A Tutorial on Optimal Dynamic Treatment Regimes

A dynamic treatment regime (DTR) 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 DTR which, if followed by everyone in the population, would yield the best outcome on average; and extensive investigations have been conducted from both methodological and applied standpoints. The purpose of this tutorial is to provide readers who are interested in optimal DTRs 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 for learning the optimal regime from the data, and application of these methods to both simulated and real data.

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Publication Details

Journal
Statistics in Medicine
Published
2026-02-01
DOI
https://doi.org/10.1002/sim.70395
Citations
1
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
41.27
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article

A Tutorial on Optimal Dynamic Treatment Regimes

Brian D. M. Tom, Chunyu Wang
1 citations
Statistics in Medicine
Advanced Causal Inference Techniques
41.27
article

A Tutorial on Optimal Dynamic Treatment Regimes

Brian D. M. Tom, Chunyu Wang
article en
1 citations

Abstract

A dynamic treatment regime (DTR) 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 DTR which, if followed by everyone in the population, would yield the best outcome on average; and extensive investigations have been conducted from both methodological and applied standpoints. The purpose of this tutorial is to provide readers who are interested in optimal DTRs 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 for learning the optimal regime from the data, and application of these methods to both simulated and real data.

Statistics in MedicineVol. 45(3-5)
University of Cambridge (GB), Yale University (US), MRC Biostatistics Unit (GB), Zhejiang University (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 1%
Advanced Causal Inference Techniques
41.27
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A Tutorial on Optimal Dynamic Treatment Regimes — Brian D. M. Tom, Chunyu Wang · Statistics in Medicine (2026) | TGRS Research Map | TGRS