A comprehensive suite of functions to perform and visualise pairwise and network meta-analysis with aggregate binary or continuous missing participant outcome data. The package covers core Bayesian one-stage models implemented in a systematic review with multiple interventions, including fixed-effect and random-effects network meta-analysis, meta-regression, evaluation of the consistency assumption via the node-splitting approach and the unrelated mean effects model (original and revised model proposed by Spineli, (2022) <doi:10.1177/0272989X211068005>), and sensitivity analysis (see Spineli et al., (2021) <doi:10.1186/s12916-021-02195-y>). Missing participant outcome data are addressed in all models of the package (see Spineli, (2019) <doi:10.1186/s12874-019-0731-y>, Spineli et al., (2019) <doi:10.1002/sim.8207>, Spineli, (2019) <doi:10.1016/j.jclinepi.2018.09.002>, and Spineli et al., (2021) <doi:10.1002/jrsm.1478>). The robustness to primary analysis results can also be investigated using a novel intuitive index (see Spineli et al., (2021) <doi:10.1177/0962280220983544>). Methods to evaluate the transitivity assumption using trial dissimilarities and hierarchical clustering are provided (see Spineli, (2024) <doi:10.1186/s12874-024-02436-7>, and Spineli et al., (2025) <doi:10.1002/sim.70068>). A novel index to facilitate interpretation of local inconsistency is also available (see Spineli, (2024) <doi:10.1186/s13643-024-02680-4>) The package also offers a rich, user-friendly visualisation toolkit that aids in appraising and interpreting the results thoroughly and preparing the manuscript for journal submission. The visualisation tools comprise the network plot, forest plots, panel of diagnostic plots, heatmaps on the extent of missing participant outcome data in the network, league heatmaps on estimation and prediction, rankograms, Bland-Altman plot, leverage plot, deviance scatterplot, heatmap of robustness, barplot of Kullback-Leibler divergence, heatmap of comparison dissimilarities and dendrogram of comparison clustering. The package also allows the user to export the results to an Excel file at the working directory.
Version: 0.5.0 Depends: R (≥ 4.0.0) Imports: cluster, coda (≥ 0.13), dendextend, gemtc, ggfittext, ggplot2, ggpubr, ggrepel, heatmaply, igraph, knitr, MASS, Matrix, R2jags, reshape2, scales, stringr, writexl Suggests: metafor, netmeta, rmarkdown, testthat (≥ 3.0.0) Published: 2025-06-13 DOI: 10.32614/CRAN.package.rnmamod Author: Loukia Spineli [aut, cre], Chrysostomos Kalyvas [ctb], Katerina Papadimitropoulou [ctb] Maintainer: Loukia Spineli <Spineli.Loukia at mh-hannover.de> BugReports: https://github.com/LoukiaSpin/rnmamod/issues License: GPL (≥ 3) URL: https://CRAN.R-project.org/package=rnmamod, https://github.com/LoukiaSpin/rnmamod, https://loukiaspin.github.io/rnmamod/ NeedsCompilation: no Language: en-US Materials: NEWS In views: MetaAnalysis, MissingData CRAN checks: rnmamod results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=rnmamod to link to this page.
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