Estimating Interpretable Heterogeneous Treatment Effect with Causal Subgroup Discovery in Survival Outcomes

TL;DR

This paper introduces an interpretable framework for estimating heterogeneous treatment effects in survival outcomes, enabling simultaneous subgroup identification and effect estimation to support personalized medicine.

Contribution

It proposes a novel method integrating three meta-learners for concurrent HTE estimation and subgroup selection in survival analysis, addressing limitations of existing post-hoc approaches.

Findings

Effective in simulation studies across various RCT settings.

Successfully applied to AMD trial data to identify genetics-based subgroups.

Provides interpretable estimates supporting precision healthcare.

Abstract

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.