meta (version 4.18-2)

meta-package: meta: Brief overview of methods and general hints


R package meta is a user-friendly general package providing standard methods for meta-analysis and supporting Schwarzer et al. (2015),



R package meta (Schwarzer, 2007; Balduzzi et al., 2019) provides the following statistical methods for meta-analysis.

  1. 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)

  2. Several plots for meta-analysis:

  3. 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

  4. Cumulative meta-analysis (metacum) and leave-one-out meta-analysis (metainf)

  5. Meta-regression (metareg)

  6. 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

  7. Prediction interval for the treatment effect of a new study (Higgins et al., 2009); see argument prediction in meta-analysis functions, e.g., metagen

  8. Hartung-Knapp method for random effects meta-analysis (Hartung & Knapp, 2001a,b); see argument hakn in meta-analysis functions, e.g., metagen

  9. 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

  10. 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, 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 ( 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 ( 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 = "meta") if you do not use RStudio,

  • send an email to Guido Schwarzer if you use RStudio.

The development version of meta is available on GitHub


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