Abstract

Objective Meta-analysis is of fundamental importance to obtain an unbiased assessment of the available evidence. In general, the use of meta-analysis has been increasing over the last three decades with mental health as a major research topic. It is then essential to well understand its methodology and interpret its results. In this publication, we describe how to perform a meta-analysis with the freely available statistical software environment R, using a working example taken from the field of mental health. Methods R package meta is used to conduct standard meta-analysis. Sensitivity analyses for missing binary outcome data and potential selection bias are conducted with R package metasens. All essential R commands are provided and clearly described to conduct and report analyses. Results The working example considers a binary outcome: we show how to conduct a fixed effect and random effects meta-analysis and subgroup analysis, produce a forest and funnel plot and to test and adjust for funnel plot asymmetry. All these steps work similar for other outcome types. Conclusions R represents a powerful and flexible tool to conduct meta-analyses. This publication gives a brief glimpse into the topic and provides directions to more advanced meta-analysis methods available in R.

Keywords

Funnel plotMeta-analysisPublication biasComputer scienceOutcome (game theory)Plot (graphics)Forest plotData scienceSubgroup analysisStatisticsData miningMedicineMathematics

MeSH Terms

Biomedical ResearchHumansMeta-Analysis as TopicSoftware

Affiliated Institutions

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Publication Info

Year
2019
Type
article
Volume
22
Issue
4
Pages
153-160
Citations
4936
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

4936
OpenAlex
609
Influential
4117
CrossRef

Cite This

Sara Balduzzi, Gerta Rücker, Guido Schwarzer (2019). How to perform a meta-analysis with R: a practical tutorial. Evidence-Based Mental Health , 22 (4) , 153-160. https://doi.org/10.1136/ebmental-2019-300117

Identifiers

DOI
10.1136/ebmental-2019-300117
PMID
31563865
PMCID
PMC10231495

Data Quality

Data completeness: 86%