Variable selection for estimating optimal treatment regimes with multiple treatments.
1Department of Mathematics, Shanghai Normal University, Shanghai, China.
Scientific Reports
|June 14, 2026
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.
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.
Keywords:
Doubly robust estimationHigh-dimensionalMulticlass classificationOptimal treatment regimeVariable selectionMore Related Videos
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