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Multiobjective tree-based reinforcement learning for estimating tolerant dynamic treatment regimes.

Yao Song1, Lu Wang1

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, MI 48105, United States.

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|February 16, 2024
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Summary

This study introduces a "tolerant regime" concept for personalized medicine, offering multiple feasible treatment rules when priorities conflict. The new multiobjective tree-based reinforcement learning (MOT-RL) method effectively estimates these tolerant dynamic treatment regimes (tDTRs).

Keywords:
causal inferencedecision treedynamic treatment regimesmultiobjective optimizationpersonalized medicine

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Area of Science:

  • Biostatistics
  • Machine Learning
  • Personalized Medicine

Background:

  • Dynamic treatment regimes (DTRs) personalize medical decisions based on patient history.
  • Real-world scenarios often involve multiple competing objectives, requiring trade-offs in treatment decisions.
  • Existing methods may not adequately address situations with multiple optimal or near-optimal treatment strategies.

Purpose of the Study:

  • To introduce the concept of a "tolerant regime" (tDTR) that accommodates multiple feasible individualized decision rules under a specified tolerance rate.
  • To develop a novel multiobjective tree-based reinforcement learning (MOT-RL) method for estimating tDTRs in multistage, multitreatment settings.
  • To optimize clinical decision support systems by considering multiple objectives and trade-offs.

Main Methods:

  • Developed a multiobjective tree-based reinforcement learning (MOT-RL) algorithm.
  • Employed unsupervised decision trees at each stage, modeling counterfactual outcomes via semiparametric regression.
  • Utilized scalarized augmented inverse probability weighted estimators (SAIPWE) to construct a purity measure for optimization.
  • Implemented the algorithm in a backward inductive manner for multistage decision-making.

Main Results:

  • The MOT-RL method directly estimates optimal DTRs and tDTRs, accommodating decision-maker preferences.
  • The approach is robust, efficient, interpretable, and flexible across various settings.
  • Successfully applied MOT-RL to evaluate 2-stage chemotherapy regimes for advanced prostate cancer.

Conclusions:

  • The proposed "tolerant regime" concept and MOT-RL method offer a flexible framework for personalized medicine with competing objectives.
  • This approach enhances clinical decision support by providing a set of feasible, individualized treatment strategies.
  • Demonstrated the practical utility of MOT-RL in optimizing cancer treatment strategies for improved patient outcomes.