Modeling Heterogeneous Mediation Effects in Survival Analysis via an Interpretable M-Learner Framework

TL;DR

This paper introduces the M-survival learner, a new interpretable method for estimating and identifying heterogeneous mediation effects in survival analysis, especially useful for clinical trial surrogate endpoint evaluation.

Contribution

The paper presents a novel statistical framework for detecting subgroup-specific mediation effects in censored survival data, with theoretical guarantees and real-world application.

Findings

Effectively identifies patient subgroups with distinct mediation pathways.

Provides a statistical criterion to distinguish heterogeneity in survival data.

Demonstrates practical utility through clinical trial data analysis.

Abstract

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.