Title:BayesMetaNMA: An Interactive R/Shiny Application for Bayesian Pairwise and Network Meta-Analysis

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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. 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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. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Software Tool Article Title:BayesMetaNMA: An Interactive R/Shiny Application for Bayesian Pairwise and Network Meta-Analysis [version 1; peer review: 1 approved with reservations, 1 not approved] Laiba Khan https://orcid.org/0009-0005-4845-3900 1 , Maham khan https://orcid.org/0009-0002-2994-143X 1 , Mahmood Ahmad https://orcid.org/0000-0001-9107-3704 1 , Joanne Lac https://orcid.org/0009-0004-3533-434X 2 Laiba Khan https://orcid.org/0009-0005-4845-3900 1 , Maham khan https://orcid.org/0009-0002-2994-143X 1 , Mahmood Ahmad https://orcid.org/0000-0001-9107-3704 1 , Joanne Lac https://orcid.org/0009-0004-3533-434X 2 PUBLISHED 15 Sep 2025 Author details Author details 1 Royal Free London NHS Foundation Trust, London, England, UK 2 University College London, London, England, UK Laiba Khan Roles: Investigation, Methodology, Supervision, Validation, Writing – Review & Editing Maham khan Roles: Writing – Review & Editing Mahmood Ahmad Roles: Supervision, Validation, Writing – Original Draft Preparation Joanne Lac Roles: Funding Acquisition, Supervision, Validation OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the RPackage gateway. This article is included in the University College London collection. 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. READ ALL READ LESS Keywords Bayesian Meta-Analysis, Network Meta-Analysis, Pairwise Meta-Analysis, R, Shiny, JAGS, MCMC, netmeta, meta, Open Science, Software Tool Corresponding Author(s) Joanne Lac ( [email protected] ) Close 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: 1 approved with reservations, 1 not approved] . 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_i 2 ), θ_i ~ N(μ, τ 2 ) with priors μ ~ N(μ 0 , σ_μ 2 ), 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_i 2 ), θ_i = (μ_t1[i] − μ_t2[i]) + δ_i, δ_i ~ N(0, τ 2 ), μ_j ~ N(μ 0 , σ_μ 2 ), 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. Limitations Requires JAGS for local use; computation time for large models; limited prior distribution options; meta-regression requires user-prepared covariates; no Bayesian node-splitting. Future directions Expanded prior specification; effect size calculation from raw data; enhanced meta-regression; Bayesian inconsistency evaluation methods. Data availability The datasets supporting the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.16944163 . These datasets include all values required to replicate the analyses reported in the article, including summary data, effect sizes, and variables used for pairwise and network meta-analyses. The BayesMetaNMA software is also openly available under an MIT License at https://doi.org/10.5281/zenodo.16944435 . Software availability Source code available from: https://github.com/laibakhan122/NMABayesianalltypes Archived software available from: https://doi.org/10.5281/zenodo.16944435 License: MIT License Acknowledgments We thank the developers of R, JAGS, and the R packages shiny, bs4Dash, rjags, coda, ggplot2, igraph, netmeta, dmetar, grid, and meta. References Balduzzi S, Rücker G, Schwarzer G: How to perform a meta-analysis with R: a practical tutorial. Evid. Based Ment. Health. 2019; 22 (4): 153–160. PubMed Abstract | Publisher Full Text | Free Full Text Chang W, Cheng J, Allaire JJ, et al. : shiny: Web Application Framework for R. R package version 1.7.5. 2023. Reference Source Dias S, Sutton AJ, Ades AE, et al. : Evidence synthesis for decision making 2: A generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Med. Decis. Mak. 2013; 33 (5): 607–617. PubMed Abstract | Publisher Full Text | Free Full Text Plummer M: JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Proceedings of the 3rd International Workshop on Distributed Statistical Computing. Vienna, Austria: 2003. R Core Team: R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2023. Reference Source Rücker G, Krahn U, König J, et al. : netmeta: Network Meta-Analysis using Frequentist Methods. R package version 2.8-1. 2015. Reference Source Spiegelhalter DJ, Abrams KR, Myles JP: Bayesian approaches to clinical trials and health-care evaluation. John Wiley & Sons; 2004. Ahmad M, Khan L, Khan M, et al. : Datasets for BayesMetaNMA: An Interactive R/Shiny Application. [Data set]. Zenodo. 2025a. Publisher Full Text Ahmad M, Khan L, Khan M, et al. : BayesMetaNMA v1.0.0 (Software). Zenodo. 2025b. Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 15 Sep 2025 ADD YOUR COMMENT Comment Author details Author details 1 Royal Free London NHS Foundation Trust, London, England, UK 2 University College London, London, England, UK Laiba Khan Roles: Investigation, Methodology, Supervision, Validation, Writing – Review & Editing Maham khan Roles: Writing – Review & Editing Mahmood Ahmad Roles: Supervision, Validation, Writing – Original Draft Preparation Joanne Lac Roles: Funding Acquisition, Supervision, Validation Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 15 Sep 2025, 14:924 https://doi.org/10.12688/f1000research.169341.1 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. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS 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: 1 approved with reservations, 1 not approved] . 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. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The 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 approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 15 Sep 2025 Views 0 Cite How to cite this report: Wang QA. Reviewer Report For: Title:BayesMetaNMA: An Interactive R/Shiny Application for Bayesian Pairwise and Network Meta-Analysis [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :924 ( https://doi.org/10.5256/f1000research.186674.r435605 ) The direct URL for this report is: https://f1000research.com/articles/14-924/v1#referee-response-435605 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 02 Jan 2026 Qi-Ang Wang , China University of Mining and Technology, Xuzhou, China Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.186674.r435605 BayesMetaNMA is an open-source R/Shiny application that enables Bayesian pairwise and network meta-analysis through an interactive GUI. It integrates JAGS for MCMC estimation and supports various effect measures, diagnostics, and visualizations. While user-friendly, the tool lacks flexibility in priors, rare-events ... Continue reading READ ALL BayesMetaNMA is an open-source R/Shiny application that enables Bayesian pairwise and network meta-analysis through an interactive GUI. It integrates JAGS for MCMC estimation and supports various effect measures, diagnostics, and visualizations. While user-friendly, the tool lacks flexibility in priors, rare-events support, and comprehensive documentation. Peer review found it underdeveloped compared to existing platforms. 1. The current implementation relies on large-sample normal approximations, which are inappropriate for binary outcomes with rare events. Please incorporate exact likelihood models (e.g., binomial with logit or cloglog links) to handle sparse data more accurately. This would improve the tool's applicability in safety or adverse-event meta-analyses. 2. The software currently offers limited prior choices, restricting users to uniform or vague normal priors. Please expand the prior library to include informed, hierarchical, or mixture priors, and allow users to define custom priors interactively. Provide guidance or warnings when priors are poorly matched to data scale or structure. 3. The literature review part can be improved by including more recent publications on machine learning methods, e.g., Bayesian method or Gaussian method, which can refer to Uncertainty-awarded, high-precision multi-step prediction of structural health monitoring sensor streams under extreme typhoon events: an enhanced Bayesian dynamic linear model leveraging the kernel regression basis function for severe environmental adaptation, Data interpretation and forecasting of SHM heteroscedastic measurements under typhoon conditions enabled by an enhanced Hierarchical sparse Bayesian Learning model with high robustness, Towards high-accuracy data modelling, uncertainty quantification and correlation analysis for SHM measurements during typhoon events using an improved most likely heteroscedastic Gaussian process, Bayesian Network in Structural Health Monitoring: Theoretical Background and Applications Review. 4. The manuscript and tool lack detailed explanations of model assumptions, output interpretation, and diagnostic thresholds. Add embedded help modules, tooltips, and interpretive summaries within the Shiny interface. Include a user manual with worked examples covering common pitfalls, such as non-convergence or inconsistency detection. 5. The NMA model indexes only two-arm trials and ignores correlation between multi-arm study contrasts. Refactor the underlying code to support full multivariate random-effects modeling, ensuring consistency and coherence in treatment rankings. Additionally, implement Bayesian inconsistency detection methods (e.g., node-splitting or design-by-treatment interaction) to enhance model validity. Is the rationale for developing the new software tool clearly explained? Partly Is the description of the software tool technically sound? Partly Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Partly Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Machine learning I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Wang QA. Reviewer Report For: Title:BayesMetaNMA: An Interactive R/Shiny Application for Bayesian Pairwise and Network Meta-Analysis [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :924 ( https://doi.org/10.5256/f1000research.186674.r435605 ) The direct URL for this report is: https://f1000research.com/articles/14-924/v1#referee-response-435605 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Disher T. Reviewer Report For: Title:BayesMetaNMA: An Interactive R/Shiny Application for Bayesian Pairwise and Network Meta-Analysis [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :924 ( https://doi.org/10.5256/f1000research.186674.r419052 ) The direct URL for this report is: https://f1000research.com/articles/14-924/v1#referee-response-419052 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 08 Nov 2025 Tim Disher , Dalhousie University, Halifax, Nova Scotia, Canada Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.186674.r419052 Authors provide an R Shiny application for Bayesian pairwise and network meta-analyses. The provided github link is closed, and the archived code linked on Zenodo gave errors when trying to run the example data so I was not able to ... Continue reading READ ALL Authors provide an R Shiny application for Bayesian pairwise and network meta-analyses. The provided github link is closed, and the archived code linked on Zenodo gave errors when trying to run the example data so I was not able to confirm whether the code works or produces sensible results. The authors do not describe what gap this application is intended to fill, as freely available GUIs are already available (eg, https://crsu-metainsight.le.ac.uk/MetaInsight/) for those who can't program, and these solutions offer more capability, stronger validation, and better documentation. The included options for analyses are all based on large sample normal approximations which will not be appropriate for binary rare events. No tools are provided to create the required summary measures from raw data. NMA code indexes over studies K defined as rows in the underlying data frame only comparing a max of two treatments within trials. This simplifies code since they are not required to account for correlation between contrasts with the same control arm or through random effects between arms, but limits how useful the tool can be. The manuscript itself does not provide summary of the underlying logic, outputs, or their interpretations and so it is unlikely to tool would be useful to someone who does not already have some expertise in the area the vast majority of whom would have sufficient coding ability to use existing simple packages like multinma to conduct a wider variety of analysis with superior documentation and rigour of implementation. Is the rationale for developing the new software tool clearly explained? No Is the description of the software tool technically sound? No Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Partly Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? No Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? No Competing Interests: No competing interests were disclosed. Reviewer Expertise: NMA methods, health economics and outcomes research I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Disher T. Reviewer Report For: Title:BayesMetaNMA: An Interactive R/Shiny Application for Bayesian Pairwise and Network Meta-Analysis [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :924 ( https://doi.org/10.5256/f1000research.186674.r419052 ) The direct URL for this report is: https://f1000research.com/articles/14-924/v1#referee-response-419052 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 15 Sep 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The 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 approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 1 15 Sep 25 read read Tim Disher , Dalhousie University, Halifax, Canada Qi-Ang Wang , China University of Mining and Technology, Xuzhou, China Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Wang Q. This is an open access peer review report 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. 02 Jan 2026 | for Version 1 Qi-Ang Wang , China University of Mining and Technology, Xuzhou, China 0 Views copyright © 2026 Wang Q. This is an open access peer review report 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. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The 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 approved Fundamental flaws in the paper seriously undermine the findings and conclusions BayesMetaNMA is an open-source R/Shiny application that enables Bayesian pairwise and network meta-analysis through an interactive GUI. It integrates JAGS for MCMC estimation and supports various effect measures, diagnostics, and visualizations. While user-friendly, the tool lacks flexibility in priors, rare-events support, and comprehensive documentation. Peer review found it underdeveloped compared to existing platforms. 1. The current implementation relies on large-sample normal approximations, which are inappropriate for binary outcomes with rare events. Please incorporate exact likelihood models (e.g., binomial with logit or cloglog links) to handle sparse data more accurately. This would improve the tool's applicability in safety or adverse-event meta-analyses. 2. The software currently offers limited prior choices, restricting users to uniform or vague normal priors. Please expand the prior library to include informed, hierarchical, or mixture priors, and allow users to define custom priors interactively. Provide guidance or warnings when priors are poorly matched to data scale or structure. 3. The literature review part can be improved by including more recent publications on machine learning methods, e.g., Bayesian method or Gaussian method, which can refer to Uncertainty-awarded, high-precision multi-step prediction of structural health monitoring sensor streams under extreme typhoon events: an enhanced Bayesian dynamic linear model leveraging the kernel regression basis function for severe environmental adaptation, Data interpretation and forecasting of SHM heteroscedastic measurements under typhoon conditions enabled by an enhanced Hierarchical sparse Bayesian Learning model with high robustness, Towards high-accuracy data modelling, uncertainty quantification and correlation analysis for SHM measurements during typhoon events using an improved most likely heteroscedastic Gaussian process, Bayesian Network in Structural Health Monitoring: Theoretical Background and Applications Review. 4. The manuscript and tool lack detailed explanations of model assumptions, output interpretation, and diagnostic thresholds. Add embedded help modules, tooltips, and interpretive summaries within the Shiny interface. Include a user manual with worked examples covering common pitfalls, such as non-convergence or inconsistency detection. 5. The NMA model indexes only two-arm trials and ignores correlation between multi-arm study contrasts. Refactor the underlying code to support full multivariate random-effects modeling, ensuring consistency and coherence in treatment rankings. Additionally, implement Bayesian inconsistency detection methods (e.g., node-splitting or design-by-treatment interaction) to enhance model validity. Is the rationale for developing the new software tool clearly explained? Partly Is the description of the software tool technically sound? Partly Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Partly Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Machine learning I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Wang QA. Peer Review Report For: Title:BayesMetaNMA: An Interactive R/Shiny Application for Bayesian Pairwise and Network Meta-Analysis [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :924 ( https://doi.org/10.5256/f1000research.186674.r435605) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-924/v1#referee-response-435605 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Disher T. This is an open access peer review report 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. 08 Nov 2025 | for Version 1 Tim Disher , Dalhousie University, Halifax, Nova Scotia, Canada 0 Views copyright © 2025 Disher T. This is an open access peer review report 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. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The 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 approved Fundamental flaws in the paper seriously undermine the findings and conclusions Authors provide an R Shiny application for Bayesian pairwise and network meta-analyses. The provided github link is closed, and the archived code linked on Zenodo gave errors when trying to run the example data so I was not able to confirm whether the code works or produces sensible results. The authors do not describe what gap this application is intended to fill, as freely available GUIs are already available (eg, https://crsu-metainsight.le.ac.uk/MetaInsight/) for those who can't program, and these solutions offer more capability, stronger validation, and better documentation. The included options for analyses are all based on large sample normal approximations which will not be appropriate for binary rare events. No tools are provided to create the required summary measures from raw data. NMA code indexes over studies K defined as rows in the underlying data frame only comparing a max of two treatments within trials. This simplifies code since they are not required to account for correlation between contrasts with the same control arm or through random effects between arms, but limits how useful the tool can be. The manuscript itself does not provide summary of the underlying logic, outputs, or their interpretations and so it is unlikely to tool would be useful to someone who does not already have some expertise in the area the vast majority of whom would have sufficient coding ability to use existing simple packages like multinma to conduct a wider variety of analysis with superior documentation and rigour of implementation. Is the rationale for developing the new software tool clearly explained? No Is the description of the software tool technically sound? No Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Partly Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? No Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? No Competing Interests No competing interests were disclosed. Reviewer Expertise NMA methods, health economics and outcomes research I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Disher T. Peer Review Report For: Title:BayesMetaNMA: An Interactive R/Shiny Application for Bayesian Pairwise and Network Meta-Analysis [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :924 ( https://doi.org/10.5256/f1000research.186674.r419052) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-924/v1#referee-response-419052 Alongside their report, reviewers assign a status to the article: Approved - the 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 approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. 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