Model-Assisted Causal Inference for the Treatment Effect on Recurrent Events in the Presence of Terminal Events

2026-01-03

AI Summary

This study addresses the challenge of estimating treatment effects on recurrent events in the presence of a terminal event, such as death, where conventional methods are susceptible to survivorship bias and perform poorly under time-varying recurrence rates. The authors propose a novel approach, PR-MSMaT, which uniquely integrates proportional rates marginal structural models with the separable effects framework from causal inference to accurately estimate the separable effect of treatment on recurrent events. The method effectively controls Type I error under time-varying recurrence rates, maintains high statistical power, and mitigates survivorship bias. Simulation studies demonstrate its superiority over traditional While-Alive tests, and real-world application to MCS device comparison successfully identifies differences in postoperative gastrointestinal bleeding risk.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Intelligent Robots: State Estimation

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper is motivated by evaluating the benefits of patients receiving mechanical circulatory support (MCS) devices in end-stage heart failure management inference, in which hypothesis testing for a treatment effect on the risk of recurrent events is challenged in the presence of terminal events. Existing methods based on cumulative frequency unreasonably disadvantage longer survivors as they tend to experience more recurrent events. The While-Alive-based (WA) test has provided a solution to address this survival-length-bias problem, and it performs well when the recurrent event rate holds constant over time. However, if such a constant-rate assumption is violated, the WA test can exhibit an inflated type I error and inaccurate estimation of treatment effects. To fill this methodological gap, we propose a Proportional Rate Marginal Structural Model-assisted Test (PR-MSMaT) in the causal inference framework of separable treatment effects for recurrent and terminal events. Using the simulation study, we demonstrate that our PR-MSMaT can properly control type I error while gaining power comparable to the WA test under time-varying recurrent event rates. We employ PR-MSMaT to compare different MCS devices with the postoperative risk of gastrointestinal bleeding among patients enrolled in the Interagency Registry of Mechanically Assisted Circulatory Support program.

Problem

• treatment effect
• recurrent events
• terminal events
• causal inference
• survival-length bias

Innovation

• Causal Inference
• Recurrent Events
• Terminal Events
• Marginal Structural Model
• Treatment Effect