Identifying and estimating effects of sustained interventions under parallel trends assumptions

逆概率加权 协变量 估计员 观察研究 加权 混淆 反概率 心理干预 计量经济学 统计 人口 平均处理效果 医学 数学 计算机科学 贝叶斯概率 环境卫生 精神科 放射科 后验概率
作者
Audrey Renson,Michael G. Hudgens,Alexander P. Keil,Paul N Zivich,Allison E. Aiello
出处
期刊:Biometrics [Oxford University Press]
标识
DOI:10.1111/biom.13862
摘要

Many research questions in public health and medicine concern sustained interventions in populations defined by substantive priorities. Existing methods to answer such questions typically require a measured covariate set sufficient to control confounding, which can be questionable in observational studies. Differences-in-differences rely instead on the parallel trends assumption, allowing for some types of time-invariant unmeasured confounding. However, most existing difference-in-differences implementations are limited to point treatments in restricted subpopulations. We derive identification results for population effects of sustained treatments under parallel trends assumptions. In particular, in settings where all individuals begin follow-up with exposure status consistent with the treatment plan of interest but may deviate at later times, a version of Robins' g-formula identifies the intervention-specific mean under stable unit treatment value assumption, positivity, and parallel trends. We develop consistent asymptotically normal estimators based on inverse-probability weighting, outcome regression, and a double robust estimator based on targeted maximum likelihood. Simulation studies confirm theoretical results and support the use of the proposed estimators at realistic sample sizes. As an example, the methods are used to estimate the effect of a hypothetical federal stay-at-home order on all-cause mortality during the COVID-19 pandemic in spring 2020 in the United States.
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