Optimal Dynamic Treatment Regimes for High-Dimensional Accelerated Failure Time Model

The increasing prevalence of high-dimensional covariates presents a significant challenge to precision medicine. To overcome this, we propose a novel multi-stage optimal dynamic treatment regime method specifically designed for high-dimensional accelerated failure time models. Our approach, which aims to maximize individual survival time, employs backward induction with counterfactual survival times as the optimization objective at each stage. Parameter estimation leverages weighted counterfactual survival times to account for censoring, followed by the slope determination by analysis of residuals (SDAR) algorithm. Theoretical analysis guarantees exponential convergence with non-asymptotic error bounds under mild conditions. Simulation studies demonstrate a substantial improvement in survival time estimation, particularly under high censoring rates, compared to existing methods. We illustrate the practical utility of our method by applying it to a sepsis dataset, revealing clinically meaningful treatment recommendations.