David G. Lugo-Palacios, Patrick Bidulka, Stephen O’Neill, Orlagh Carroll, Anirban Basu, Amanda Adler, Karla DíazOrdaz, Andrew Briggs, Richard Grieve
{"title":"超越随机对照试验,评估不同目标人群的治疗效果异质性。","authors":"David G. Lugo-Palacios, Patrick Bidulka, Stephen O’Neill, Orlagh Carroll, Anirban Basu, Amanda Adler, Karla DíazOrdaz, Andrew Briggs, Richard Grieve","doi":"10.1002/hec.4903","DOIUrl":null,"url":null,"abstract":"<p>Methods have been developed for transporting evidence from randomised controlled trials (RCTs) to target populations. However, these approaches allow only for differences in characteristics observed in the RCT and real-world data (overt heterogeneity). These approaches do not recognise heterogeneity of treatment effects (HTE) according to unmeasured characteristics (essential heterogeneity). We use a target trial design and apply a local instrumental variable (LIV) approach to electronic health records from the Clinical Practice Research Datalink, and examine both forms of heterogeneity in assessing the comparative effectiveness of two second-line treatments for type 2 diabetes mellitus. We first estimate individualised estimates of HTE across the entire target population defined by applying eligibility criteria from national guidelines (<i>n</i> = 13,240) within an overall target trial framework. We define a subpopulation who meet a published RCT's eligibility criteria (‘RCT-eligible’, <i>n</i> = 6497), and a subpopulation who do not (‘RCT-ineligible’, <i>n</i> = 6743). We compare average treatment effects for pre-specified subgroups within the RCT-eligible subpopulation, the RCT-ineligible subpopulation, and within the overall target population. We find differences across these subpopulations in the magnitude of subgroup-level treatment effects, but that the direction of estimated effects is stable. Our results highlight that LIV methods can provide useful evidence about treatment effect heterogeneity including for those subpopulations excluded from RCTs.</p>","PeriodicalId":12847,"journal":{"name":"Health economics","volume":"34 1","pages":"85-104"},"PeriodicalIF":2.0000,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631826/pdf/","citationCount":"0","resultStr":"{\"title\":\"Going beyond randomised controlled trials to assess treatment effect heterogeneity across target populations\",\"authors\":\"David G. Lugo-Palacios, Patrick Bidulka, Stephen O’Neill, Orlagh Carroll, Anirban Basu, Amanda Adler, Karla DíazOrdaz, Andrew Briggs, Richard Grieve\",\"doi\":\"10.1002/hec.4903\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Methods have been developed for transporting evidence from randomised controlled trials (RCTs) to target populations. However, these approaches allow only for differences in characteristics observed in the RCT and real-world data (overt heterogeneity). These approaches do not recognise heterogeneity of treatment effects (HTE) according to unmeasured characteristics (essential heterogeneity). We use a target trial design and apply a local instrumental variable (LIV) approach to electronic health records from the Clinical Practice Research Datalink, and examine both forms of heterogeneity in assessing the comparative effectiveness of two second-line treatments for type 2 diabetes mellitus. We first estimate individualised estimates of HTE across the entire target population defined by applying eligibility criteria from national guidelines (<i>n</i> = 13,240) within an overall target trial framework. We define a subpopulation who meet a published RCT's eligibility criteria (‘RCT-eligible’, <i>n</i> = 6497), and a subpopulation who do not (‘RCT-ineligible’, <i>n</i> = 6743). We compare average treatment effects for pre-specified subgroups within the RCT-eligible subpopulation, the RCT-ineligible subpopulation, and within the overall target population. We find differences across these subpopulations in the magnitude of subgroup-level treatment effects, but that the direction of estimated effects is stable. Our results highlight that LIV methods can provide useful evidence about treatment effect heterogeneity including for those subpopulations excluded from RCTs.</p>\",\"PeriodicalId\":12847,\"journal\":{\"name\":\"Health economics\",\"volume\":\"34 1\",\"pages\":\"85-104\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631826/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health economics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/hec.4903\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health economics","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/hec.4903","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
Going beyond randomised controlled trials to assess treatment effect heterogeneity across target populations
Methods have been developed for transporting evidence from randomised controlled trials (RCTs) to target populations. However, these approaches allow only for differences in characteristics observed in the RCT and real-world data (overt heterogeneity). These approaches do not recognise heterogeneity of treatment effects (HTE) according to unmeasured characteristics (essential heterogeneity). We use a target trial design and apply a local instrumental variable (LIV) approach to electronic health records from the Clinical Practice Research Datalink, and examine both forms of heterogeneity in assessing the comparative effectiveness of two second-line treatments for type 2 diabetes mellitus. We first estimate individualised estimates of HTE across the entire target population defined by applying eligibility criteria from national guidelines (n = 13,240) within an overall target trial framework. We define a subpopulation who meet a published RCT's eligibility criteria (‘RCT-eligible’, n = 6497), and a subpopulation who do not (‘RCT-ineligible’, n = 6743). We compare average treatment effects for pre-specified subgroups within the RCT-eligible subpopulation, the RCT-ineligible subpopulation, and within the overall target population. We find differences across these subpopulations in the magnitude of subgroup-level treatment effects, but that the direction of estimated effects is stable. Our results highlight that LIV methods can provide useful evidence about treatment effect heterogeneity including for those subpopulations excluded from RCTs.
期刊介绍:
This Journal publishes articles on all aspects of health economics: theoretical contributions, empirical studies and analyses of health policy from the economic perspective. Its scope includes the determinants of health and its definition and valuation, as well as the demand for and supply of health care; planning and market mechanisms; micro-economic evaluation of individual procedures and treatments; and evaluation of the performance of health care systems.
Contributions should typically be original and innovative. As a rule, the Journal does not include routine applications of cost-effectiveness analysis, discrete choice experiments and costing analyses.
Editorials are regular features, these should be concise and topical. Occasionally commissioned reviews are published and special issues bring together contributions on a single topic. Health Economics Letters facilitate rapid exchange of views on topical issues. Contributions related to problems in both developed and developing countries are welcome.