R package meta is a user-friendly general package providing standard methods for meta-analysis and supporting Schwarzer et al. (2015), https://www.springer.com/gp/book/9783319214153.
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, https://training.cochrane.org/online-learning/core-software-cochrane-reviews/revman) 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
labels according to JAMA guidelines can be generated using
The next 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/about-us/methods/methods-paper/).
The last setting can be used to print p-values in scientific notation and to suppress the calculation of confidence intervals for the between-study variance.
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 firstname.lastname@example.org 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 email@example.com
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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