How to perform a meta-analysis with R: a practical tutorial

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.

Figure 1

Printout of metabin function.

Figure 2

Forest plot showing the results of fixed effect and random effects meta-analysis (available case analysis).

Figure 3

Forest plot showing the subgroup analysis by presence of missing data (available case analysis).

Figure 4

Summary risk ratios according to different assumptions about mechanism of missingness.

Figure 5

Funnel plot and various methods to evaluate funnel plot asymmetry.