Bayesian Network Meta-Analysis with Missing Participants
c(
person("Loukia", "Spineli", email = "Spineli.Loukia@mh-hannover.de",
role = c("aut", "cre")),
person("Chrysostomos", "Kalyvas", role = "ctb"),
person("Katerina", "Papadimitropoulou", role = "ctb")
)
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, and
sensitivity analysis. Missing participant outcome data are addressed in all
models of the package. 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, and barplot of Kullback-Leibler divergence. The package also
allows the user to export the results to an Excel file at the working
directory.
coda, dplyr, fdrtool, gemtc, ggfittext, ggplot2, ggpubr,
ggrepel, knitr, MASS, mcmcplots, netmeta, pcnetmeta, R2jags,
reshape2, scales, stats, utils, writexl
Loukia Spineli [aut, cre],
Chrysostomos Kalyvas [ctb],
Katerina Papadimitropoulou [ctb]

coda, dplyr, fdrtool, gemtc, ggfittext, ggplot2, ggpubr,
ggrepel, knitr, MASS, mcmcplots, netmeta, pcnetmeta, R2jags,
reshape2, scales, writexl

dplyr, fdrtool, gemtc, ggfittext, ggplot2, ggpubr, ggrepel,
knitr, MASS, mcmcplots, netmeta, pcnetmeta, R2jags, reshape2,
scales, writexl

Implementation of a Recurrent Neural Network in R.