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GENERALIZATION ERROR BOUNDS OF DYNAMIC TREATMENT REGIMES IN PENALIZED REGRESSION-BASED LEARNING
Eun Jeong Oh1, Min Qian1, Ying Kuen Cheung1
1Department of Biostatistics, Columbia University.
Annals of Statistics
|June 12, 2026
Summary
This study introduces penalized regression methods to find optimal dynamic treatment regimes (DTRs) for maximizing patient outcomes. The proposed approach provides generalization error bounds and practical algorithms for complex treatment decisions.
Area of Science:
- Biostatistics
- Machine Learning
- Clinical Trial Methodology
Background:
- Dynamic treatment regimes (DTRs) are crucial for personalized medicine but challenging to optimize with many variables.
- Existing methods struggle with high-dimensional prognostic data for DTR discovery.
Purpose of the Study:
- To develop and evaluate penalized regression methods for estimating optimal DTRs.
- To provide theoretical guarantees (generalization error bounds) for the proposed DTR estimation methods.
- To enable practical implementation of optimal DTRs through an efficient algorithm.
Main Methods:
- Utilized penalized regression with L1 penalty for DTR estimation.
- Derived finite sample upper bounds for the difference between optimal and estimated DTR values.
- Developed a partial regularization via orthogonality algorithm for practical DTR construction.
Main Results:
- Proposed methods effectively estimate optimal DTRs that maximize expected outcomes.
- Generalization error bounds were established for finite-stage DTRs with multiple treatments.
- Simulations and depression trial data analysis demonstrated the methods' advantages.
Conclusions:
- Penalized regression offers a robust framework for discovering optimal dynamic treatment regimes.
- The developed methods and algorithms facilitate practical application in clinical settings.
- This work advances personalized treatment strategies by improving DTR estimation accuracy and reliability.
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