Bayesian Sparse Mediation Analysis with Targeted Penalization of Natural Indirect Effects.

ABSTRACT: Causal mediation analysis aims to characterize an exposure's effect on an outcome and quantify the indirect effect that acts through a given mediator or a group of mediators of interest. With the increasing availability of measurements on a large number of potential mediators, like the epigenome or the microbiome, new statistical methods are needed to simultaneously accommodate high-dimensional mediators while directly target penalization of the natural indirect effect (NIE) for active mediator identification. Here, we develop two novel prior models for identification of active mediators in high-dimensional mediation analysis through penalizing NIEs in a Bayesian paradigm. Both methods specify a joint prior distribution on the exposure-mediator effect and mediator-outcome effect with eithe

SUBMITTER: Song Y 

PROVIDER: S-EPMC8653861 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature