High-dimensional multivariate mediation with application to neuroimaging data.

ABSTRACT: Mediation analysis is an important tool in the behavioral sciences for investigating the role of intermediate variables that lie in the path between a treatment and an outcome variable. The influence of the intermediate variable on the outcome is often explored using a linear structural equation model (LSEM), with model coefficients interpreted as possible effects. While there has been significant research on the topic, little work has been done when the intermediate variable (mediator) is a high-dimensional vector. In this work, we introduce a novel method for identifying potential mediators in this setting called the directions of mediation (DMs). DMs linearly combine potential mediators into a smaller number of orthogonal components, with components ranked based on the proportion of the

SUBMITTER: Chen OY 

PROVIDER: S-EPMC5862274 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature