Abstract
Abstract:
Background: Network meta-analysis (NMA) is a valuable technique for comparing three or more treatments. Yet, performing the analysis and checking crucial factors such as consistency and bias tends to necessitate statistical programming abilities, and therefore represents a drawback in the meta-analysis of research studies with ratio measures (RR,OR, HR).
Methods: We created 786-MIII RR/OR NMA, an R (v4.4.0) and Shiny framework (v1.8.1.1) interactive web application with a bs4Dash (v2.3.2) dashboard layout. It performs frequentist NMA through the netmeta package (v[e.g., 2.8-1]). Data (arm-level for RR/OR binary; contrast-level for log-ratios) are uploaded via CSV. visNetwork and igraph are used for network plots.
Results and Functionality: The 786-MIII RR/OR NMA offers functionalities for uploading data, choosing the outcome (binary RR/OR or survival/ratio HR), visualizing the network graph, conducting fixed- or random-effects NMA, and investigating results. Outputs comprise forest plots (with RR/OR/HR) that can be customized, league tables, P-score rankings, inconsistency diagnostics (design decomposition, heat plots, node-splitting), comparison-adjusted funnel plots, and leave-one-out analysis. Results and plots can be downloaded as well.
Conclusions: The 786-MIII RR/OR NMA provides a graphical workflow for the frequentist network meta-analysis (NMA) of ratio measures under the netmeta package. With integrated analysis, visualisation, and diagnostic capabilities, it is designed to optimize the accessibility and efficiency of NMA.
References
1)Balduzzi, S., Rücker, G., Nikolakopoulou, A., Papakonstantinou, T., Salanti, G., Efthimiou, O., & Schwarzer, G. (2023). Netmeta: An R package for network meta-analysis using frequentist methods. Journal of Statistical Software, 106(2), 1–40. https://doi.org/10.18637/jss.v106.i02
2)Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to Meta-Analysis. Wiley. ISBN: 978-0470057247
3)Chaimani, A., Higgins, J. P. T., Mavridis, D., Spyridonos, P., & Salanti, G. (2013). Graphical tools for network meta-analysis in STATA. PLoS ONE, 8(10), e76654. https://doi.org/10.1371/journal.pone.0076654
4)Chang, W., Cheng, J., Allaire, J. J., Sievert, C., Schloerke, B., Xie, Y., Allen, J.,McPherson, J., Dipert, A., & Borges, B. (2024). Shiny: Web Application Framework for R. R package version 1.8.1.1. URL: https://shiny.posit.co/
5)Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695. https://igraph.org
6)Dias, S., Welton, N. J., Sutton, A. J., & Ades, A. E. (2013). Evidence synthesis for decision making 1: Introduction. Medical Decision Making, 33(5), 597–606. https://doi.org/10.1177/0272989X13485157
7)Granjon, D. (2023). Bs4Dash: A ‘Bootstrap 4’ Version of ‘shinydashboard’. R package version 2.3.2. URL: https://github.com/RinteRface/bs4Dash
8)Higgins, J. P. T., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21(11), 1539–1558. https://doi.org/10.1002/sim.1186
9)Lumley, T. (2002). Network meta-analysis for indirect treatment comparisons. Statistics in Medicine, 21(16), 2313–2324. https://doi.org/10.1002/sim.1201
10)R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL: https://www.R-project.org
11)Rucker, G., Krahn, U., König, J., Efthimiou, O., & Schwarzer, G. (2015). Netmeta: Network Meta‐Analysis using Frequentist Methods. R package version [e.g., 2.8-1 or check current]. URL: https://CRAN.R-project.org/package=netmeta
12)Salanti, G. (2012). Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the appropriate application of meta-analytical techniques. Clinical Trials, 9(2), 230–241. https://doi.org/10.1177/1740774511438119
13)Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03
14)Wickham, H. (2016). Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN: 978-3-319-24277-4
15)Wickham, H., Hester, J., & Bryan, J. (2024). Readr: Read Rectangular Text Data. R package version 2.1.5. URL: https://readr.tidyverse.org/
16)Xie, Y., Cheng, J., & Tan, X. (2024). DT: A Wrapper of the JavaScript Library ‘DataTables’. R package version 0.33. URL: https://github.com/rstudio/DT
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2025 Laiba Khan, Maham KHAN, Mahmood Ahmad, Muhammad Hamza KHAN, Joanne Lac