Mastering meta-analysis in R: a practical review and recommendations

Meta-analysis is a powerful statistical method that synthesizes results from multiple independent studies to generate an overall quantitative estimate of effect sizes.With the growing demand for reproducible and transparent research, R has become a preferred tool for conducting meta-analyses.This manuscript reviews the fundamental principles of meta-analysis and demonstrates its practical implementation in R using several packages.We describe how to compute effect sizes, choose appropriate models, assess heterogeneity, and diagnose publication bias.In addition, we explore alternative metaanalytic approaches -including network meta-analysis, cumulative meta-analysis, individual participant data meta-analysis, Bayesian meta-analysis, and multivariate meta-analysis -and provide an overview of the R packages that support these methods.The manuscript presents examples, tables, and figures alongside recent references to guide researchers in applying meta-analytic techniques effectively.