Description
High Dimensional Bayesian Mediation Analysis.
Description
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.
README.md
hdbm is a Bayesian inference method that uses continuous shrinkage priors for high-dimensional mediation analysis, developed by Song et al (2018). hdbm provides estimates for the regression coefficients as well as the posterior inclusion probability for ranking mediators.
Install
You can install hdbm via CRAN
install.packages("hdbm")
Or devtools
devtools::install_github("umich-cphds/hdbm", build_opts = c())
If you wish to install the package via devtools, you will need a C++ compiler installed. This can be accomplished by installing Rtools on Windows and Xcode on MacOS.
Example
Taken from the hdbm help file
library(hdbm)
Y <- hdbm.data$y
A <- hdbm.data$a
# grab the mediators from the example data.frame
M <- as.matrix(hdbm.data[, paste0("m", 1:100)], nrow(hdbm.data))
# We just include the intercept term in this example.
C <- matrix(1, nrow(M), 1)
beta.m <- rep(0, 100)
alpha.a <- rep(0, 100)
set.seed(1245)
output <- hdbm(Y, A, M, C, C, beta.m, alpha.a, burnin = 3000, ndraws = 100)
# Which mediators are active?
active <- which(colSums(output$r1 * output$r3) > 50)
colnames(M)[active]
Reference
Yanyi Song, Xiang Zhou et al. Bayesian Shrinkage Estimation of High Dimensional Causal Mediation Effects in Omics Studies. bioRxiv 467399