GuidelineEuropean journal of clinical pharmacology2026
Reporting and interpreting meta-analysis software choices: practical guidance for researchers, reviewers, and editors.
Guideline in European journal of clinical pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
42440169What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.