Title:BayesMetaNMA: An Interactive R/Shiny Application for Bayesian Pairwise and Network Meta-Analysis
Year: 2025
Type: article
Abstract: Background Bayesian methods provide a flexible framework for meta-analysis, particularly for network meta-analysis (NMA), which enables simultaneous comparison of multiple interventions and robust modeling of heterogeneity. However, implementing Bayesian meta-analysis often requires advanced programming skills. Methods We developed BayesMetaNMA, an open-source R/Shiny application for Bayesian pairwise and network meta-analyses. The application uses rjags for Markov Chain Monte Carlo (MCMC) estimation, netmeta for network structure visualization and frequentist NMA outputs, and meta for conventional pairwise analyses. Users can select from various effect measures (standardized mean difference, mean difference, odds ratio, risk ratio, hazard ratio), set MCMC parameters, and define prior distributions. Outputs include MCMC diagnostics, posterior summaries, study-level estimates, and network-specific analyses such as ranking tables and inconsistency checks. Results BayesMetaNMA produces comprehensive outputs for both pairwise and network models, including convergence diagnostics (trace, density, autocorrelation, Gelman–Rubin plots), pooled and treatment-specific effects, heterogeneity estimates, and optional meta-regression. All plots and summaries are downloadable. Conclusions BayesMetaNMA provides a user-friendly interface for applying Bayesian methods to evidence synthesis without extensive coding. By integrating established R packages in an interactive workflow, it facilitates robust Bayesian analyses for a wide range of research applications.
Source: F1000Research
Authors: Laiba Khan, Maham khan, Mahmood Ahmad, Joanne Lac
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,350
Funder: University College London
OpenAlex ID: W4416430613