Estimating heterogeneous treatment effect (HTE) for survival outcomes has gained increasing attention, as it captures the variation in treatment efficacy across patients or subgroups in delaying disease progression. However, most existing methods focus on post-hoc subgroup identification rather than simultaneously estimating HTE and selecting relevant subgroups. In this paper, we propose an interpretable HTE estimation framework that integrates three meta-learners that simultaneously estimate CATE for survival outcomes and identify predictive subgroups. We evaluated the performance of our method through comprehensive simulation studies across various randomized clinical trial (RCT) settings. Additionally, we demonstrated its application in a large RCT for age-related macular degeneration (AMD), a polygenic progressive eye disease, to estimate the HTE of an antioxidant and mineral supplement on time-to-AMD progression and to identify genetics-based subgroups with enhanced treatment effects. Our method offers a direct interpretation of the estimated HTE and provides evidence to support precision healthcare.

翻译：生存结局的异质性治疗效果估计日益受到关注，因为它捕捉了治疗在延缓疾病进展方面在不同患者或亚组间的疗效差异。然而，现有方法大多侧重于事后亚组识别，而非同时估计HTE与选择相关亚组。本文提出一个可解释的HTE估计框架，该框架整合了三种元学习器，能够同时估计生存结局的条件平均处理效应并识别预测性亚组。我们通过在不同随机对照试验设置下的综合模拟研究评估了本方法的性能。此外，我们在针对年龄相关性黄斑变性（一种多基因进展性眼病）的大型RCT中展示了其应用，以评估抗氧化剂和矿物质补充剂对AMD进展时间的HTE，并识别具有增强治疗效果的遗传学亚组。本方法为估计的HTE提供了直接解释，并为支持精准医疗提供了证据。