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Variable selection for estimating optimal treatment regimes with multiple treatments.

Yuexin Fang1, Yu Liu2

  • 1Department of Mathematics, Shanghai Normal University, Shanghai, China.

Scientific Reports
|June 14, 2026
PubMed
Summary

This study introduces a penalized classification method for estimating optimal treatment regimes (OTRs) with multiple treatments. The approach effectively identifies key covariates driving treatment heterogeneity, improving accuracy and robustness in complex datasets.

Keywords:
Doubly robust estimationHigh-dimensionalMulticlass classificationOptimal treatment regimeVariable selection

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Area of Science:

  • Statistics
  • Machine Learning
  • Biostatistics

Background:

  • Estimating optimal treatment regimes (OTRs) is crucial for personalized medicine.
  • Challenges arise with multiple treatments and a large number of covariates.
  • Existing methods may struggle with simultaneous variable selection and robust estimation.

Purpose of the Study:

  • To propose a novel penalized classification method for OTR estimation.
  • To integrate variable selection and doubly robust estimation into a unified framework.
  • To identify sparse subsets of covariates influencing treatment effect heterogeneity.

Main Methods:

  • Reformulating OTR estimation as a weighted multiclass classification problem.
  • Employing a data expansion technique with L1-type penalization.
  • Utilizing augmented inverse probability weighting (AIPW) estimators for double robustness.

Main Results:

  • The proposed method effectively performs simultaneous variable selection and regime estimation.
  • Simulation studies show superior accuracy and double robustness compared to existing methods.
  • The method successfully identifies covariates driving treatment effect heterogeneity.

Conclusions:

  • The penalized classification method offers a robust and accurate approach for OTR estimation.
  • It is particularly effective in high-dimensional settings with multiple treatments.
  • Demonstrated practical utility in a chronic depression clinical trial dataset.