{"title":"使用gsem通过潜在类方法估计同谋者平均因果效应","authors":"Patricio Troncoso, Ana Morales-Gómez","doi":"10.1177/1536867X221106416","DOIUrl":null,"url":null,"abstract":"In randomized controlled trials, intention-to-treat analysis is customarily used to estimate the effect of the trial. However, in the presence of noncompliance, this can often lead to biased estimates because intention-to-treat analysis completely ignores varying levels of actual treatment received. This is a known issue that can be overcome by adopting the complier average causal effect approach, which estimates the effect the trial had on the individuals who complied with the protocol. When compliance is unobserved in the control group, the complier average causal effect estimate can be obtained via a latent class specification using the gsem command.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Estimating the complier average causal effect via a latent class approach using gsem\",\"authors\":\"Patricio Troncoso, Ana Morales-Gómez\",\"doi\":\"10.1177/1536867X221106416\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In randomized controlled trials, intention-to-treat analysis is customarily used to estimate the effect of the trial. However, in the presence of noncompliance, this can often lead to biased estimates because intention-to-treat analysis completely ignores varying levels of actual treatment received. This is a known issue that can be overcome by adopting the complier average causal effect approach, which estimates the effect the trial had on the individuals who complied with the protocol. When compliance is unobserved in the control group, the complier average causal effect estimate can be obtained via a latent class specification using the gsem command.\",\"PeriodicalId\":51171,\"journal\":{\"name\":\"Stata Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2022-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Stata Journal\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1177/1536867X221106416\",\"RegionNum\":2,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"SOCIAL SCIENCES, MATHEMATICAL METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Stata Journal","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1177/1536867X221106416","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SOCIAL SCIENCES, MATHEMATICAL METHODS","Score":null,"Total":0}
Estimating the complier average causal effect via a latent class approach using gsem
In randomized controlled trials, intention-to-treat analysis is customarily used to estimate the effect of the trial. However, in the presence of noncompliance, this can often lead to biased estimates because intention-to-treat analysis completely ignores varying levels of actual treatment received. This is a known issue that can be overcome by adopting the complier average causal effect approach, which estimates the effect the trial had on the individuals who complied with the protocol. When compliance is unobserved in the control group, the complier average causal effect estimate can be obtained via a latent class specification using the gsem command.
期刊介绍:
The Stata Journal is a quarterly publication containing articles about statistics, data analysis, teaching methods, and effective use of Stata''s language. The Stata Journal publishes reviewed papers together with shorter notes and comments, regular columns, book reviews, and other material of interest to researchers applying statistics in a variety of disciplines.