Fixed-effects meta-analyses assume that the effect size d is identical in all studies. In contrast, random-effects meta-analyses assume that effects vary according to a normal distribution with mean d and standard deviation tau. Both models can be compared in a Bayesian framework by assuming specific prior distribution for d and tau. Given the posterior model probabilities, the evidence for or against an effect (i.e., whether d = 0) and the evidence for or against random effects can be evaluated (i.e., whether tau = 0). By using Bayesian model averaging (i.e., inclusion Bayes factors), both types of tests can be performed by marginalizing over the other question. Most importantly, this allows to test whether an effect exists while accoungting for uncertainty whether study heterogeneity exists or not.
The most general functions in
meta_default, which fit random- and fixed-effects models, compute the inclusion Bayes factor for the presence of an effect and the averaged posterior distribution of the mean effect d (which accounts for uncertainty regarding study heterogeneity).
meta_random fit a single meta-analysis models. The model-specific posteriors for d can be averaged by
bma and inclusion Bayes factors be computed by
inclusion. Finally, the function
prior facilitates the construction and visual inspection of prior distributions.
metaBMA requires the software JAGS. To install the latest stable release of
metaBMA from GitHub, run:
### Dependencies: # install.packages("mvtnorm", "runjags", "LaplacesDemon", "logspline") if (!require("devtools")) install.packages("devtools") devtools::install_github("danheck/metaBMA", build_vignettes = TRUE)
If you use metaBMA, please cite the software as follows:
Heck, D. W., Gronau, Q. F., & Wagenmakers, E.-J. (2017). metaBMA: Bayesian model averaging for random and fixed effects meta-analysis. Retrieved from https://github.com/danheck/metaBMA. doi:10.5281/zenodo.835494