74: Identifying Shared Pathways that Maximize the Indirect Effect between Exposure and Outcome

Thomas M. Menino Convention & Exhibition Center 

Mediation analysis is often used in the behavioral sciences to investigate the role of intermediate variables that lie in the path between an exposure and an outcome. In recent years, there has been an increased interest in studying high-dimensional mediators (e.g., imaging and genomics data). In this talk, we describe an approach towards high-dimensional mediation that identifies latent low-dimensional representations of the set of potential mediators that maximize the indirect effect. We describe an estimation technique based on solving a generalized eigenvalue problem. We further explore links to canonical correlation analysis and partial least squares. The proposed methodology is flexible enough to work with multivariate scalar and functional data. We provide illustrations of both. The methods are applied to study how the relationship between maternal exposure and child behavioral outcomes is mediated by brain volume changes across the early years.

Mediation analysis

Functional data analysis

Imaging 

Section on Statistics in Imaging