Joint Model for Mediation Analysis with Causally Related Longitudinal and Recurrent Event Mediators for Survival Outcome

2026-07-26

AI Summary

Existing methods struggle to perform causal mediation analysis when longitudinal repeated measurements and recurrent events act as multiple mediators influencing survival outcomes in the presence of unmeasured confounding. This work proposes a joint modeling framework based on shared frailties, which incorporates shared random effects to relax the sequential ignorability assumption and effectively adjust for time-invariant unobserved confounders, thereby enabling simultaneous estimation of natural direct and indirect effects. The approach accommodates multiple causal mediation pathways and, when applied to the CPCRA study, reveals that both opportunistic infections and CD4 count mediate the effect of prior AIDS-defining illnesses on survival. Simulation studies confirm that the proposed estimators exhibit good robustness in finite samples.

Abstract

Recurrent events and repeated measures are commonly encountered in clinical longitudinal studies, often holding strong associations with patient outcomes. Although joint models for repeated measures, recurrent events, and a terminal event have been developed to account for their correlation, limited methodologies exist to examine causal mediation mechanisms involving multiple types of mediators, especially when mediators are causally related. This study addresses this gap by proposing a novel causal mediation analysis framework to quantify natural direct and indirect effects when both recurrent events and repeated measures act as mediators with causal dependencies. We extend joint modeling approaches by incorporating shared random effects (frailties) structures, relaxing the commonly used ``sequential ignorability" assumption, and accounting for unmeasured time-independent confounders through shared random effects. We apply our method to the Terry Beirn Community Programs for Clinical Research on AIDS (CPCRA) study and demonstrate that both recurrent opportunistic infections (OIs) and repeated CD4 measurements mediate the effects of prior AIDS-defining conditions on survival outcomes. Additionally, the shared random effects between repeated CD4 and survival models highlight the presence of unmeasured confounding between CD4 counts and mortality. Simulation studies demonstrate the robustness and finite sample performance of our estimators for natural direct and indirect effects. The proposed methodology enables a more comprehensive investigation of causal pathways in longitudinal studies with multiple mediators, providing insights into treatment mechanisms and informing clinical decision-making.

Problem

• causal mediation analysis
• recurrent events
• longitudinal mediators
• survival outcome
• unmeasured confounding

Innovation

• causal mediation analysis
• joint modeling
• shared random effects
• recurrent events
• longitudinal mediators