Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death

Sihyung Park; Wenbin Lu; Shu Yang

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arXiv · 2510.07501

Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death

Sihyung Park·Wenbin Lu·Shu Yang

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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.

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BibTeX RIS

Sihyung Park, Wenbin Lu, Shu Yang. 2025-10-08. Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death. https://arxiv.org/abs/2510.07501

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