融合多源数据优化个体化治疗方案，提升在人群差异下的鲁棒性。
Learning Optimal Distributionally Robust Individualized Treatment Rules Integrating Multi-Source Data
- 基于先验信息构建可变分布不确定集，适应不同人群的疗效差异。
- 理论证明该方法在最坏情况下仍能保持稳定性能，风险可控。
- 适用于临床研究中跨群体治疗决策，尤其适合数据异质性强的场景。
多源数据整合用于估计最优个体化治疗规则（ITRs）可提高决策效率。核心挑战是后验偏移：源群体与目标群体在协变量条件下潜在结果的分布存在差异。本文提出一种基于先验信息的分布鲁棒个体化治疗规则（PDRO-ITR），在协变量依赖的分布不确定集上最大化最差情况下的策略价值，确保在后验偏移下依然具备稳健表现。不确定集由各源分布的个性化组合构成，权重结合先验来源归属概率与偏差项，并约束在概率单纯形内以适应后验偏移。我们推导出PDRO-ITR的闭式解，并提出自适应调参方法以调整不确定性水平。建立了该估计器的风险界，保证最坏情况下的稳健性。大量模拟实验及两个真实数据应用表明，该方法优于现有方法。
原文摘要 · Abstract (English)
Integrative analysis of multiple datasets for estimating optimal individualized treatment rules (ITRs) can enhance decision efficiency. A central challenge is posterior shift, wherein the conditional distribution of potential outcomes given covariates differs between source and target populations. We propose a prior information-based distributionally robust ITR (PDRO-ITR) that maximizes the worst-case policy value over a covariate-dependent distributional uncertainty set, ensuring robust performance under posterior shift. The uncertainty set is constructed as an individualized combination of source distributions, with weights combining prior source-membership probabilities and deviation terms constrained to the probability simplex to accommodate posterior shift. We derive a closed-form solution for the PDRO-ITR and develop an adaptive procedure to tune the uncertainty level. We establish risk bounds for the PDRO-ITR estimator, which guarantees robust performance under the worst case. Extensive simulations and two real-data applications demonstrate that the proposed method achieves superior performance compared to existing approaches.
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