Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
On Multiple Robustness of Proximal Dynamic Treatment Regimes
Dynamic treatment regimes are sequential decision rules that adapt treatment according to individual time-varying characteristics and outcomes to achieve optimal effects, with applications in precision medicine, personal…
Causal InferencePolicy Learning for Optimal Dynamic Treatment Regimes with Observational Data
Public policies and medical interventions often involve dynamic treatment assignments, in which individuals receive a sequence of interventions over multiple stages. We study the statistical learning of optimal dynamic t…
Robust classificationOptimal Dynamic Treatment Regimes and Partial Welfare Ordering
Dynamic treatment regimes are treatment allocations tailored to heterogeneous individuals. The optimal dynamic treatment regime is a regime that maximizes counterfactual welfare. We introduce a framework in which we can …
counterfactualStatistical Inference in Dynamic Treatment Regimes
Dynamic treatment regimes are of growing interest across the clinical sciences as these regimes provide one way to operationalize and thus inform sequential personalized clinical decision making. A dynamic treatment regi…
Decision MakingEstimating Dynamic Treatment Regimes in Mobile Health Using V-learning
The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best healthcare possible for each patient. Mobile technologies have an important ro…
Decision MakingReinforcement Learning