Interpretable Heterogeneous Treatment Effect Estimation and Causal Subgroup Discovery in Survival Outcomes, with Application to Age-related Macular Degeneration Studies

Music City Center 

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 improving survival or delaying disease progression. However, most existing methods focus on post hoc subgroup identification rather than simultaneously estimating HTE and selecting causal subgroups. In this paper, we propose an interpretable HTE estimation framework that uses meta-learners with the conditional inference tree to estimate CATE for survival outcomes and identify predictive subgroups simultaneously. We evaluated the performance of our method through comprehensive simulation studies in various randomized clinical trial (RCT) settings. Furthermore, we demonstrate its application in a large RCT for age-related macular degeneration (AMD), a progressive polygenic 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 guide precision medicine and healthcare.

interpretable heterogeneous treatment effect

precision medicine

randomized clinical trials

subgroup identification

age-related eye disease studies (AREDS)