用于生存结局的具有因果相关纵向和复发事件中介的中介分析联合模型
Joint Model for Mediation Analysis with Causally Related Longitudinal and Recurrent Event Mediators for Survival Outcome
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中文总结 AI 辅助
该研究针对临床纵向研究中多种中介因果机制研究方法有限的问题，提出新颖因果中介分析框架，纳入共享随机效应等扩展联合建模方法，应用于艾滋病研究，证明方法有效性和稳健性，能全面研究纵向研究因果途径。
中文摘要 AI 辅助
在临床纵向研究中经常会遇到复发事件和重复测量，它们通常与患者结局有很强的关联。虽然已经开发了用于重复测量、复发事件和终末事件的联合模型来解释它们的相关性，但用于检查涉及多种类型中介的因果中介机制的方法有限，特别是当中介具有因果关系时。本研究通过提出一种新颖的因果中介分析框架来解决这一差距，以量化当复发事件和重复测量都作为具有因果依赖性的中介时的自然直接和间接效应。我们通过纳入共享随机效应（脆弱性）结构、放宽常用的“顺序可忽略性”假设以及通过共享随机效应考虑未测量的时间独立混杂因素来扩展联合建模方法。我们将我们的方法应用于艾滋病临床研究的特里·贝恩社区项目（CPCRA）研究，并证明复发机会性感染（OIs）和重复的CD4测量都介导了先前艾滋病定义条件对生存结局的影响。此外，重复CD4和生存模型之间的共享随机效应突出了CD4计数和死亡率之间存在未测量的混杂。模拟研究证明了我们对自然直接和间接效应估计器的稳健性和有限样本性能。所提出的方法能够更全面地研究具有多个中介的纵向研究中的因果途径，为治疗机制提供见解并为临床决策提供信息。
英文摘要
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.
