rnmamod0.5.0 package

Bayesian Network Meta-Analysis with Missing Participants

balloon_plot

Enhanced balloon plot

baseline_model

The baseline model for binary outcome

bland_altman_plot

The Bland-Altman plot

comp_clustering

End-user-ready results for comparison dissimilarity and hierarchical c...

covar_contribution_plot

Visualising study percentage contributions against a covariate

data_preparation

Prepare the dataset in the proper format for R2jags

dendro_heatmap

Dendrogram with amalgamated heatmap (Comparisons' comparability for tr...

describe_network

A function to describe the evidence base

distr_characteristics

Visualising the distribution of characteristics (Comparisons' comparab...

forestplot_juxtapose

Forest plot of juxtaposing several network meta-analysis models

forestplot_metareg

Comparator-specific forest plot for network meta-regression

forestplot

Comparator-specific forest plot for network meta-analysis

gower_distance

Weighted Gower's dissimilarity measure (Trials' comparability for tran...

heatmap_missing_dataset

Heatmap of proportion of missing participants in the dataset

heatmap_missing_network

Heatmap of proportion of missing participants in the network

heatmap_robustness

Heatmap of robustness

heter_density_plot

Visualising the density of two prior distributions for the heterogenei...

heterogeneity_param_prior

Determine the prior distribution for the heterogeneity parameter

improved_ume

Detect the frail comparisons in multi-arm trials

inconsistency_variance_prior

Function for the hyper-parameters of the prior distribution of the inc...

internal_measures_plot

Internal measures for cluster validation (Comparisons' comparability f...

intervalplot_panel_ume

A panel of interval plots for the unrelated mean effects model

kld_barplot

Barplot for the Kullback-Leibler divergence measure (missingness scena...

kld_inconsistency_user

Density plots of local inconsistency results and Kullback-Leibler dive...

kld_inconsistency

Density plots of local inconsistency results and Kullback-Leibler dive...

kld_measure

Function for the Kullback-Leibler Divergence of two normally distribut...

league_heatmap_pred

League heatmap for prediction

league_heatmap

League heatmap for estimation

league_table_absolute_user

League table for relative and absolute effects (user defined)

league_table_absolute

League table for relative and absolute effects

leverage_plot

Leverage plot

mcmc_diagnostics

Markov Chain Monte Carlo diagnostics

metareg_plot

End-user-ready results for network meta-regression

miss_characteristics

Visualising missing data in characteristics (Comparisons' comparabilit...

missingness_param_prior

Define the mean value of the normal distribution of the missingness pa...

netplot

Network plot

nodesplit_plot

End-user-ready results for the node-splitting approach

plot_study_dissimilarities

Plot Gower's disimilarity values for each study (Transitivity evaluati...

prepare_model

WinBUGS code for Bayesian pairwise or network meta-analysis and meta-r...

prepare_nodesplit

WinBUGS code for the node-splitting approach

prepare_ume

WinBUGS code for the unrelated mean effects model

rankosucra_plot

Rankograms and SUCRA curves

rnmamod-package

rnmamod: Bayesian Network Meta-analysis with Missing Participants

robustness_index_user

Robustness index when 'metafor' or 'netmeta' are used

robustness_index

Robustness index

run_metareg

Perform Bayesian pairwise or network meta-regression

run_model

Perform Bayesian pairwise or network meta-analysis

run_nodesplit

Perform the node-splitting approach

run_sensitivity

Perform sensitivity analysis for missing participant outcome data

run_series_meta

Perform a series of Bayesian pairwise meta-analyses

run_ume

Perform the unrelated mean effects model

scatterplot_sucra

Scatterplot of SUCRA values

scatterplots_dev

Deviance scatterplots

series_meta_plot

End-user-ready results for a series of pairwise meta-analyses

study_perc_contrib

Calculate study percentage contributions to summary treatment effects ...

table_tau2_prior

Predictive distributions for the between-study variance in a future me...

taylor_continuous

Pattern-mixture model with Taylor series for continuous outcome

taylor_imor

Pattern-mixture model with Taylor series for a binary outcome

ume_plot

End-user-ready results for the unrelated mean effects model

unrelated_effects_plot

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.

  • Maintainer: Loukia Spineli
  • License: GPL (>= 3)
  • Last published: 2025-06-13