is a R
package that provides a collection of functions for conducting meta-analyses and meta-regressions under a Bayesian context, using JAGS
. The package includes functions for computing various effect size or outcome measures (e.g. odds ratios, mean difference and incidence rate ratio) for different types of data (e.g. binary, continuous and count, respectively), based on Markov Chain Monte Carlo (MCMC) simulations. Users are allowed to select fixed- and random-effects models with different prior distributions for the data and the relevant parameters. Meta-regression can be also performed if the effect of additional covariates are considered. Furthermore, the package provides functions for creating posterior distributions and forest plot to display main model output. Traceplots and some other diagnostic plots are also available for assessing model fit and performance.
works by allowing the user to specify the set of options in a standardised way in the R
command terminal. Currently, bmeta implements 22 models; when the user has selected the required configuration (random vs fixed effects; choice of outcome and prior distributions; presence of covariates),
writes a suitable JAGS
file in the working directory. This is used to call the package R2Jags
and perform the actual analysis, but can also be considered as a sort of "template" - the user can then modify to extend the modelling, modify the priors in a way that is not automatically done by bmeta, or use it for future references.
works in conjunction with the R packages forestplot
; these need to be installed (this step is automatically taken care of by the official CRAN
version. The development version (0.1.2
) is also available and so they need to be installed by the user, e.g. by typing on the R terminal the following commands
install.packages("http://www.statistica.it/gianluca/bmeta/bmeta_0.1.3.tar.gz", repos=NULL, type="source")
(alternatively, you can set the option "dependencies = TRUE" and R will automatically install the dependencies too).
The package is also available from CRAN (version 0.1.1) and so can be installed by typing
in an R terminal.
A full documentation guide available here
, including a complete description of the over 20 models implemented in
, as well as worked out examples.