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 (metacont)
Meta-analysis of binary outcome data (metabin)
Meta-analysis of incidence rates (metainc)
Generic inverse variance meta-analysis (metagen)
Meta-analysis of single correlations (metacor)
Meta-analysis of single means (metamean)
Meta-analysis of single proportions (metaprop)
Meta-analysis of single incidence rates (metarate)
Several plots for meta-analysis:
Forest plot (forest.meta, forest.metabind)
Funnel plot (funnel.meta)
Galbraith plot / radial plot (radial.meta)
L'Abbe plot for meta-analysis with binary outcome data
(labbe.metabin, labbe.default)
Baujat plot to explore heterogeneity in meta-analysis
(baujat.meta)
Bubble plot to display the result of a meta-regression
(bubble.metareg)
Statistical tests for funnel plot asymmetry
(metabias.meta, metabias.rm5) and
trim-and-fill method (trimfill.meta,
trimfill.default) to evaluate bias in meta-analysis
Cumulative meta-analysis (metacum) and
leave-one-out meta-analysis (metainf)
Meta-regression (metareg)
Import data from Review Manager 5 (read.rm5);
see also 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 prediction in
meta-analysis functions, e.g., metagen
Hartung-Knapp method for random effects meta-analysis
(Hartung & Knapp, 2001a,b); see argument hakn in
meta-analysis functions, e.g., metagen
Various estimators for the between-study variance
\(\tau^2\) in a random effects model (Veroniki et al., 2016);
see argument method.tau in meta-analysis functions, e.g.,
metagen
Generalised linear mixed models (metabin,
metainc, metaprop, and
metarate)
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.
See 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:
settings.meta("revman5")
settings.meta("jama")
settings.meta("iqwig5")
settings.meta("iqwig6")
settings.meta("geneexpr")
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
Medical Association
(https://jamanetwork.com/journals/jama/pages/instructions-for-authors/). Study
labels according to JAMA guidelines can be generated using
JAMAlabels.
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.
In addition, 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)
Type 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
this publication.
To report problems and bugs
type bug.report(package = "meta") if you do not use
RStudio,
send an email to Guido Schwarzer sc@imbi.uni-freiburg.de if you use RStudio.
The development version of meta is available on GitHub https://github.com/guido-s/meta/.
Balduzzi S, R<U+00FC>cker 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 R<U+00FC>cker 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