Skip to content

Abstract Search

Primary Submission Category: Mediation

Exploring causal mechanisms and quantifying direct and indirect effects using a joint modeling approach for recurrent and terminal events

Authors: Cheng Zheng, Fang Niu, Lei Liu,

Presenting Author: Cheng Zheng*

Recurrent events are commonly encountered in biomedical studies. In many situations, there exists a terminal event, e.g., death, which is potentially related to recurrent events. Joint models of recurrent and terminal events have been proposed to address the correlation between the recurrent event and the terminal event. However, there is a dearth of suitable methods to rigorously investigate the causal mechanisms between specific exposures, recurrent events, and terminal events. For example, it is of interest to know whether preventing the happening of certain recurrent events could lead to better overall survival and how much of the total effect of the primary exposure of interest on the terminal event is through the recurrent events. In this work, we propose a formal causal mediation analysis method to compute the natural direct and indirect effects. A novel joint modeling approach is used to take the recurrent event process as the mediator and the survival endpoint as the outcome. This new joint modeling approach allows us to relax the commonly used “sequential ignorability” assumption. Simulation studies show our new model’s good finite sample performance in estimating both model parameters and mediation effects. We apply our method to an AIDS study to evaluate how much of the comparative effectiveness of the two treatments and the effect of CD4 counts on overall survival are mediated by recurrent opportunistic infections.