A tutorial on optimal dynamic treatment regimes
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866917000408727552 |
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| author | Wang, Chunyu Tom, Brian DM |
| author_facet | Wang, Chunyu Tom, Brian DM |
| contents | A dynamic treatment regime is a sequence of treatment decision rules tailored to an individual's evolving status over time. In precision medicine, much focus has been placed on finding an optimal dynamic treatment regime which, if followed by everyone in the population, would yield the best outcome on average; and extensive investigation has been conducted from both methodological and applications standpoints. The aim of this tutorial is to provide readers who are interested in optimal dynamic treatment regimes with a systematic, detailed but accessible introduction, including the formal definition and formulation of this topic within the framework of causal inference, identification assumptions required to link the causal quantity of interest to the observed data, existing statistical models and estimation methods to learn the optimal regime from data, and application of these methods to both simulated and real data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16988 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A tutorial on optimal dynamic treatment regimes Wang, Chunyu Tom, Brian DM Other Statistics Applications A dynamic treatment regime is a sequence of treatment decision rules tailored to an individual's evolving status over time. In precision medicine, much focus has been placed on finding an optimal dynamic treatment regime which, if followed by everyone in the population, would yield the best outcome on average; and extensive investigation has been conducted from both methodological and applications standpoints. The aim of this tutorial is to provide readers who are interested in optimal dynamic treatment regimes with a systematic, detailed but accessible introduction, including the formal definition and formulation of this topic within the framework of causal inference, identification assumptions required to link the causal quantity of interest to the observed data, existing statistical models and estimation methods to learn the optimal regime from data, and application of these methods to both simulated and real data. |
| title | A tutorial on optimal dynamic treatment regimes |
| topic | Other Statistics Applications |
| url | https://arxiv.org/abs/2502.16988 |