Package: rnmamod 0.4.0

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, (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 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.

Authors:Loukia Spineli [aut, cre], Chrysostomos Kalyvas [ctb], Katerina Papadimitropoulou [ctb]

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rnmamod.pdf |rnmamod.html✨
rnmamod/json (API)
NEWS

# Install 'rnmamod' in R:
install.packages('rnmamod', repos = c('https://loukiaspin.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/loukiaspin/rnmamod/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

58 exports 4 stars 2.44 score 161 dependencies 9 scripts 360 downloads

Last updated 11 days agofrom:1f805c04aa. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 06 2024
R-4.5-winOKSep 06 2024
R-4.5-linuxOKSep 06 2024
R-4.4-winOKSep 06 2024
R-4.4-macOKSep 06 2024
R-4.3-winOKSep 06 2024
R-4.3-macOKSep 06 2024

Exports:balloon_plotbaseline_modelbland_altman_plotcomp_clusteringdata_preparationdendro_heatmapdescribe_networkdistr_characteristicsforestplotforestplot_juxtaposeforestplot_metareggower_distanceheatmap_missing_datasetheatmap_missing_networkheatmap_robustnessheter_density_plotheterogeneity_param_priorimproved_umeinconsistency_variance_priorinternal_measures_plotintervalplot_panel_umekld_barplotkld_inconsistencykld_inconsistency_userkld_measureleague_heatmapleague_heatmap_predleague_table_absoluteleague_table_absolute_userleverage_plotmcmc_diagnosticsmetareg_plotmiss_characteristicsmissingness_param_priornetplotnodesplit_plotprepare_modelprepare_nodesplitprepare_umerainbow_similaritiesrankosucra_plotrobustness_indexrobustness_index_userrun_metaregrun_modelrun_nodesplitrun_sensitivityrun_series_metarun_umescatterplot_sucrascatterplots_devseries_meta_plottable_tau2_priortaylor_continuoustaylor_imorume_plotunrelated_effects_plotweight_defined

Dependencies:abindaskpassassertthatbackportsbase64encbitbit64bootbroombslibcacachemcallrcarcarDataclicliprclustercodacodetoolscolorspacecommonmarkCompQuadFormcorrplotcowplotcpp11crayoncrosstalkcurldata.tabledendextenddenstripDerivdigestdoBydplyreggevaluatefansifarverfastmapfontawesomeforcatsforeachfsgclusgemtcgenericsggfittextggplot2ggpubrggrepelggsciggsignifgluegridExtragridtextgtableheatmaplyhighrhmshtmltoolshtmlwidgetshttrigraphisobanditeratorsjpegjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelme4magrittrmarkdownMASSmathjaxrMatrixMatrixModelsmcmcplotsmemoisemetametadatmetaformgcvmicrobenchmarkmimeminqamodelrmunsellnlmenloptrnnetnumDerivopensslpbapplypbkrtestpermutepillarpkgconfigplotlyplyrpngpolynomprettyunitsprocessxprogresspromisespspurrrqapquantregR2jagsR2WinBUGSR6rappdirsRColorBrewerRcppRcppEigenreadrregistryreshape2RglpkrjagsrlangrmarkdownrstatixsassscalesseriationsfsmiscshadesslamSparseMstringistringrsurvivalsystibbletidyrtidyselecttinytextruncnormTSPtzdbutf8vctrsveganviridisviridisLitevroomwebshotwithrwritexlxfunxml2yaml

Description of the network

Loukia M. Spineli

Rendered fromnetwork_description.Rmdusingknitr::rmarkdownon Sep 06 2024.

Last update: 2023-07-07
Started: 2021-10-04

Perform network meta-analysis

Loukia M. Spineli

Rendered fromperform_network_metaanalysis.Rmdusingknitr::rmarkdownon Sep 06 2024.

Last update: 2023-07-07
Started: 2021-10-22

Readme and manuals

Help Manual

Help pageTopics
rnmamod: Bayesian Network Meta-analysis with Missing Participantsrnmamod-package rnmamod
Enhanced balloon plotballoon_plot
The baseline model for binary outcomebaseline_model
The Bland-Altman plotbland_altman_plot
End-user-ready results for comparison dissimilarity and hierarchical clustering (Comparisons' comparability for transitivity evaluation)comp_clustering
Prepare the dataset in the proper format for R2jagsdata_preparation
Dendrogram with amalgamated heatmap (Comparisons' comparability for transitivity evaluation)dendro_heatmap
A function to describe the evidence basedescribe_network
Visualising the distribution of characteristics (Comparisons' comparability for transitivity evaluation)distr_characteristics
Comparator-specific forest plot for network meta-analysisforestplot
Forest plot of juxtaposing several network meta-analysis modelsforestplot_juxtapose
Comparator-specific forest plot for network meta-regressionforestplot_metareg
Gower's dissimilarity measure (Trials' comparability for transitivity evaluation)gower_distance
Heatmap of proportion of missing participants in the datasetheatmap_missing_dataset
Heatmap of proportion of missing participants in the networkheatmap_missing_network
Heatmap of robustnessheatmap_robustness
Visualising the density of two prior distributions for the heterogeneity parameterheter_density_plot
Determine the prior distribution for the heterogeneity parameterheterogeneity_param_prior
Detect the frail comparisons in multi-arm trialsimproved_ume
Function for the hyper-parameters of the prior distribution of the inconsistency variance (network meta-analysis with random inconsistency effects)inconsistency_variance_prior
Internal measures for cluster validation (Comparisons' comparability for transitivity evaluation)internal_measures_plot
A panel of interval plots for the unrelated mean effects modelintervalplot_panel_ume
Barplot for the Kullback-Leibler divergence measure (missingness scenarios)kld_barplot
Density plots of local inconsistency results and Kullback-Leibler divergence when 'rnmamod', 'netmeta' or 'gemtc' R packages are usedkld_inconsistency
Density plots of local inconsistency results and Kullback-Leibler divergence (When dataset is created by the user)kld_inconsistency_user
Function for the Kullback-Leibler Divergence of two normally distributed treatment effects for the same pairwise comparisonkld_measure
League heatmap for estimationleague_heatmap
League heatmap for predictionleague_heatmap_pred
League table for relative and absolute effectsleague_table_absolute
League table for relative and absolute effects (user defined)league_table_absolute_user
Leverage plotleverage_plot
Markov Chain Monte Carlo diagnosticsmcmc_diagnostics
End-user-ready results for network meta-regressionmetareg_plot
Visualising missing data in characteristics (Comparisons' comparability for transitivity evaluation)miss_characteristics
Define the mean value of the normal distribution of the missingness parametermissingness_param_prior
Network plotnetplot
Pharmacological interventions for chronic obstructive pulmonary diseasenma.baker2009
Pharmacological interventions for moderately severe scalp psoriasisnma.bottomley2011
Oral antithrombotics for stroke episodenma.dogliotti2014
Antidepressants in Parkinson's diseasenma.liu2013
Training modalities for patients with type 2 diabetesnma.schwingshackl2014
Antiparkinsonian interventions for later Parkinson's diseasenma.stowe2011
End-user-ready results for the node-splitting approachnodesplit_plot
Paroxetine versus placebo for depressive disorderspma.hetrick2012
Inositol versus glucose for depressive episodepma.taylor2004
WinBUGS code for Bayesian pairwise or network meta-analysis and meta-regressionprepare_model
WinBUGS code for the node-splitting approachprepare_nodesplit
WinBUGS code for the unrelated mean effects modelprepare_ume
Rainbow of Gower's similarity values for each study (Transitivity evaluation)rainbow_similarities
Rankograms and SUCRA curvesrankosucra_plot
Robustness indexrobustness_index
Robustness index when 'metafor' or 'netmeta' are usedrobustness_index_user
Perform Bayesian pairwise or network meta-regressionrun_metareg
Perform Bayesian pairwise or network meta-analysisrun_model
Perform the node-splitting approachrun_nodesplit
Perform sensitivity analysis for missing participant outcome datarun_sensitivity
Perform a series of Bayesian pairwise meta-analysesrun_series_meta
Perform the unrelated mean effects modelrun_ume
Scatterplot of SUCRA valuesscatterplot_sucra
Deviance scatterplotsscatterplots_dev
End-user-ready results for a series of pairwise meta-analysesseries_meta_plot
Predictive distributions for the between-study variance in a future meta-analysis on odds ratio or standardised mean differencetable_tau2_prior
Pattern-mixture model with Taylor series for continuous outcometaylor_continuous
Pattern-mixture model with Taylor series for a binary outcometaylor_imor
End-user-ready results for the unrelated mean effects modelume_plot
End-user-ready results for unrelated trial effects modelunrelated_effects_plot
Preparing the study weights based on Gower's dissimilarities (Transitivity evaluation)weight_defined