Doing Meta-Analysis with R: A Hands-On Guide

Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 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.
• 2 Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered.
• 3 A companion R package, dmetar, is introduced at the beginning of the guide.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

This summary is based on publicly available metadata and abstract. For the full research paper, visit the original source: