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; Balduzzi et al., 2019) 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
trim-and-fill method (
trimfill.default) to evaluate bias in meta-analysis
Cumulative meta-analysis (
leave-one-out meta-analysis (
Import data from Review Manager 5 (
metacr to conduct meta-analysis for a
single comparison and outcome from a Cochrane review
Prediction interval for the treatment effect of a new study
(Higgins et al., 2009); see argument
meta-analysis functions, e.g.,
Hartung-Knapp method for random effects meta-analysis
(Hartung & Knapp, 2001a,b); see argument
meta-analysis functions, e.g.,
Various estimators for the between-study variance
τ^2 in a random effects model (Veroniki et al., 2016);
method.tau in meta-analysis functions, e.g.,
Generalised linear mixed models (
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). The other two commands implement the recommendations of the Institute for Quality and Efficiency in Health Care (IQWiG), Germany accordinging to General Methods 5 and 6, respectively (https://www.iqwig.de/en/methods/methods-paper.3020.html).
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.
Balduzzi et al. (2019) is the preferred citation in publications
for meta. Type
citation("meta") for a BibTeX entry of
To report problems and bugs
bug.report(package = "meta") if you do not use
send an email to Guido Schwarzer email@example.com if you use RStudio.
The development version of meta is available on GitHub https://github.com/guido-s/meta.
R package meta imports R functions from metafor (Viechtbauer, 2010) to
estimate the between-study variance τ^2,
estimate generalised linear mixed models.
Guido Schwarzer firstname.lastname@example.org
Balduzzi S, Rücker G, Schwarzer G (2019): How to perform a meta-analysis with R: a practical tutorial. Evidence-Based Mental Health, 22, 153–160.
Hartung J, Knapp G (2001a): On tests of the overall treatment effect in meta-analysis with normally distributed responses. Statistics in Medicine, 20, 1771–82
Hartung J, Knapp G (2001b): A refined method for the meta-analysis of controlled clinical trials with binary outcome. Statistics in Medicine, 20, 3875–89
Higgins JPT, Thompson SG, Spiegelhalter DJ (2009): A re-evaluation of random-effects meta-analysis. Journal of the Royal Statistical Society: Series A, 172, 137–59
Schwarzer G (2007): meta: An R package for meta-analysis. R News, 7, 40–5
Schwarzer G, Carpenter JR and Rücker G (2015): Meta-Analysis with R (Use-R!). Springer International Publishing, Switzerland
Veroniki AA, Jackson D, Viechtbauer W, Bender R, Bowden J, Knapp G, et al. (2016): Methods to estimate the between-study variance and its uncertainty in meta-analysis. Research Synthesis Methods, 7, 55–79
Viechtbauer W (2010): Conducting Meta-Analyses in R with the metafor Package. Journal of Statistical Software, 36, 1–48
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