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

Nina Zhou1, Lu Wang1, Daniel Almirall2

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

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PubMed
Summary

We introduce a restricted tree-based reinforcement learning (RT-RL) method to optimize dynamic treatment regimes (DTRs) under specific treatment sequence restrictions. This approach enhances DTR estimation from observational data, even when some treatment paths are no longer viable.

Keywords:
constrained optimizationdynamic treatment regimeobservational studiestree-based reinforcement learningviable decision rules

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Area of Science:

  • Machine Learning
  • Biostatistics
  • Health Services Research

Background:

  • Dynamic treatment regimes (DTRs) guide sequential treatment decisions based on patient status.
  • Observational data is crucial for DTR hypothesis generation but often requires handling treatment sequence restrictions.
  • Restrictions arise when certain treatment sequences become non-viable, unavailable, or are of specific scientific interest (e.g., stepped-up treatments).

Purpose of the Study:

  • To propose a novel restricted tree-based reinforcement learning (RT-RL) method for estimating interpretable DTRs.
  • To incorporate user-specified restrictions on treatment sequences within the DTR estimation process.
  • To maximize the expected outcome under these specified restrictions.

Main Methods:

  • Developed a restricted tree-based reinforcement learning (RT-RL) algorithm.
  • RT-RL identifies optimal DTRs by searching through treatment options while adhering to user-defined restrictions.
  • Evaluated RT-RL performance against standard methods using simulations and an observational dataset for adolescent substance use disorder treatment.

Main Results:

  • Simulations demonstrated RT-RL's effectiveness compared to ignoring data from individuals not adhering to restrictions.
  • The method successfully estimated a two-stage stepped-up DTR for adolescent substance use disorder care placement.
  • RT-RL provides an interpretable DTR that accounts for practical constraints on treatment pathways.

Conclusions:

  • The proposed RT-RL method offers a robust approach for estimating DTRs with treatment sequence restrictions from observational data.
  • This method enhances the practical utility of DTRs by accommodating real-world constraints on treatment options.
  • RT-RL is a valuable tool for optimizing treatment strategies in complex healthcare settings, such as guiding care for adolescents with substance use disorder.