Keywords
Bayesian Meta-Analysis, Network Meta-Analysis, Pairwise Meta-Analysis, R, Shiny, JAGS, MCMC, netmeta, meta, Open Science, Software Tool
-
Views
-
Downloads
How to cite this article
Khan L, khan M, ahmad M and Lac J. Title:BayesMetaNMA: An Interactive R/Shiny Application for Bayesian Pairwise and Network Meta-Analysis [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:924 (https://doi.org/10.12688/f1000research.169341.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
Export Citation
Sciwheel
EndNote
Ref. Manager
Bibtex
ProCite
Sente
▬
✚
Software Tool Article
Title:BayesMetaNMA: An Interactive R/Shiny Application for Bayesian Pairwise and Network Meta-Analysis
[version 1; peer review: awaiting peer review]
PUBLISHED 15 Sep 2025
OPEN PEER REVIEW
REVIEWER STATUS
This article is included in the RPackage gateway.
This article is included in the University College London collection.
Abstract
Corresponding author:
Joanne Lac
Competing interests:
No competing interests were disclosed.
Grant information:
The author(s) declared that no grants were involved in supporting this work.
Copyright:
© 2025 Khan L et al.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite: Khan L, khan M, ahmad M and Lac J. Title:BayesMetaNMA: An Interactive R/Shiny Application for Bayesian Pairwise and Network Meta-Analysis [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:924 (https://doi.org/10.12688/f1000research.169341.1)
First published: 15 Sep 2025, 14:924 (https://doi.org/10.12688/f1000research.169341.1)
Latest published: 15 Sep 2025, 14:924 (https://doi.org/10.12688/f1000research.169341.1)
Introduction
Meta-analysis combines results from multiple studies to produce a pooled estimate of an effect, aiding evidence-based decision-making. While frequentist approaches remain common, Bayesian methods offer advantages such as incorporating prior knowledge, explicit probabilistic interpretation, and flexible modeling of complex evidence structures (Spiegelhalter et al., 2004; Dias et al., 2013).
Network meta-analysis (NMA) extends pairwise meta-analysis by synthesizing evidence from multiple interventions, even in the absence of direct head-to-head trials. Despite their benefits, Bayesian NMAs typically require coding in specialized environments such as JAGS or Stan, which can be a barrier for applied researchers.
BayesMetaNMA addresses this challenge by providing a graphical interface for Bayesian pairwise and network meta-analyses. Built with R/Shiny, it integrates Bayesian computation, network visualization, and diagnostic tools into a single interactive platform.
Methods
Implementation
BayesMetaNMA is implemented in R (R Core Team, 2023) (≥4.0.0) using the Shiny framework (Chang et al., 2023) (≥1.7.0) and bs4Dash for interface design. Core dependencies include: rjags, coda, netmeta (Rücker et al., 2015), meta, igraph, ggplot2, dmetar, and grid.
Bayesian model structures
Pairwise Random-Effects Model:
Study-specific effects θ_i are modeled as: y_i ~ N(θ_i, σ_i2), θ_i ~ N(μ, τ2) with priors μ ~ N(μ0, σ_μ2), τ ~ Uniform(0, τ_max).
Network Random-Effects Model:
Treatment effects μ_j are estimated relative to a reference treatment: y_i ~ N(θ_i, σ_i2), θ_i = (μ_t1[i] − μ_t2[i]) + δ_i, δ_i ~ N(0, τ2), μ_j ~ N(μ0, σ_μ2), τ ~ Uniform(0, τ_max) (Plummer, 2003).
Data input
Data are uploaded as CSV:
- Pairwise: Study, Effect, SE
- Network: Study, Treatment1, Treatment2, Effect, SE (log transformation required for OR, RR, HR)
Example datasets are provided for all supported effect measures.
Workflow
1. Data & Settings: Upload dataset or load an example; choose analysis type and summary measure.
2. Priors & MCMC: Specify iterations, burn-in, and prior parameters.
3. Run Analysis: Execute Bayesian estimation via JAGS.
4. Explore Outputs: Convergence plots, forest plots, network diagrams, posterior summaries, heterogeneity estimates.
Use cases
Example 1
– Pairwise Meta-Analysis of SMD: Data loaded, priors specified, results examined via convergence diagnostics and posterior summaries.
Example 2
– Network Meta-Analysis of logOR: Outputs include Bayesian treatment effect estimates, heterogeneity parameters, network rankings, and probability calculations.
Discussion
Strengths
Unified Bayesian platform for both pairwise and network analyses; comprehensive diagnostics; rich visual outputs; configurable priors and MCMC settings; open-source.
Download
Export To
metrics
| Views | Downloads | |
|---|---|---|
| F1000Research | - | - |
|
PubMed Central
Data from PMC are received and updated monthly.
|
- | - |
Citations
CITE
how to cite this article
Khan L, khan M, ahmad M and Lac J. Title:BayesMetaNMA: An Interactive R/Shiny Application for Bayesian Pairwise and Network Meta-Analysis [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:924 (https://doi.org/10.12688/f1000research.169341.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
track
receive updates on this article
Track an article to receive email alerts on any updates to this article.
Open Peer Review
Current Reviewer Status:
AWAITING PEER REVIEW
AWAITING PEER REVIEW
Key to Reviewer Statuses
VIEW
HIDE
ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations
A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions
Comments on this article Comments (0)