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Step-adjusted tree-based reinforcement learning for evaluating nested dynamic treatment regimes using test-and-treat

Ming Tang1, Lu Wang1, Michael A Gorin2

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.

Statistics in Medicine
|September 7, 2021
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Summary

We developed a new statistical method to evaluate dynamic treatment regimes (DTRs) in nested test-and-treat strategies. This approach optimizes medical decision-making for conditions like prostate cancer, improving cost-effectiveness.

Keywords:
dynamic treatment regimesmultistage decision-makingobservational datapersonalized health caretest-and-treat strategytree-based reinforcement learning

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Area of Science:

  • * Statistical learning and reinforcement learning.
  • * Biostatistics and health services research.

Background:

  • * Dynamic treatment regimes (DTRs) adapt treatments over time based on patient health status.
  • * Nested test-and-treat strategies, common in practice, pose challenges for existing statistical methods.
  • * Prostate cancer screening exemplifies nested decisions: biopsy follows PSA testing, influencing subsequent treatment.

Purpose of the Study:

  • * To develop a novel statistical learning method for evaluating DTRs within nested, multistage decision frameworks.
  • * To address the limitations of current methods in handling embedded treatment decisions within testing decisions.
  • * To identify optimal test-and-treat strategies using observational data.

Main Methods:

  • * Developed a step-adjusted tree-based reinforcement learning method.
  • * Combined semiparametric estimation via augmented inverse probability weighting with tree-based reinforcement learning.
  • * Applied to observational data within a nested multistage dynamic decision framework.

Main Results:

  • * Simulation studies confirmed the proposed method's robust performance across various scenarios.
  • * The method effectively handles counterfactual optimization in complex decision pathways.
  • * Applied to prostate cancer data, it evaluated biopsy necessity and identified optimal regimes.

Conclusions:

  • * The novel method successfully accommodates nested treatment decisions within dynamic regimes.
  • * It provides a robust framework for evaluating and optimizing complex, sequential medical strategies.
  • * The approach has practical implications for improving prostate cancer screening and treatment decisions.