Identification and estimation of causal effects with outcomes truncated by death

• 成果类型: Article
• 署名作者: Wang, Linbo; Zhou, Xiao-Hua; Richardson, Thomas S.
• 署名单位: Harvard University; Harvard T.H. Chan School of Public Health; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asx034
• 发表日期: 2017
• 页码: 597612
• 关键词: hiv vaccine trials; principal stratification; sensitivity-analysis; post-randomization; inference; Identifiability; noncompliance; mortality; bounds

摘要

It is common in medical studies that the outcome of interest is truncated by death, meaning that a subject has died before the outcome could be measured. In this case, restricted analysis among survivors may be subject to selection bias. Hence, it is of interest to estimate the survivor average causal effect, defined as the average causal effect among the subgroup consisting of subjects who would survive under either exposure. In this paper, we consider the identification and estimation problems of the survivor average causal effect. We propose to use a substitution variable in place of the latent membership in the always-survivor group. The identification conditions required for a substitution variable are conceptually similar to conditions for a conditional instrumental variable, and may apply to both randomized and observational studies. We show that the survivor average causal effect is identifiable with use of such a substitution variable, and propose novel model parameterizations for estimation of the survivor average causal effect under our identification assumptions. Our approaches are illustrated via simulation studies and a data analysis.