regression, outlier assessment, sensitivity analysis and evaluating the publication bias. Statistical software and programs are available to enable the effective and accurate conduct of the meta-analysis. RevMan, STATA, SPSS, R and Comprehensive Meta-Analysis (CMA) are some of the most well-known tools. Among these RevMan and R software are free of cost for researchers worldwide. While RevMan operates by graphic user interface (GUI)/Menu-driven user interface, R software is a command line interface (CLI). Though the CLI makes R cumbersome for beginners, its customisability expands the scope of the analysis that can be undertaken with R. For instance, meta-regression and network meta-analysis are not possible with RevMan, while it is feasible in R. There are several other advantages, including but not limited to a better assessment of publication bias, and computation of prediction intervals. Thus, R has a significant advantage over other software for meta-analysis. However, the cumbersome and CLI nature of the R can make it difficult for the researchers, especially the clinical and medical researchers, to accept, adopt and implement the R software in their meta-analysis.
An easy-to-use, stepwise guide to assist the clinical and medical researchers involved in EBM and EBPH would improve the quality and quantity of the evidence synthesis work done. Moreover, R has a very rewarding learning curve. Once comfortable with the software, one can use it for various uses, including SRMAs, health economics and outcomes research, bioinformatics, and other analyses in primary research.
Studies in the past have reported stepwise guides by including the codes and applications for conducting the meta-analysis in R software [12–16]. However, they were limited to the two-group meta-analysis with binary outcomes [14,16] or continuous outcomes only in pre-post designs [15]. Several important aspects like heterogeneity assessment and exploration, publication bias assessment and sensitivity analysis have been skipped or explained inadequately [12–15]. With the rise in the single group meta-analysis (proportion and continuous outcome), it is imperative to disseminate the stepwise R guide to enable such meta-analysis. Newer methods to detect and report publication bias (Doi plots and LFK index) [17], steps for the outlier detections, and prediction intervention calculations [18] were also not reported in the previous studies. Hence, the current article has been framed to address the above gaps and provide a stepwise guide from installing R, along with R codes for the standard and newer analyses for single-group and two-group meta-analyses (proportion/binary and continuous outcome).
Learning with an example
This article focuses on assisting researchers who have never used R or have no knowledge about R, but are eager to follow the steps closely to learn and perform their first meta-analysis using R. We have summarised the steps in Figure 1. Though this may seem daunting, it is explained in simple words in the subsequent sections. It should serve as a good recap once the reader has finished reading the article, alongside being a primer to the workflow.