Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on
how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered,
including calculation and pooling of outcome measures, forest plots, heterogeneity
diagnostics, subgroup analyses, meta-regression, methods to control for publication bias,
risk of bias assessments and plotting tools. Advanced but highly relevant topics such as
network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis …

Scientific synthesis is a diverse field of contemporary science. Syntheses advance
knowledge in many domains and can include data compilation, theory syntheses, methods
contrasts, and systematic reviews with meta-analyses through an integrated and big-picture
view of evidence (Halpern et al. 2020). All these knowledge tools are typically strongly
supported by statistical software including the open-source programming language R.
Within this environment, there are nearly 100 packages to support meta-analyses each with …