The bmeta package provides a collection of functions for conducting meta-analyses under
Bayesian context in R. 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 based on MCMC simulations. Users are allowed to fit fixed- and
random-effects models with different priors to the data. Meta-regression can be carried
out if effects of additional covariates are observed. Furthermore, the package provides
functions for creating posterior distribution plots and forest plot to display main model
output. Traceplots and some other diagnostic plots are also available for assessing model
fit and performance.
Details
Package:
bmeta
Type:
Package
Version:
0.1.2
Date:
2016-01-08
License:
GPL2
LazyLoad:
yes
Bayesian meta-analysis is becoming more frequently accepted as a statistical approach
for evidence synthesis from multiple studies in health research. The Bayesian methods
differ inherently from frequentist ones by assuming that model parameters are random
quantities. Therefore, prior distributions for model parameters can be specified, which
are normally based on external evidence. The bmeta function provides 22 models with
commonly used priors for fitting different types of data (i.e. binary, continuous and
count data).
Alex J Sutton and Keith R Abrams.(2001).Bayesian methods in meta-analysis and evidence
synthesis. Statistical Methods in Medical Research,10,277-303.
Welton,N.J., Sutton,A.J., Cooper,N., Abrams,K.R.& Ades,A.E.(2012) Evidence synthesis for
decision making in healthcare. Chichester, UK: John Wiley & Sons, Ltd.