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Estimating tree‐based dynamic treatment regimes using observational data with restricted treatment sequences

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  • Nina Zhou
  • Lu Wang
  • Daniel Almirall

Abstract

A dynamic treatment regime (DTR) is a sequence of decision rules that provide guidance on how to treat individuals based on their static and time‐varying status. Existing observational data are often used to generate hypotheses about effective DTRs. A common challenge with observational data, however, is the need for analysts to consider “restrictions” on the treatment sequences. Such restrictions may be necessary for settings where (1) one or more treatment sequences that were offered to individuals when the data were collected are no longer considered viable in practice, (2) specific treatment sequences are no longer available, or (3) the scientific focus of the analysis concerns a specific type of treatment sequences (eg, “stepped‐up” treatments). To address this challenge, we propose a restricted tree–based reinforcement learning (RT‐RL) method that searches for an interpretable DTR with the maximum expected outcome, given a (set of) user‐specified restriction(s), which specifies treatment options (at each stage) that ought not to be considered as part of the estimated tree‐based DTR. In simulations, we evaluate the performance of RT‐RL versus the standard approach of ignoring the partial data for individuals not following the (set of) restriction(s). The method is illustrated using an observational data set to estimate a two‐stage stepped‐up DTR for guiding the level of care placement for adolescents with substance use disorder.

Suggested Citation

  • Nina Zhou & Lu Wang & Daniel Almirall, 2023. "Estimating tree‐based dynamic treatment regimes using observational data with restricted treatment sequences," Biometrics, The International Biometric Society, vol. 79(3), pages 2260-2271, September.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:3:p:2260-2271
    DOI: 10.1111/biom.13754
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    References listed on IDEAS

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    1. Lu Wang & Andrea Rotnitzky & Xihong Lin & Randall E. Millikan & Peter F. Thall, 2012. "Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 493-508, June.
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    3. Yilun Sun & Lu Wang, 2021. "Stochastic Tree Search for Estimating Optimal Dynamic Treatment Regimes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(533), pages 421-432, January.
    4. Yebin Tao & Lu Wang, 2017. "Adaptive contrast weighted learning for multi-stage multi-treatment decision-making," Biometrics, The International Biometric Society, vol. 73(1), pages 145-155, March.
    5. S. A. Murphy, 2003. "Optimal dynamic treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 331-355, May.
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