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

Laiba Khan; Maham khan; Nijat Rzayev; Manpreet Kour; Andrew Woo; Parichatra Homhuan; Poe Hnin Phyu; Mahmood Ahmad; Touqeer Rana; Joanne Lac

doi:10.12688/f1000research.167961.1

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

[version 1; peer review: 2 not approved]

Laiba Khan¹, Maham khan¹, Nijat Rzayev¹, [...] Manpreet Kour¹, Andrew Woo¹, Parichatra Homhuan¹, Poe Hnin Phyu¹, Mahmood Ahmad¹, Touqeer Rana¹, Joanne Lac ²

Laiba Khan¹, Maham khan¹, [...] Nijat Rzayev¹, Manpreet Kour¹, Andrew Woo¹, Parichatra Homhuan¹, Poe Hnin Phyu¹, Mahmood Ahmad¹, Touqeer Rana¹, Joanne Lac 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

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 (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.

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: 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
- I², τ², 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/

- 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

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.

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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: 2 not approved]. F1000Research 2025, 14:1168 (https://doi.org/10.12688/f1000research.167961.1)

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Version 1

VERSION 1

PUBLISHED 27 Oct 2025

Views

4

Reviewer Report 11 Dec 2025

Angel Garza Reyna, Duke University School of Medicine, Durham, North Carolina, USA

Not Approved

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

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.

CITE

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7

Reviewer Report 24 Nov 2025

Naomi Bradbury, University of Leicester, Leicester, UK

Not Approved

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

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.

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Naomi Bradbury, University of Leicester, Leicester, UK
Angel Garza Reyna, Duke University School of Medicine, Durham, USA

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Reviewer Report

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11 Dec 2025 | for Version 1

Angel Garza Reyna, Duke University School of Medicine, Durham, North Carolina, USA

4 Views Cite this report Responses(0)

Not Approved

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.

Respond to this report

Responses (0)

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Reviewer Report

7 Views

24 Nov 2025 | for Version 1

Naomi Bradbury, University of Leicester, Leicester, UK

7 Views Cite this report Responses(0)

Not Approved

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.

Respond to this report

Responses (0)

[1] Ahmad M: RROR Shiny. Zenodo. 2025. Publisher Full Text

[2] 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

[3] Chang W, Cheng J, Allaire JJ, et al.: shiny: Web Application Framework for R. R package version 1.7.5.2023. Reference Source

[4] Harrer M, Cuijpers P, Furukawa TA, et al.: dmetar (v0.0.9000).2021.

[5] Higgins JPT, Thomas J, Chandler J, et al., editors.: Cochrane Handbook for Systematic Reviews of Interventions. Version 6.3.Cochrane; 2011. Reference Source

[6] R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing.Vienna, Austria; 2023. Reference Source

[7] Viechtbauer W: Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 2010; 36(3): 1–48. Publisher Full Text

[8] Wickham H: ggplot2: Elegant Graphics for Data Analysis. Springer; 2016.

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

Abstract

Background

Methods

Results

Conclusions

Keywords

Introduction

Methods

Data input

Results

Use case example

Discussion

Strengths

Limitations

Future development

License

Data availability

Underlying data

Acknowledgments

References

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