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-analysisCumulative 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 reviewPrediction 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/). 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