ABSTRACT

This study develops a joint modeling approach that incorporates latent traits into causal mediation analysis with multiple mediators and a survival outcome. A linear structural equation model is used to characterize the latent mediators with several highly correlated observable surrogates and depicts the relationships among multiple parallel or causally ordered mediators and the exposure. A proportional hazards model is used to derive the path-specific causal effects on the scale of hazard ratio under the counterfactual framework with a set of sequential ignorability assumptions. A Bayesian approach with Markov chain Monte Carlo algorithm is developed to perform efficient estimation of the causal effects. Posterior propriety theory is established for the proportional hazards model with latent variables. Empirical performance of the proposed method is verified through simulation studies. The proposed model is then applied to a study on the Alzheimer’s Disease Neuroimaging Initiative dataset to investigate the causal effects of APOE-$\epsilon 4$ allele on the disease progression, either directly or through potential mediators, such as hippocampus atrophy, ventricle expansion, and cognitive impairment.

摘要

本研究开发了一种联合建模方法，将潜在特征纳入具有多种中介因素和生存结果的因果中介分析中。线性结构方程模型用于表征具有几个高度相关的可观察替代物的潜在介质，并描述多个平行或因果有序的介质与暴露之间的关系。比例风险模型用于在具有一组连续可忽略性假设的反事实框架下推导路径特定的因果关系对风险比规模的影响。开发了具有马尔可夫链蒙特卡罗算法的贝叶斯方法来执行因果效应的有效估计。为具有潜在变量的比例风险模型建立了后验性理论。通过仿真研究验证了所提出方法的经验性能。然后将提出的模型应用于阿尔茨海默病神经影像学倡议数据集的研究，以研究 APOE-$\epsilon 4$ 直接或通过潜在介质（如海马体萎缩、心室扩张和认知障碍）影响疾病进展的等位基因。