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Correspondence to Dr Sara Balduzzi, Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg im Breisgau 79085, Germany; [email protected]
Introduction
Evidence synthesis is the basis for decision making in a large range of applications including the fields of medicine, psychology and economics.1 Systematic reviews and meta-analysis as statistical method to combine results are cornerstones to yield an unbiased assessment of the available evidence.2 In general, the use of meta-analysis has been increasing over the last three decades with mental health as a major research topic, for example, in Cochrane reviews.3
The freely available statistical software environment R4 (https://www.r-project.org) and the commercial software Stata5 provide the largest collection of statistical methods for meta-analysis. RStudio (https://www.rstudio.com/) is a popular and highly recommended integrated development environment for R providing menu-driven tools for plotting, a history of previous R commands, data management and the installation and update of R packages.
An introduction to meta-analysis with Stata has been published in Evidence-Based Mental Health 6 with specific focus on the challenges in the conduct and interpretation of meta-analysis when outcome data are missing and when small-study effects occur.
In this publication, we replicate these analyses in R using the packages meta7 and metasens.8
In the following, we present R commands to:
Install R packages for meta-analysis.
Conduct a meta-analysis when the outcome of interest is binary.
Assess the impact of missing outcome data.
Assess and account for small-study effects.
Methods
Before conducting a meta-analysis, the R packages meta and metasens need to be installed,9 which include all functions to perform the analyses and to create the figures presented in this publication.
insta l l .packages(c( “ meta ”, “ metasens ” ))
Using this first R command, we would like to mention three general properties of R commands. First, we use brackets in order to execute an R function, here instal l .packages(); the command instal l .packages (without brackets) would show the definition of the function. Second, the argument of an R function can be another R function; here we use the function c() to combine the names of R packages. Third, function arguments are separated by commas that is...