nma.baker2009

<p>A dataset of 21 trials comparing seven pharmacologic
  interventions with each other and placebo in patients with chronic
  obstructive pulmonary disease (COPD). The exacerbation of COPD is the
  analysed binary outcome.</p>

datasets

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 robustness to primary analysis results can
also be investigated using a novel intuitive index. Methods to evaluate the
transitivity assumption quantitatively are provided. 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.

Loukia Spineli

rnmamod

Bayesian Network Meta-Analysis with Missing Participants

Chrysostomos Kalyvas

Katerina Papadimitropoulou

nma.baker2009 function

<p>A data-frame with 21 rows of arm-based data and 17 columns (maximum
   number of 4 arms).</p>

nma.baker2009: Pharmacological interventions for chronic obstructive pulmonary disease

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