Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Department of Biostatistics and Epidemiology, University of California, Berkeley, Berkeley, California, USA.
Hum Brain Mapp. 2022 Jun 1;43(8):2519-2533. doi: 10.1002/hbm.25800. Epub 2022 Feb 7.
Motivated by an imaging proteomics study for Alzheimer's disease (AD), in this article, we propose a mediation analysis approach with high-dimensional exposures and high-dimensional mediators to integrate data collected from multiple platforms. The proposed method combines principal component analysis with penalized least squares estimation for a set of linear structural equation models. The former reduces the dimensionality and produces uncorrelated linear combinations of the exposure variables, whereas the latter achieves simultaneous path selection and effect estimation while allowing the mediators to be correlated. Applying the method to the AD data identifies numerous interesting protein peptides, brain regions, and protein-structure-memory paths, which are in accordance with and also supplement existing findings of AD research. Additional simulations further demonstrate the effective empirical performance of the method.
受阿尔茨海默病（AD）成像蛋白质组学研究的启发，本文提出了一种中介分析方法，该方法使用高维暴露和高维中介来整合来自多个平台的数据。所提出的方法将主成分分析与惩罚最小二乘估计相结合，用于一组线性结构方程模型。前者降低了维度，并产生了暴露变量的不相关线性组合，而后者则在允许中介相关的同时实现了同时路径选择和效应估计。将该方法应用于 AD 数据，确定了许多有趣的蛋白质肽、大脑区域和蛋白质结构记忆路径，这些结果与 AD 研究的现有发现一致，并补充了这些发现。额外的模拟进一步证明了该方法的有效经验性能。
