RRORPair: An Interactive R/Shiny Dashboard for Comprehensive Risk Ratio and Odds Ratio Meta-Analysis

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These requirements can pose barriers to many researchers. Methods We introduce RRORPair, an open-source R/Shiny web application for RR/OR meta-analysis. Built on widely-used R packages (meta, metafor, dmetar, and ggplot2), it offers an intuitive interface enabling data import via CSV, selection of analytical models, and extensive customization of visual outputs. RRORPair supports classical and Bayesian methods, publication bias detection, heterogeneity diagnostics, meta-regression, and subgroup or cumulative analyses. Results RRORPair provides: Forest plots (standard, JAMA, RevMan5 style) Funnel plots, Egger’s test, trim-and-fill, limit meta-analysis, and p-curve analysis Heterogeneity statistics (I2, τ2), Baujat plots, and influence diagnostics Meta-regression with up to three moderators, cumulative and subgroup analysis Bayesian meta-analysis Outputs include interactive plots and downloadable reports. Conclusions RRORPair is a powerful and user-friendly tool that makes advanced meta-analysis of binary outcomes accessible without programming. It supports robust evidence synthesis and encourages transparency and reproducibility in research. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-1168/v1", "name": "RRORPair: An Interactive R/Shiny Dashboard for Comprehensive Risk..." } } ] } Home Browse RRORPair: An Interactive R/Shiny Dashboard for Comprehensive Risk... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Khan L, khan M, Rzayev N et al. RRORPair: An Interactive R/Shiny Dashboard for Comprehensive Risk Ratio and Odds Ratio Meta-Analysis [version 1; peer review: 2 not approved] . F1000Research 2025, 14 :1168 ( https://doi.org/10.12688/f1000research.167961.1 ) 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 RRORPair: An Interactive R/Shiny Dashboard for Comprehensive Risk Ratio and Odds Ratio Meta-Analysis [version 1; peer review: 2 not approved] Laiba Khan https://orcid.org/0009-0005-4845-3900 1 , Maham khan https://orcid.org/0009-0002-2994-143X 1 , Nijat Rzayev https://orcid.org/0009-0006-0570-6928 1 , [...] Manpreet Kour https://orcid.org/0009-0004-6682-4966 1 , Andrew Woo https://orcid.org/0009-0004-2986-1872 1 , Parichatra Homhuan https://orcid.org/0009-0009-4553-0244 1 , Poe Hnin Phyu https://orcid.org/0009-0000-2459-9028 1 , Mahmood Ahmad https://orcid.org/0000-0001-9107-3704 1 , Touqeer Rana https://orcid.org/0009-0003-0904-7235 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 , [...] Nijat Rzayev https://orcid.org/0009-0006-0570-6928 1 , Manpreet Kour https://orcid.org/0009-0004-6682-4966 1 , Andrew Woo https://orcid.org/0009-0004-2986-1872 1 , Parichatra Homhuan https://orcid.org/0009-0009-4553-0244 1 , Poe Hnin Phyu https://orcid.org/0009-0000-2459-9028 1 , Mahmood Ahmad https://orcid.org/0000-0001-9107-3704 1 , Touqeer Rana https://orcid.org/0009-0003-0904-7235 1 , Joanne Lac https://orcid.org/0009-0004-3533-434X 2 PUBLISHED 27 Oct 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: Validation, Writing – Review & Editing Maham khan Roles: Writing – Review & Editing Nijat Rzayev Roles: Writing – Review & Editing Manpreet Kour Roles: Writing – Review & Editing Andrew Woo Roles: Writing – Review & Editing Parichatra Homhuan Roles: Writing – Review & Editing Poe Hnin Phyu Roles: Writing – Review & Editing Mahmood Ahmad Roles: Conceptualization, Software, Validation, Visualization, Writing – Original Draft Preparation Touqeer Rana Roles: Writing – Review & Editing Joanne Lac Roles: Funding Acquisition, Supervision, Validation, Visualization OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the RPackage gateway. Abstract Background Meta-analysis is central to evidence-based practice, yet conducting comprehensive analyses—especially involving binary outcomes such as risk ratios (RR) and odds ratios (OR)—often demands specialized software and statistical programming skills. These requirements can pose barriers to many researchers. Methods We introduce RRORPair, an open-source R/Shiny web application for RR/OR meta-analysis. Built on widely-used R packages (meta, metafor, dmetar, and ggplot2), it offers an intuitive interface enabling data import via CSV, selection of analytical models, and extensive customization of visual outputs. RRORPair supports classical and Bayesian methods, publication bias detection, heterogeneity diagnostics, meta-regression, and subgroup or cumulative analyses. Results RRORPair provides: Forest plots (standard, JAMA, RevMan5 style) Funnel plots, Egger’s test, trim-and-fill, limit meta-analysis, and p-curve analysis Heterogeneity statistics (I 2 , τ 2 ), Baujat plots, and influence diagnostics Meta-regression with up to three moderators, cumulative and subgroup analysis Bayesian meta-analysis Outputs include interactive plots and downloadable reports. Conclusions RRORPair is a powerful and user-friendly tool that makes advanced meta-analysis of binary outcomes accessible without programming. It supports robust evidence synthesis and encourages transparency and reproducibility in research. READ ALL READ LESS Keywords Keywords: Meta-analysis, Risk Ratio, Odds Ratio, R, Shiny, Bayesian Meta-Analysis, Publication Bias, Heterogeneity, Meta-regression, Open-source Software 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, Rzayev N et al. RRORPair: An Interactive R/Shiny Dashboard for Comprehensive Risk Ratio and Odds Ratio Meta-Analysis [version 1; peer review: 2 not approved] . F1000Research 2025, 14 :1168 ( https://doi.org/10.12688/f1000research.167961.1 ) First published: 27 Oct 2025, 14 :1168 ( https://doi.org/10.12688/f1000research.167961.1 ) Latest published: 27 Oct 2025, 14 :1168 ( https://doi.org/10.12688/f1000research.167961.1 ) Introduction Meta-analysis allows researchers to synthesize results from multiple studies, enhancing statistical power and clarifying effects of interventions ( Higgins et al., 2011 ). For binary outcomes, risk ratios (RR) and odds ratios (OR) are commonly used effect measures. However, conducting high-quality meta-analyses often involves steep learning curves with statistical software or access to commercial packages such as Comprehensive Meta-Analysis (CMA) or Stata. To address these barriers, we developed RRORPair, an open-source, interactive web application built with R and Shiny. RRORPair allows users to conduct advanced meta-analytic procedures through an intuitive graphical interface without writing code. By integrating functions from popular R packages (meta, metafor, dmetar, ggplot2), the tool supports rigorous analyses, extensive visualization, and comprehensive diagnostics—enabling a wide range of users to undertake binary outcome meta-analyses. Methods Software and implementation RRORPair is developed in R (≥4.0.0; R Core Team, 2023 ) using the Shiny web framework (≥1.7.0; Chang et al., 2023 ). It is designed as a modular application using the following R packages: - meta: For traditional meta-analysis calculations (e.g., metabin; Balduzzi et al., 2019 ) - metafor: For meta-regression, robust estimation, and publication bias methods ( Viechtbauer, 2010 ) - dmetar: For influence analysis, p-curve diagnostics, and enhanced visual outputs ( Harrer et al., 2021 ) - ggplot2 and ggbeeswarm: For customizable plots ( Wickham, 2016 ) - bayesmeta: For Bayesian meta-analyses - shinyjs, bs4Dash, fontawesome: For UI customization and layout enhancements - PerformanceAnalytics, dplyr: For data wrangling and diagnostics User interface and workflow RRORPair consists of the following modules: - Data Import & Settings - Meta-Analysis Summary - Forest Plots - Publication Bias Analysis - Heterogeneity Assessment - Meta-Regression - Bayesian Analysis - Advanced Analyses (e.g., subgroup and cumulative) Data input Users upload a CSV file containing: - Required columns: eventintervention, totalintervention, eventcontrol, totalcontrol, author - Optional: year (for cumulative meta-analysis), Reg, Reg2, Reg3 (moderators), subgroup Analytical options The user can choose: - Effect measure: Risk Ratio (RR) or Odds Ratio (OR) - Model: Fixed-effect or random-effects - Heterogeneity estimator: e.g., Paule-Mandel, DL, ML, REML Output and features - Forest plots (standard, JAMA-style, RevMan5) - Funnel plots with Egger’s test, trim-and-fill, limit meta-analysis - I 2 , τ 2 , Q-tests, Baujat, L’Abbé, and influence diagnostics - Meta-regression (up to three moderators) - Bayesian RR/OR analysis - Exportable results (PNG, TXT, HTML) Results Functionality highlights Forest Plots: Available in standard, JAMA-style, and RevMan5 formats Bias Assessment: Funnel plots, contour-enhanced versions, Egger’s test, trim-and-fill, limit meta-analysis, p-curve Heterogeneity Exploration: I 2 , τ 2 , Q-test, Baujat, influence, L’Abbé, and drapery plots Meta-Regression: Up to three moderators with bubble plots and correlation matrices Bayesian Analysis: Basic Bayesian RR/OR models Cumulative/Subgroup Analysis: By year or subgroup variable Influence Diagnostics: Leave-one-out analysis, outlier detection All plots and summaries can be exported (e.g., PNG, TXT). Educational tooltips and embedded tutorials are included. Use case example A researcher investigating the effect of a new drug on adverse event occurrence across 10 studies prepares a CSV with columns: author, year, eventintervention, totalintervention, eventcontrol, totalcontrol, Reg (average age) They upload the file, select RR, Paule-Mandel estimator, random-effects model, and explore: Results Tab: Summary table with pooled RR, CI, prediction interval, heterogeneity stats Forest Plot Tab: Customizable visual output Bias Tab: Funnel plot, Egger’s test, p-curve Heterogeneity Tab: Baujat plot identifies influential studies Meta-Regression Tab: Bubble plot visualizes age as a moderator Discussion RRORPair aims to democratize meta-analysis by making high-level statistical methods accessible via an interactive platform. Strengths Comprehensive: RR, OR, publication bias, heterogeneity, regression, Bayesian options User-Friendly: No programming required Interactive & Exportable: Real-time adjustments, downloadable visuals Open-Source: Transparent and community-extensible Educational: Built-in guidance and references Limitations Currently supports only binary outcomes Depends on correct formatting of input data Some advanced or niche meta-analytic methods not yet included Future development Plans include support for continuous outcomes, network meta-analysis, and integration with risk of bias tools. License The software and data are licensed under the Apache License 2.0, an OSI-approved open license. Data availability Underlying data All datasets used in this article are openly available. Web Application: https://786miii.shinyapps.io/MIII786ORRR/ - Example data for demonstrating the RRORPair application is available on GitHub: https://github.com/mahmood789/RRORPair - An archived version is available via Zenodo: https://doi.org/10.5281/zenodo.15879475 . Ahmad, Mahmood (2025) . The datasets include: - The values behind the reported outcomes and summary statistics - Sample CSV files required to operate the tool - Code and documentation for reproducing the study and generating figures Example Data: Available via the GitHub repository. Users can format their own CSV files using required column specifications. Acknowledgments We thank the Ahmadiyya Muslim Research Association (AMRA) for their support. Special thanks to Luciano Candilio, Malik Takreem Ahmad, Niraj Kumar, Jonathan Bray, Reubeen Ahmad, and Prof Rui Providencia for their insights and feedback. We also appreciate the testing efforts of all co-authors. References Ahmad M: RROR Shiny. Zenodo. 2025. Publisher Full Text 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 Harrer M, Cuijpers P, Furukawa TA, et al. : dmetar (v0.0.9000).2021. Higgins JPT, Thomas J, Chandler J, et al. , editors.: Cochrane Handbook for Systematic Reviews of Interventions. Version 6.3.Cochrane; 2011. Reference Source R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing.Vienna, Austria; 2023. Reference Source Viechtbauer W: Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 2010; 36 (3): 1–48. Publisher Full Text Wickham H: ggplot2: Elegant Graphics for Data Analysis. Springer; 2016. Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 27 Oct 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: Validation, Writing – Review & Editing Maham khan Roles: Writing – Review & Editing Nijat Rzayev Roles: Writing – Review & Editing Manpreet Kour Roles: Writing – Review & Editing Andrew Woo Roles: Writing – Review & Editing Parichatra Homhuan Roles: Writing – Review & Editing Poe Hnin Phyu Roles: Writing – Review & Editing Mahmood Ahmad Roles: Conceptualization, Software, Validation, Visualization, Writing – Original Draft Preparation Touqeer Rana Roles: Writing – Review & Editing Joanne Lac Roles: Funding Acquisition, Supervision, Validation, Visualization 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: 27 Oct 2025, 14:1168 https://doi.org/10.12688/f1000research.167961.1 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, Rzayev N et al. RRORPair: An Interactive R/Shiny Dashboard for Comprehensive Risk Ratio and Odds Ratio Meta-Analysis [version 1; peer review: 2 not approved] . F1000Research 2025, 14 :1168 ( https://doi.org/10.12688/f1000research.167961.1 ) 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 27 Oct 2025 Views 0 Cite How to cite this report: Garza Reyna A. Reviewer Report For: RRORPair: An Interactive R/Shiny Dashboard for Comprehensive Risk Ratio and Odds Ratio Meta-Analysis [version 1; peer review: 2 not approved] . F1000Research 2025, 14 :1168 ( https://doi.org/10.5256/f1000research.185111.r432423 ) The direct URL for this report is: https://f1000research.com/articles/14-1168/v1#referee-response-432423 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 11 Dec 2025 Angel Garza Reyna , Duke University School of Medicine, Durham, North Carolina, USA Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.185111.r432423 Khan et al present RRORPair -- an alternative to existing shiny apps, which aim to conduct binary outcome meta-analyses. RRORPair is deployed online via shinyapps.io and as a standalone published on GitHub. Both were functional to me. ... Continue reading READ ALL Khan et al present RRORPair -- an alternative to existing shiny apps, which aim to conduct binary outcome meta-analyses. RRORPair is deployed online via shinyapps.io and as a standalone published on GitHub. Both were functional to me. The online deployment includes a plethora of tabs, each with unique functionalities. However, the app would benefit from an example dataset, a tutorial, or a document demonstrating the systematic use of RRORPair -- such could include the case example mentioned in "Use case example". On another note, in the "About" tab, there is a hyperlink on " Ackowldgement for the Ahmadiyya Muslim Research Association " which is not accessible -- the link needs to be updated. The authors should consider a grammatical check on the existing text within their final version. Moving on to GitHub, a READ.me file would be helpful for non-experts with a description of RRORPair, example usage, and installation instructions. Unlike the online version, the standalone fails to display several figures, i.e., Introduction & Data Import -> Understanding Meta-Analysis -> Understanding Forest Plots. From a technical perspective, the code needs extensive polishing. There are 4959 lines of code, which is likely due to multiple server sections: server <- function(input, output, session) {}. Within the script, there exist comments such as: "# ...[All your existing output$... definitions remain the same]... # Make sure you replaced studlab = author with studlab = dat$author # and removed the “N” argument from pcurve() calls." and " # Similarly for forestPlotJAMA, forestPlotRevman5, labbe, drapery, etc.: # change studlab = author to studlab = dat$author. # Also remove “N” from pcurve calls, e.g.: # pcurve(m.bin, effect.estimation = FALSE, dmin = 0, dmax = 1)" which I would recommend are removed. It appears that these are from the development stage, and annotations should be descriptive of coding chunks. Lastly, the manuscript itself appears to be in its early stages -- much like an outline. The introduction would benefit from a more in-depth comparison of RRORPair and existing resources, i.e., Comprehensive Meta-Analysis (CMA) or Stata. There appear to be citations missing, such as those in the Methods section referencing bayesmeta. I recommend that the authors consult similar articles available for reference. In the Data Availability section, the authors mention Example Data is available in the GitHub repo; however, only two files exist -- LICENSE and app.R. Is the rationale for developing the new software tool clearly explained? Partly 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? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Computational Biology; R; R/Shiny; Immunology 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 Garza Reyna A. Reviewer Report For: RRORPair: An Interactive R/Shiny Dashboard for Comprehensive Risk Ratio and Odds Ratio Meta-Analysis [version 1; peer review: 2 not approved] . F1000Research 2025, 14 :1168 ( https://doi.org/10.5256/f1000research.185111.r432423 ) The direct URL for this report is: https://f1000research.com/articles/14-1168/v1#referee-response-432423 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: Bradbury N. Reviewer Report For: RRORPair: An Interactive R/Shiny Dashboard for Comprehensive Risk Ratio and Odds Ratio Meta-Analysis [version 1; peer review: 2 not approved] . F1000Research 2025, 14 :1168 ( https://doi.org/10.5256/f1000research.185111.r430306 ) The direct URL for this report is: https://f1000research.com/articles/14-1168/v1#referee-response-430306 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 24 Nov 2025 Naomi Bradbury , University of Leicester, Leicester, UK Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.185111.r430306 The authors have produced an R Shiny application for conducting meta-analysis for binary outcomes (odds ratios and risk ratios). The app is available online and the underlying R code is viewable through the provided GitHub link. However, there are ... Continue reading READ ALL The authors have produced an R Shiny application for conducting meta-analysis for binary outcomes (odds ratios and risk ratios). The app is available online and the underlying R code is viewable through the provided GitHub link. However, there are no datasets available through the GitHub link so it has not been possible to assess the functionality of the app or if it produces accurate results. Nor was I able to run the code locally on my machine, as the ‘dmetar’ package is not available via CRAN for my version of R (4.4.2). By providing the underlying code the authors have made a start on good practices for research software. However, as a minimum, they should also address what efforts they have made to test their app to identify if the outputs are accurate and reproducible. There is a wealth of information available online around software engineering best practices with the (e)Book “Engineering Production Grade Shiny Apps” providing a thorough introduction tailored to R Shiny app development. The authors note that this tool provides an advantage over commercial software but they have not described what additional functionality or other advantages it provides over other open access meta-analysis tools such as those produced by the CRSU ( https://www.gla.ac.uk/research/az/crsu/apps/ ) The article methods, results and discussion sections consist only of a skeleton outline. The authors should consult other research articles that have been published that describe the development of R Shiny apps to provide a framework for how to outline a successful article describing the production of a piece of research software. If an example dataset were available this could be used to give readers and potential users a walkthrough of the functionality of the software. Is the rationale for developing the new software tool clearly explained? Partly 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: I have previously been employed by the CRSU and was involved with developing their R Shiny evidence synthesis apps (https://www.gla.ac.uk/research/az/crsu/apps/) Reviewer Expertise: R, Shiny, Epidemiology 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 Bradbury N. Reviewer Report For: RRORPair: An Interactive R/Shiny Dashboard for Comprehensive Risk Ratio and Odds Ratio Meta-Analysis [version 1; peer review: 2 not approved] . F1000Research 2025, 14 :1168 ( https://doi.org/10.5256/f1000research.185111.r430306 ) The direct URL for this report is: https://f1000research.com/articles/14-1168/v1#referee-response-430306 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 27 Oct 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 27 Oct 25 read read Naomi Bradbury , University of Leicester, Leicester, UK Angel Garza Reyna , Duke University School of Medicine, Durham, USA 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 © 2025 Garza Reyna A. 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. 11 Dec 2025 | for Version 1 Angel Garza Reyna , Duke University School of Medicine, Durham, North Carolina, USA 0 Views copyright © 2025 Garza Reyna A. 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 Khan et al present RRORPair -- an alternative to existing shiny apps, which aim to conduct binary outcome meta-analyses. RRORPair is deployed online via shinyapps.io and as a standalone published on GitHub. Both were functional to me. The online deployment includes a plethora of tabs, each with unique functionalities. However, the app would benefit from an example dataset, a tutorial, or a document demonstrating the systematic use of RRORPair -- such could include the case example mentioned in "Use case example". On another note, in the "About" tab, there is a hyperlink on " Ackowldgement for the Ahmadiyya Muslim Research Association " which is not accessible -- the link needs to be updated. The authors should consider a grammatical check on the existing text within their final version. Moving on to GitHub, a READ.me file would be helpful for non-experts with a description of RRORPair, example usage, and installation instructions. Unlike the online version, the standalone fails to display several figures, i.e., Introduction & Data Import -> Understanding Meta-Analysis -> Understanding Forest Plots. From a technical perspective, the code needs extensive polishing. There are 4959 lines of code, which is likely due to multiple server sections: server <- function(input, output, session) {}. Within the script, there exist comments such as: "# ...[All your existing output$... definitions remain the same]... # Make sure you replaced studlab = author with studlab = dat$author # and removed the “N” argument from pcurve() calls." and " # Similarly for forestPlotJAMA, forestPlotRevman5, labbe, drapery, etc.: # change studlab = author to studlab = dat$author. # Also remove “N” from pcurve calls, e.g.: # pcurve(m.bin, effect.estimation = FALSE, dmin = 0, dmax = 1)" which I would recommend are removed. It appears that these are from the development stage, and annotations should be descriptive of coding chunks. Lastly, the manuscript itself appears to be in its early stages -- much like an outline. The introduction would benefit from a more in-depth comparison of RRORPair and existing resources, i.e., Comprehensive Meta-Analysis (CMA) or Stata. There appear to be citations missing, such as those in the Methods section referencing bayesmeta. I recommend that the authors consult similar articles available for reference. In the Data Availability section, the authors mention Example Data is available in the GitHub repo; however, only two files exist -- LICENSE and app.R. Is the rationale for developing the new software tool clearly explained? Partly 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? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Computational Biology; R; R/Shiny; Immunology 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) Garza Reyna A. Peer Review Report For: RRORPair: An Interactive R/Shiny Dashboard for Comprehensive Risk Ratio and Odds Ratio Meta-Analysis [version 1; peer review: 2 not approved] . F1000Research 2025, 14 :1168 ( https://doi.org/10.5256/f1000research.185111.r432423) 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/14-1168/v1#referee-response-432423 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Bradbury N. 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. 24 Nov 2025 | for Version 1 Naomi Bradbury , University of Leicester, Leicester, UK 0 Views copyright © 2025 Bradbury N. 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 The authors have produced an R Shiny application for conducting meta-analysis for binary outcomes (odds ratios and risk ratios). The app is available online and the underlying R code is viewable through the provided GitHub link. However, there are no datasets available through the GitHub link so it has not been possible to assess the functionality of the app or if it produces accurate results. Nor was I able to run the code locally on my machine, as the ‘dmetar’ package is not available via CRAN for my version of R (4.4.2). By providing the underlying code the authors have made a start on good practices for research software. However, as a minimum, they should also address what efforts they have made to test their app to identify if the outputs are accurate and reproducible. There is a wealth of information available online around software engineering best practices with the (e)Book “Engineering Production Grade Shiny Apps” providing a thorough introduction tailored to R Shiny app development. The authors note that this tool provides an advantage over commercial software but they have not described what additional functionality or other advantages it provides over other open access meta-analysis tools such as those produced by the CRSU ( https://www.gla.ac.uk/research/az/crsu/apps/ ) The article methods, results and discussion sections consist only of a skeleton outline. The authors should consult other research articles that have been published that describe the development of R Shiny apps to provide a framework for how to outline a successful article describing the production of a piece of research software. If an example dataset were available this could be used to give readers and potential users a walkthrough of the functionality of the software. Is the rationale for developing the new software tool clearly explained? Partly 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 I have previously been employed by the CRSU and was involved with developing their R Shiny evidence synthesis apps (https://www.gla.ac.uk/research/az/crsu/apps/) Reviewer Expertise R, Shiny, Epidemiology 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) Bradbury N. Peer Review Report For: RRORPair: An Interactive R/Shiny Dashboard for Comprehensive Risk Ratio and Odds Ratio Meta-Analysis [version 1; peer review: 2 not approved] . F1000Research 2025, 14 :1168 ( https://doi.org/10.5256/f1000research.185111.r430306) 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/14-1168/v1#referee-response-430306 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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