Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death

Sihyung Park, Wenbin Lu, Shu Yang

Keywords: Dynamic Treatment Regimes, Panel Data, Principal Stratification, Truncation by Death, Multiple Robustness, Nonparametric Efficiency, Personalized Medicine

TL;DR: Robust, efficient estimator for multi-stage DTRs under truncation by death using principal stratification is proposed and applied on EHR for personalized treatment.

Abstract: Truncation by death, a prevalent challenge in critical care, renders traditional dynamic treatment regime (DTR) evaluation inapplicable due to ill-defined potential outcomes. We introduce a principal stratification-based method, focusing on the always-survivor value function. We derive a semiparametrically efficient, multiply robust estimator for multi-stage DTRs, demonstrating its robustness and efficiency. Empirical validation and an application to electronic health records showcase its utility for personalized treatment optimization.

Primary Area: General machine learning (supervised, unsupervised, online, active, etc.)

Submission Number: 6517