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Optimal Dynamic Treatment Regimes for High-Dimensional Accelerated Failure Time Model
1Institute for Financial Studies, Shandong University, Jinan, Shandong, China.
Statistics in Medicine
|August 14, 2026
Summary
This study introduces a new method for high-dimensional accelerated failure time models to optimize dynamic treatment regimes, significantly improving survival time estimation for precision medicine. The approach enhances patient outcomes, especially with high censoring rates, and provides clinically relevant treatment recommendations.
Area of Science:
- Biostatistics
- Machine Learning in Healthcare
- Precision Medicine
Background:
- High-dimensional covariates pose challenges for precision medicine.
- Accelerated failure time (AFT) models are crucial for survival analysis.
- Optimizing dynamic treatment regimes (DTRs) is essential for personalized care.
Purpose of the Study:
- To develop a novel multi-stage optimal DTR method for high-dimensional AFT models.
- To maximize individual survival time in precision medicine.
- To address challenges posed by high-dimensional data and censoring.
Main Methods:
- A multi-stage optimal DTR approach using backward induction.
- Optimization objective: maximizing counterfactual survival times at each stage.
- Parameter estimation using weighted counterfactual survival times and the Slope Determination by Analysis of Residuals (SDAR) algorithm.
Main Results:
- Theoretical analysis guarantees exponential convergence with non-asymptotic error bounds.
- Simulation studies show substantial improvements in survival time estimation.
- The method performs well, especially under high censoring rates.
Conclusions:
- The proposed method effectively handles high-dimensional data in AFT models for DTRs.
- It offers significant improvements over existing methods, particularly in challenging scenarios.
- Application to a sepsis dataset yielded clinically meaningful treatment recommendations.
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