R package meta is a user-friendly general package providing standard methods for meta-analysis and supporting Schwarzer et al. (2015), http://meta-analysis-with-r.org/.
R package meta (Schwarzer, 2007) provides the following statistical methods for meta-analysis.
Fixed effect and random effects model:
Meta-analysis of continuous outcome data (
Meta-analysis of binary outcome data (
Meta-analysis of incidence rates (
Generic inverse variance meta-analysis (
Meta-analysis of single correlations (
Meta-analysis of single means (
Meta-analysis of single proportions (
Meta-analysis of single incidence rates (
Several plots for meta-analysis:
Forest plot (
Funnel plot (
Galbraith plot / radial plot (
L'Abbe plot for meta-analysis with binary outcome data (
Baujat plot to explore heterogeneity in meta-analysis (
Bubble plot to display the result of a meta-regression (
Statistical tests for funnel plot asymmetry
metabias) and trim-and-fill method
trimfill) to evaluate bias in meta-analysis
Import data from 'RevMan 5' (
read.rm5); see also
metacr to conduct meta-analysis for a single
comparison and outcome from a Cochrane review
Prediction interval, Hartung-Knapp and Paule-Mandel method for
random effects model (see arguments
method.tau, respectively, in meta-analysis
functions listed under 1. Fixed effect and random effects model)
Cumulative meta-analysis (
leave-one-out meta-analysis (
metareg); if R package
metafor is installed
Generalised linear mixed models (
metarate); if R packages metafor, lme4,
numDeriv, and BiasedUrn are installed
The following more advanced statistical methods are provided by add-on R packages:
Frequentist methods for network meta-analysis (R package netmeta)
Advanced methods to model and adjust for bias in meta-analysis (R package metasens)
Results of several meta-analyses can be combined with
metabind. This is, for example, useful to generate a
forest plot with results of subgroup analyses.
settings.meta to learn how to print and specify
default meta-analysis methods used during your R session. For
example, the function can be used to specify general settings:
The first command can be used to reproduce meta-analyses from Cochrane reviews conducted with Review Manager 5 (RevMan 5, http://community.cochrane.org/tools/review-production-tools/revman-5) and specifies to use a RevMan 5 layout in forest plots. The second command can be used to generate forest plots following instructions for authors of the Journal of the American Medical Association (http://jamanetwork.com/journals/jama/pages/instructions-for-authors).
settings.meta can be used to change
individual settings. For example, the following R command specifies
the use of the Hartung-Knapp and Paule-Mandel methods, and the
printing of prediction intervals in the current R session for any
meta-analysis generated after execution of this command:
settings.meta(hakn=TRUE, method.tau="PM", prediction=TRUE)
help(package = "meta") for a listing of R functions and
datasets available in meta.
Schwarzer (2007) is the preferred citation in publications for
citation("meta") for a BibTeX entry of this
To report problems and bugs
bug.report(package = "meta") if you do not use
send an email to Guido Schwarzer [email protected] if you use RStudio.
The development version of meta is available on GitHub https://github.com/guido-s/meta.
Guido Schwarzer [email protected]
Schwarzer G (2007), meta: An R package for meta-analysis. R News, 7(3), 40–5. https://cran.r-project.org/doc/Rnews/Rnews_2007-3.pdf
Schwarzer G, Carpenter JR and Rücker G (2015), Meta-Analysis with R (Use-R!). Springer International Publishing, Switzerland. http://www.springer.com/gp/book/9783319214153
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