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

[version 1; peer review: awaiting peer review]
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
OPEN PEER REVIEW
REVIEWER STATUS AWAITING PEER REVIEW

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 (I2, Ï„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.

Keywords

Keywords: Meta-analysis, Risk Ratio, Odds Ratio, R, Shiny, Bayesian Meta-Analysis, Publication Bias, Heterogeneity, Meta-regression, Open-source Software

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: awaiting peer review]. 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

  • - I2, Ï„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: I2, τ2, 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/

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.

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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: awaiting peer review]. F1000Research 2025, 14:1168 (https://doi.org/10.12688/f1000research.167961.1)
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