Abstract Number:
3358
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Fang Niu (1), Cheng Zheng (1)
Institutions:
(1) University of Nebraska Medical Center, Omaha, NE, USA
Co-Author:
First Author:
Fang Niu
University of Nebraska Medical Center
Presenting Author:
Abstract Text:
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.
Keywords:
Causal Inference|Joint Modeling|Mutiple Mediation Analysis|Recurrent Event|Survival Data|
Sponsors:
Lifetime Data Science Section
Tracks:
Miscellaneous
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