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CAPITAL: Optimal subgroup identification via constrained policy tree search.

Hengrui Cai1, Wenbin Lu2, Rachel Marceau West3

  • 1Department of Statistics, University of California Irvine, Irvine, California, USA.

Statistics in Medicine
|July 8, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an optimal subgroup selection rule (SSR) to identify the maximum number of patients benefiting from personalized medicine treatments. The method ensures a clinically meaningful average treatment effect while maximizing patient inclusion.

Keywords:
constrained policy tree searchoptimal subgroup identificationpersonalized medicine

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Area of Science:

  • Biostatistics
  • Health Informatics
  • Personalized Medicine

Background:

  • Personalized medicine aims to tailor treatments to individual patient characteristics.
  • Identifying patient subgroups with superior treatment response is crucial.
  • Current methods often prioritize treatment effect over subgroup size.

Purpose of the Study:

  • To develop an optimal subgroup selection rule (SSR) that maximizes patient numbers benefiting from treatment.
  • To achieve a pre-specified clinically meaningful mean outcome, such as average treatment effect.
  • To introduce a flexible method for personalized medicine subgroup identification.

Main Methods:

  • Derived two theoretical forms of the optimal SSR based on treatment-covariate interaction.
  • Proposed the Constrained Policy Tree search algorithm (CAPITAL) for optimal SSR identification.
  • Developed a method flexible for multiple constraints and time-to-event data (restricted mean survival time).

Main Results:

  • The optimal SSR effectively maximizes the number of patients receiving beneficial treatment.
  • The CAPITAL algorithm successfully identifies interpretable decision trees for subgroup selection.
  • The method demonstrated validity and utility in simulations and real-world data applications.

Conclusions:

  • The proposed optimal SSR and CAPITAL algorithm offer a clinically meaningful approach to personalized medicine.
  • This method enhances the identification of patient subgroups who benefit most from targeted therapies.
  • The approach is adaptable for various clinical outcomes and constraints.