Bayesian hierarchical models for high-dimensional mediation analysis with coordinated selection of correlated mediators.

ABSTRACT: We consider Bayesian high-dimensional mediation analysis to identify among a large set of correlated potential mediators the active ones that mediate the effect from an exposure variable to an outcome of interest. Correlations among mediators are commonly observed in modern data analysis; examples include the activated voxels within connected regions in brain image data, regulatory signals driven by gene networks in genome data, and correlated exposure data from the same source. When correlations are present among active mediators, mediation analysis that fails to account for such correlation can be suboptimal and may lead to a loss of power in identifying active mediators. Building upon a recent high-dimensional mediation analysis framework, we propose two Bayesian hierarchical models, on

SUBMITTER: Song Y 

PROVIDER: S-EPMC9257993 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature