Investigating Multiple Causal Mechanisms and Estimating NDE and NIE:A Joint Modeling Approach

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Fang Niu (1), Cheng Zheng (1)

(1) University of Nebraska Medical Center, Omaha, NE, USA

Cheng Zheng  
University of Nebraska Medical Center

Fang Niu  
University of Nebraska Medical Center

Fang Niu  
N/A

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|

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