A Guide to Estimating Conditional Average Treatment Effects in Competing Risks Settings

2026-06-08

AI Summary

This study addresses the critical need for accurate estimation of the conditional average treatment effect (CATE) for specific events in survival analysis under competing risks and right censoring, which is essential for personalized medicine. The authors propose a meta-learner–based framework in a binary treatment setting, defining CATE as the absolute risk difference at a fixed time point. They systematically evaluate six meta-learner strategies that combine either Cox regression or random survival forests to model event-specific risks, paired with elastic net or random forest models to directly estimate CATE. Comprehensive simulations encompassing diverse risk structures, treatment effect heterogeneity, treatment assignment mechanisms, and censoring levels are conducted to assess performance. Based on empirical findings, the study offers practical modeling recommendations and releases the open-source R package crsurvlearners to facilitate broader application.

Abstract

Conditional average treatment effects (CATEs) are central to treatment decision-making in personalized medicine. In competing risks settings, estimating CATEs from survival data allows for patient-specific assessments of treatment effectiveness for a specific event of interest while properly accounting for alternative event types. This distinction is essential in the presence of comorbidities, where competing causes of death may otherwise confound the therapeutic benefit. Focusing on right-censored survival times with binary treatment, we examine CATEs defined as covariate-conditional differences in the absolute risk for the event of interest at a fixed time. To this end, we study meta-learners which adapt machine learning algorithms for CATE estimation in competing risks scenarios. We systematically compare six meta-learners, combining Cox regression or random survival forests for risk modeling with elastic net regression or random forests for direct CATE modeling. To provide practical guidance on model selection, we evaluate their performance in multiple simulation settings, that differ in hazard complexity, treatment heterogeneity, treatment assignment, event type distribution and censoring. To facilitate applied use, we provide the R package, crsurvlearners, which implements all considered approaches.

Problem

• conditional average treatment effects
• competing risks
• personalized medicine
• survival analysis
• treatment decision-making

Innovation

• conditional average treatment effect
• competing risks
• meta-learners
• survival analysis
• personalized medicine