Continuous-time mediation analysis for repeatedly measured mediators and outcomes
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重复测量中介与结局的连续时间中介分析
- 研究问题
- 当中介、结局及混杂过程随时间变化且在不规则访视中观测时，如何估计固定暴露通过不同路径产生的因果效应？
- 方法与思路
- 作者定义自然效应、路径特定效应及随机干预类比效应，给出识别假设，并用基于微分方程的动态多变量模型估计。
- 材料陈述的贡献
- 摘要提出处理连续时间重复测量中介分析的方法，通过模拟考察表现，并以脑老化研究中的教育和遗传因素为应用。
- 阅读关注点
- 应分别核对三类效应的定义与识别假设、时变混杂的处理，以及不规则观测时间如何进入动态模型和估计过程。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未核实；摘要未列出具体识别假设、模型限制及两项应用的效应数值。
原文摘要
Mediation analysis aims to decipher the underlying causal mechanisms between an exposure, an outcome, and intermediate variables called mediators. Initially developed for fixed-time mediator and outcome, it has been extended to the framework of longitudinal data by discretizing the assessment times of mediator and outcome. Yet, processes in play in longitudinal studies are usually defined in continuous time and measured at irregular and subject-specific visits. This is the case in dementia research when cerebral and cognitive changes measured at planned visits in cohorts are of interest. We thus propose a methodology to estimate the causal mechanisms between a time-fixed exposure ($X$), a mediator process ($\mathcal{M}_t$) and an outcome process ($\mathcal{Y}_t$) both measured repeatedly over time in the presence of a time-dependent confounding process ($\mathcal{L}_t$). We consider three types of causal estimands, the natural effects, path-specific effects and randomized interventional analogues to natural effects, and provide identifiability assumptions. We employ a dynamic multivariate model based on differential equations for their estimation. The performance of the methods are explored in simulations, and we illustrate the method in two real-world examples motivated by the 3C cerebral aging study to assess: (1) the effect of educational level on functional dependency through depressive symptomatology and cognitive functioning, and (2) the effect of a genetic factor on cognitive functioning potentially mediated by vascular brain lesions and confounded by neurodegeneration.