BHMnet: Bayesian high-dimensional mediation analysis with network information integration for correlated mediators.

Abstract

We consider identifying a small yet meaningful set of active mediators from a high-dimensional pool of potential mediators, commonly derived from "-omics" or imaging data. In these contexts, mediators are often correlated or exist network structures, which present unique opportunities to improve efficacy by using this valuable information. To this aim, we develop a Bayesian method that accommodates both high dimensionality and correlations among the mediators. Our approach flexibly learns the interconnection between the mediators while improving estimation accuracy by incorporating external knowledge about these relationships. Simulation studies demonstrate the effectiveness of the proposed method compared with alternative approaches. The analysis of the environmental toxicity data provides new insights into the intermediate effects of molecular-level traits.