Mediation analysis is a useful tool to evaluate surrogate endpoints in clinical trials. We propose a novel method, the M-survival learner, for estimating heterogeneous indirect treatment effects in the presence of censored outcomes. The proposed approach enables the identification of interpretable patient subgroups characterized by distinct mediation pathways. To distinguish heterogeneous from homogeneous mediation effects, we introduce a new statistical criterion specifically designed for survival data. The method provides a principled framework for evaluating heterogeneity in surrogate biomarker performance across patient populations, offering evidence to support accelerated approval drug. By explicitly assessing subgroup-specific surrogate validity, the proposed approach addresses key regulatory concerns regarding the reliability of surrogate endpoints. We further establish theoretical properties of the method to justify its statistical guarantees. We apply the approach to data from a Phase III randomized clinical trial of HIV treatment, demonstrating its practical utility in real-world settings. Extensive simulation studies further evaluate and demonstrate its finite-sample performance.

翻译：中介分析是评估临床试验中替代终点的重要工具。我们提出了一种新方法——M生存学习器，用于在存在删失结局时估计异质性间接处理效应。该方法能够识别以不同中介通路为特征的可解释患者亚组。为区分异质性与同质性中介效应，我们引入了专门针对生存数据设计的统计新准则。该方法为评估替代生物标志物在不同患者群体中的表现异质性提供了系统性框架，为药物加速审批提供证据支持。通过显式评估亚组特异性替代终点有效性，该方法解决了监管机构对替代终点可靠性方面的核心关切。我们进一步建立了该方法的理论性质以证明其统计保证。将该方法应用于一项HIV治疗的III期随机临床试验数据，展示了其在真实场景中的实用价值。广泛的模拟研究进一步评估并证明了其有限样本性能。