Deep Optimal Individualized Treatment Rules for Bivariate Survival Outcomes via Adaptive Prediction-Powered Learning
For researchers analyzing randomized trials with multiple treatments and bivariate survival outcomes, this work provides a novel method to derive optimal treatment rules, though it is domain-specific to survival analysis.
This paper develops a deep learning method to derive optimal individualized treatment rules for bivariate survival outcomes, maximizing joint survival probability beyond fixed time points while handling right censoring. The proposed adaptive prediction-powered approach improves decision-making robustness and effectiveness.
In randomized trials involving multiple treatments, bivariate survival outcomes present significant analytical challenges for making decisions. This paper addresses the problem of deriving optimal individualized treatment rules to maximize the joint survival probability beyond fixed time points $(t_1, t_2)$ through deep neural networks, while accounting for right censoring. We propose a novel approach that models treatment rules via stochastic policies, coupling marginal accelerated failure time models via link function to capture bivariate dependence. To enhance robustness and effectiveness of decision making, we introduce an adaptive prediction-powered method that leverages auxiliary predictions from machine learning models.