hdbm: High Dimensional Bayesian Mediation Analysis
Perform mediation analysis in the presence of high-dimensional
mediators based on the potential outcome framework. High dimensional
Bayesian mediation (HDBM), developed by Song et al (2018)
<doi:10.1101/467399>, relies on two Bayesian sparse linear mixed models to
simultaneously analyze a relatively large number of mediators for a
continuous exposure and outcome assuming a small number of mediators are
truly active. This sparsity assumption also allows the extension of
univariate mediator analysis by casting the identification of active
mediators as a variable selection problem and applying Bayesian methods
with continuous shrinkage priors on the effects.
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