rnmamod - Bayesian Network Meta-Analysis with Missing Participants
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, (2021)
<doi:10.1177/0272989X211068005>), and sensitivity analysis (see
Spineli et al., (2021) <doi:10.1002/jrsm.1478> and 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:10.1177/0962280220983544>). The robustness to
primary analysis results can also be investigated using a novel
intuitive index (see Spineli et al., (2021)
<doi:10.1002/jrsm.1478> and Spineli et al., (2021)
<doi:10.1186/s12916-021-02195-y>). 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.
