4.6 Article

Selection Bias When Estimating Average Treatment Effects Using One-sample Instrumental Variable Analysis

期刊

EPIDEMIOLOGY
卷 30, 期 3, 页码 350-357

出版社

LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/EDE.0000000000000972

关键词

Causal exposure effect; Collider stratification bias; Instrumental variable; Selection bias; Two-stage least squares

资金

  1. Medical Research Council (MRC) [MC\_ UU\_00011/1, MC\_UU\_00011/3]
  2. University of Bristol [MC\_ UU\_00011/1, MC\_UU\_00011/3]
  3. Economics and Social Research Council (ESRC) [ES/N000757/1]
  4. Wellcome Trust
  5. Elizabeth Blackwell Institute
  6. ESRC [ES/N000757/1] Funding Source: UKRI
  7. MRC [MC_UU_00011/3, MC_UU_00011/1] Funding Source: UKRI

向作者/读者索取更多资源

Participants in epidemiologic and genetic studies are rarely true random samples of the populations they are intended to represent, and both known and unknown factors can influence participation in a study (known as selection into a study). The circumstances in which selection causes bias in an instrumental variable (IV) analysis are not widely understood by practitioners of IV analyses. We use directed acyclic graphs (DAGs) to depict assumptions about the selection mechanism (factors affecting selection) and show how DAGs can be used to determine when a two-stage least squares IV analysis is biased by different selection mechanisms. Through simulations, we show that selection can result in a biased IV estimate with substantial confidence interval (CI) undercoverage, and the level of bias can differ between instrument strengths, a linear and nonlinear exposure-instrument association, and a causal and noncausal exposure effect. We present an application from the UK Biobank study, which is known to be a selected sample of the general population. Of interest was the causal effect of staying in school at least 1 extra year on the decision to smoke. Based on 22,138 participants, the two-stage least squares exposure estimates were very different between the IV analysis ignoring selection and the IV analysis which adjusted for selection (e.g., risk differences, 1.8% [95% CI, -1.5%, 5.0%] and -4.5% [95% CI, -6.6%, -2.4%], respectively). We conclude that selection bias can have a major effect on an IV analysis, and further research is needed on how to conduct sensitivity analyses when selection depends on unmeasured data.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据