Perform mediation analysis in the presence of high-dimensional mediators based on the potential outcome framework. Bayesian Mediation Analysis (BAMA), developed by Song et al (2019) and Song et al (2020), 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.
You can install
bama via CRAN
devtools::install_github("umich-cphds/bama", build_opts = c())
The Github version may contain new features or bug fixes not yet present on CRAN, so if you are experiencing issues, you may want to try the Github version of the package.
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.
This example is taken from the
bama help file to help you get started
using the method. Please check the documentation of the function by
?bama::bama, and the vignette by typing
bama includes an example dataset,
bama.data. It is a
with a numeric response
y, numeric exposure
a and 100 numeric
m1, m2, ..., m100.
We recommend using much larger numbers for
example (30000, 35000).
library(bama) Y <- bama.data$y A <- bama.data$a # grab the mediators from the example data.frame M <- as.matrix(bama.data[, paste0("m", 1:100)], nrow(bama.data)) # We just include the intercept term in this example as we have no covariates C1 <- matrix(1, 1000, 1) C2 <- matrix(1, 1000, 1) beta.m <- rep(0, 100) alpha.a <- rep(0, 100) out <- bama(Y = Y, A = A, M = M, C1 = C1, C2 = C2, method = "BSLMM", seed = 1234, burnin = 1000, ndraws = 1100, weights = NULL, inits = NULL, control = list(k = 2, lm0 = 1e-04, lm1 = 1, l = 1)) # The package includes a function to summarise output from 'bama' summary <- summary(out) head(summary) # Product Threshold Gaussian ptgmod = bama(Y = Y, A = A, M = M, C1 = C1, C2 = C2, method = "PTG", seed = 1234, burnin = 1000, ndraws = 1100, weights = NULL, inits = NULL, control = list(lambda0 = 0.04, lambda1 = 0.2, lambda2 = 0.2)) mean(ptgmod$beta.a) apply(ptgmod$beta.m, 2, mean) apply(ptgmod$alpha.a, 2, mean) apply(ptgmod$betam_member, 2, mean) apply(ptgmod$alphaa_member, 2, mean) # Gaussian Mixture Model gmmmod = bama(Y = Y, A = A, M = M, C1 = C1, C2 = C2, method = "GMM", seed = 1234, burnin = 1000, ndraws = 1100, weights = NULL, inits = NULL, control = list(phi0 = 0.01, phi1 = 0.01)) mean(gmmmod$beta.a) apply(gmmmod$beta.m, 2, mean) apply(gmmmod$alpha.a, 2, mean) mean(gmmmod$sigma.sq.a) mean(gmmmod$sigma.sq.e) mean(gmmmod$sigma.sq.g)
Song, Y, Zhou, X, Zhang, M, et al. Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies. Biometrics. 2019; 1-11.
Song, Yanyi, Xiang Zhou, Jian Kang, Max T. Aung, Min Zhang, Wei Zhao, Belinda L. Needham et al. “Bayesian Sparse Mediation Analysis with Targeted Penalization of Natural Indirect Effects.” arXiv preprint arXiv:2008.06366 (2020).
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