论文标题
带有多个暴露和多个调解人的调解分析
Mediation Analysis with Multiple Exposures and Multiple Mediators
论文作者
论文摘要
在线性结构方程建模框架下，提出了一种用于多个暴露，多个介体和连续标量结果的中介分析方法。它假设存在正交成分，这些成分在结果上表现出平行的中介机制，因此被称为主要成分调解分析（PCMA）。引入了基于似然的估计器，以同时估计组件投影和效果参数。估计量的渐近分布是用于低维数据的。引入了引导程序进行推理。仿真研究说明了所提出的方法的出色表现。该框架应用于阿尔茨海默氏病神经影像倡议（ADNI）的蛋白质组学成像数据集（ADNI），所提出的框架确定了蛋白质的沉积 - 脑萎缩 - 记忆缺陷机制与现有知识一致，并通过从不同模态中整合收集的数据来暗示潜在的AD病理学。
A mediation analysis approach is proposed for multiple exposures, multiple mediators, and a continuous scalar outcome under the linear structural equation modeling framework. It assumes that there exist orthogonal components that demonstrate parallel mediation mechanisms on the outcome, and thus is named Principal Component Mediation Analysis (PCMA). Likelihood-based estimators are introduced for simultaneous estimation of the component projections and effect parameters. The asymptotic distribution of the estimators is derived for low-dimensional data. A bootstrap procedure is introduced for inference. Simulation studies illustrate the superior performance of the proposed approach. Applied to a proteomics-imaging dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the proposed framework identifies protein deposition - brain atrophy - memory deficit mechanisms consistent with existing knowledge and suggests potential AD pathology by integrating data collected from different modalities.
