Wednesday, Aug 7: 9:50 AM - 9:55 AM
3358
Contributed Speed
Oregon Convention Center
In the context of HIV patients, over 20 distinct opportunistic infections (OIs) present complex effects on the health trajectory and associated mortality. It is crucial to differentiate among these OIs to devise tailored strategies to enhance patients' survival and quality of life. However, existing statistical frameworks for studying causal mechanisms have limitations, either focusing on single mediators or lacking the ability to handle unmeasured confounding, especially for the survival outcomes. In this work, we propose a novel joint modeling approach that considers multiple recurrent events as mediators and survival endpoints as outcomes, relaxing the assumption of "sequential ignorability" by utilizing the shared random effect to handle unmeasured confounders. We assume the multiple mediators are not causally related to each other given observed covariates and the shared frailty. Simulation studies demonstrate good finite sample performance of our methods in estimating both model parameters and multiple mediation effects. We apply our approach to an AIDS study and find that distinct pathways through the two treatments and CD4 counts impact overall survival via different OIs.
Causal Inference
Joint Modeling
Mutiple Mediation Analysis
Recurrent Event
Survival Data
Main Sponsor
Lifetime Data Science Section