Bayesian Network Meta-Analysis with Missing Participants
Enhanced balloon plot
The baseline model for binary outcome
The Bland-Altman plot
End-user-ready results for comparison dissimilarity and hierarchical c...
Visualising study percentage contributions against a covariate
Prepare the dataset in the proper format for R2jags
Dendrogram with amalgamated heatmap (Comparisons' comparability for tr...
A function to describe the evidence base
Visualising the distribution of characteristics (Comparisons' comparab...
Forest plot of juxtaposing several network meta-analysis models
Comparator-specific forest plot for network meta-regression
Comparator-specific forest plot for network meta-analysis
Weighted Gower's dissimilarity measure (Trials' comparability for tran...
Heatmap of proportion of missing participants in the dataset
Heatmap of proportion of missing participants in the network
Heatmap of robustness
Visualising the density of two prior distributions for the heterogenei...
Determine the prior distribution for the heterogeneity parameter
Detect the frail comparisons in multi-arm trials
Function for the hyper-parameters of the prior distribution of the inc...
Internal measures for cluster validation (Comparisons' comparability f...
A panel of interval plots for the unrelated mean effects model
Barplot for the Kullback-Leibler divergence measure (missingness scena...
Density plots of local inconsistency results and Kullback-Leibler dive...
Density plots of local inconsistency results and Kullback-Leibler dive...
Function for the Kullback-Leibler Divergence of two normally distribut...
League heatmap for prediction
League heatmap for estimation
League table for relative and absolute effects (user defined)
League table for relative and absolute effects
Leverage plot
Markov Chain Monte Carlo diagnostics
End-user-ready results for network meta-regression
Visualising missing data in characteristics (Comparisons' comparabilit...
Define the mean value of the normal distribution of the missingness pa...
Network plot
End-user-ready results for the node-splitting approach
Plot Gower's disimilarity values for each study (Transitivity evaluati...
WinBUGS code for Bayesian pairwise or network meta-analysis and meta-r...
WinBUGS code for the node-splitting approach
WinBUGS code for the unrelated mean effects model
Rankograms and SUCRA curves
rnmamod: Bayesian Network Meta-analysis with Missing Participants
Robustness index when 'metafor' or 'netmeta' are used
Robustness index
Perform Bayesian pairwise or network meta-regression
Perform Bayesian pairwise or network meta-analysis
Perform the node-splitting approach
Perform sensitivity analysis for missing participant outcome data
Perform a series of Bayesian pairwise meta-analyses
Perform the unrelated mean effects model
Scatterplot of SUCRA values
Deviance scatterplots
End-user-ready results for a series of pairwise meta-analyses
Calculate study percentage contributions to summary treatment effects ...
Predictive distributions for the between-study variance in a future me...
Pattern-mixture model with Taylor series for continuous outcome
Pattern-mixture model with Taylor series for a binary outcome
End-user-ready results for the unrelated mean effects model
End-user-ready results for unrelated trial effects model
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.
Useful links