Survivor-Complier Effects in the Presence of Selection on Treatment, With Application to a Study of Prompt ICU Admission
本文提出在治疗仅对部分人群定义时（如ICU入院），估计幸存者-依从者因果效应的方法，推导出边界并进行敏感性分析，允许高维协变量调整和机器学习，应用于英国ICU入院研究。
Pretreatment selection or censoring (“selection on treatment”) can occur when two treatment levels are compared ignoring the third option of neither treatment, in “censoring by death” settings where treatment is only defined for those who survive long enough to receive it, or in general in studies where the treatment is only defined for a subset of the population. Unfortunately, the standard instrumental variable (IV) estimand is not defined in the presence of such selection, so we consider estimating a new survivor-complier causal effect. Although this effect is generally not identified under standard IV assumptions, it is possible to construct sharp bounds. We derive these bounds and give a corresponding data-driven sensitivity analysis, along with nonparametric yet efficient estimation methods. Importantly, our approach allows for high-dimensional confounding adjustment, and valid inference even after employing machine learning. Incorporating covariates can tighten bounds dramatically, especially when they are strong predictors of the selection process. We apply the methods in a UK cohort study of critical care patients to examine the mortality effects of prompt admission to the intensive care unit, using ICU bed availability as an instrument. Supplementary materials for this article are available online.