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

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.