用深度学习优化两种生存结局的个体化治疗方案。
Deep Optimal Individualized Treatment Rules for Bivariate Survival Outcomes via Adaptive Prediction-Powered Learning
- 通过随机策略与链接函数建模双生存结局相关性。
- 结合辅助预测提升治疗决策在右删失下的鲁棒性。
- 适合临床试验中多疗法个体化决策研究者。
在包含多种治疗方案的随机对照试验中，双生存结局带来显著分析挑战。本文提出一种新方法，利用深度神经网络推导最优个体化治疗规则，以最大化固定时间点 $(t_1, t_2)$ 之后的联合生存概率，同时处理右删失问题。该方法通过随机策略建模治疗规则，并采用链接函数耦合边际加速失效时间模型，捕捉双生存结局间的依赖关系。为增强决策的稳健性与有效性，引入自适应预测驱动方法，利用机器学习模型的辅助预测信息提升性能。
原文摘要 · Abstract (English)
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.
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