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<p>Understanding the diverse causal mechanisms between primary exposure and outcomes has garnered significant interest in the social and medical fields. 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 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 evaluate the mediation effects of different types of OIs. We find that distinct pathways through the two treatments and CD4 counts impact overall survival via different types of recurrent opportunistic infections.</p>

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Investigating Multiple Causal Mechanisms with Multiple Mediators and Estimating Direct and Indirect Effects: A Joint Modeling Approach for Recurrent and Terminal Events

  • 期刊论文

  • 发表日期：

    2025年5月29日

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  • Fang Niu,
  • Cheng Zheng,
  • Lei Liu

摘要

Understanding the diverse causal mechanisms between primary exposure and outcomes has garnered significant interest in the social and medical fields. 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 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 evaluate the mediation effects of different types of OIs. We find that distinct pathways through the two treatments and CD4 counts impact overall survival via different types of recurrent opportunistic infections.