Reporting and interpreting meta-analysis software choices: practical guidance for researchers, reviewers, and editors

Meta-analysis results depend on statistical assumptions that are partly determined by the software used to conduct the analysis. Meta-epidemiological studies show that Review Manager remains the most frequently reported software in published intervention meta-analyses, but software versions, estimators, interval methods, heterogeneity specifications, and other analytical outputs are often incompletely described. This methods-focused narrative tutorial provides a structured comparison of selected meta-analysis software platforms used in health-related evidence synthesis, including Review Manager, Meta-DiSc, Comprehensive Meta-Analysis, R, and Stata. The comparison focuses on how these platforms implement or restrict key modeling choices relevant to intervention/effect-size meta-analysis and diagnostic test accuracy meta-analysis-including between-study variance estimation, confidence-interval construction, prediction intervals, sparse-data handling, meta-regression, and hierarchical diagnostic accuracy modeling-as well as how they differ in transparency and reproducibility. The article also distinguishes legacy graphical interfaces, updated web-based platforms, commercial software, and script-based environments, emphasizing how differences in flexibility, auditability, and reporting can affect interpretation. The aim is to help authors, reviewers, editors, and clinical readers recognize when software choices affect model specification, uncertainty quantification, heterogeneity assessment, sparse-data handling, diagnostic accuracy modeling, and reproducibility. Software should therefore be reported as part of the statistical specification of a meta-analysis, not only as a computational detail.