Joint Model for Mediation Analysis with Causally Related Longitudinal and Recurrent Event Mediators for Survival Outcome
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生存结局中具有因果关联的纵向与复发事件中介的联合模型
- 研究问题
- 当重复测量与复发事件既共同影响生存又存在因果关联时，如何估计自然直接和间接效应，并处理未测量混杂？
- 方法与思路
- 作者扩展联合模型，引入共享随机效应与脆弱性结构，用以刻画纵向中介、复发事件和生存之间的依赖及时间不变混杂。
- 材料陈述的贡献
- 摘要称框架放松通常的序贯可忽略性要求，并在模拟与艾滋病研究中展示多类型中介效应估计及有限样本表现。
- 阅读关注点
- 应核对自然效应的定义、多个中介的因果顺序，以及共享随机效应如何替代原有识别限制和应对时间变化混杂。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未说明替代识别假设，不能据共享随机效应认定所有未测量混杂已消除。
原文摘要
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.