Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916997868027904 |
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| author | Park, Sihyung Lu, Wenbin Yang, Shu |
| author_facet | Park, Sihyung Lu, Wenbin Yang, Shu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07501 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death Park, Sihyung Lu, Wenbin Yang, Shu Machine Learning Methodology 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. |
| title | Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2510.07501 |