm contrast is not automatically\n# a causal strategy effect because reaching the service is post-allocation.\nstopifnot(all(d$completed<=d$reached),all(d$reached<=d$eligible),\n          is.na(safe_ratio(0,0)),all(is.na(d$fidelity[d$reached==0])),\n          all(is.finite(fit$conf.int)))\ncat('PASS: denominators, missing zero-denominator ratios, 40 randomized sites, finite interval\\n')","status":"R 4.6.1 已运行教学模拟与断言；未验证真实研究数据"}},{"id":"code-library:implementation-science-skill-2","collection":"code-library","collection_title":"代码及上下文","kind":"代码","title":"实施科学：从框架到研究设计 · 机构层级分析 · STATA","summary":"","url":"code.html","source":"data/code-library.json","details":{"id":"implementation-science-skill-2","title":"实施科学：从框架到研究设计 · 机构层级分析 · STATA","language":"stata","source":"skills/implementation-science-skill.md","line":137,"code":"* Teaching template. Not run in Stata in this release.\nclear all\nset seed 20261025\nset obs 40\ngen site_id = _n\ngen allocation_order = runiform()\nsort allocation_order\ngen strategy = (_n > 20)\ngen eligible = 50 + floor(51*runiform())\ngen adopted = runiform() < invlogit(-.1+.6*strategy)\ngen reached = rbinomial(eligible, invlogit(-.3+.3*strategy)) if adopted==1\nreplace reached = 0 if adopted==0\ngen completed = rbinomial(reached,.8)\ngen reach = reached/eligible if eligible>0\ngen fidelity = completed/reached if reached>0\ngen implementation_cost = 500+15*reached\nassert completed<=reached & reached<=eligible\nassert missing(fidelity) if reached==0\ntabstat adopted reach fidelity, by(strategy) stat(mean n)\n* Site-weighted reach contrast. Zero reach remains in the primary analysis.\nregress reach i.strategy, vce(robust)\nlincom 1.strategy\n* Person-weighted summaries use pooled numerators AND denominators.\npreserve\ncollapse (sum) reached eligible completed implementation_cost, by(strategy)\ngen pooled_reach = reached/eligible\ngen pooled_fidelity = completed/reached if reached>0\ngen cost_per_reached = implementation_cost/reached if reached>0\nlist, noobs\nrestore\n* These are different estimands; do not equate a conditional fidelity contrast\n* with a randomized strategy effect on everyone originally eligible.","status":"Stata教学模板；未执行"}},{"id":"tools:1","collection":"tools","collection_title":"完整Skill教程","kind":"方法","title":"主动健康与健康融入万策（总纲旗舰）","summary":"把健康融入万策、带到每一个人心中：主动健康策略 + 健康影响评估（PAF/集中指数）+ 理论·数据·选题·院校五大板块，学科建设总纲。","url":"tools.html","source":"data/tools.json","details":{"name":"主动健康与健康融入万策（总纲旗舰）","category":"flagship","description":"把健康融入万策、带到每一个人心中：主动健康策略 + 健康影响评估（PAF/集中指数）+ 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