Package: bnma 1.6.0

Michael Seo

bnma: Bayesian Network Meta-Analysis using 'JAGS'

Network meta-analyses using Bayesian framework following Dias et al. (2013) <doi:10.1177/0272989X12458724>. Based on the data input, creates prior, model file, and initial values needed to run models in 'rjags'. Able to handle binomial, normal and multinomial arm-level data. Can handle multi-arm trials and includes methods to incorporate covariate and baseline risk effects. Includes standard diagnostics and visualization tools to evaluate the results.

Authors:Michael Seo [aut, cre], Christopher Schmid [aut]

bnma_1.6.0.tar.gz
bnma_1.6.0.tar.gz(r-4.5-noble)bnma_1.6.0.tar.gz(r-4.4-noble)
bnma_1.6.0.tgz(r-4.4-emscripten)bnma_1.6.0.tgz(r-4.3-emscripten)
bnma.pdf |bnma.html✨
bnma/json (API)
NEWS

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

Peer review:

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:
  • blocker - Beta blockers to prevent mortality after myocardial infarction
  • cardiovascular - Trials of low dose and high dose statins for cardiovascular disease vs. placebo
  • certolizumab - Trials of certolizumab pegol (CZP) for the treatment of rheumatoid arthritis in patients
  • parkinsons - Dopamine agonists as adjunct therapy in Parkinson's disease
  • parkinsons_contrast - Dopamine agonists as adjunct therapy in Parkinson's disease
  • smoking - Smoking cessation counseling programs
  • statins - Trials of statins for cholesterol lowering vs. placebo or usual care
  • thrombolytic - Thrombolytic drugs and percutaneous transluminal coronary angioplasty

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.70 score 7 scripts 372 downloads 1 mentions 29 exports 32 dependencies

Last updated 10 months agofrom:b4258b7de9. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 07 2024
R-4.5-linuxNOTENov 07 2024

Exports:calculate.contrast.deviancecalculate.deviancecontrast.network.datacontrast.network.deviance.plotcontrast.network.leverage.plotcontrast.network.rundraw.network.graphnetwork.autocorr.diagnetwork.autocorr.plotnetwork.covariate.plotnetwork.cumrank.tx.plotnetwork.datanetwork.deviance.plotnetwork.forest.plotnetwork.gelman.diagnetwork.gelman.plotnetwork.inconsistency.plotnetwork.leverage.plotnetwork.rank.tx.plotnetwork.runnodesplit.network.datanodesplit.network.runrank.txrelative.effectsrelative.effects.tablesucraume.network.dataume.network.runvariance.tx.effects

Dependencies:clicodacolorspacecpp11fansifarverggplot2gluegtableigraphisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrjagsrlangscalestibbleutf8vctrsviridisLitewithr

Bayesian network meta analysis

Michael Seo and Christopher Schmid

Rendered frombnma.Rmdusingknitr::rmarkdownon Nov 07 2024.

Last update: 2024-02-11
Started: 2024-02-11

Readme and manuals

Help Manual

Help pageTopics
bnma: A package for network meta analysis using Bayesian methodsbnma-package
Beta blockers to prevent mortality after myocardial infarctionblocker
Find deviance statistics such as DIC and pD.calculate.contrast.deviance
Find deviance statistics such as DIC and pD.calculate.deviance
Trials of low dose and high dose statins for cardiovascular disease vs. placebocardiovascular
Trials of certolizumab pegol (CZP) for the treatment of rheumatoid arthritis in patientscertolizumab
Make a network object for contrast-level data containing data, priors, and a JAGS model filecontrast.network.data
Make a contrast network deviance plotcontrast.network.deviance.plot
Make a leverage plotcontrast.network.leverage.plot
Run the model using the network objectcontrast.network.run
Draws network graph using igraph packagedraw.network.graph
Generate autocorrelation diagnostics using coda packagenetwork.autocorr.diag
Generate autocorrelation plot using coda packagenetwork.autocorr.plot
Make a covariate plotnetwork.covariate.plot
Create a treatment cumulative rank plotnetwork.cumrank.tx.plot
Make a network object containing data, priors, and a JAGS model filenetwork.data
Make a deviance plotnetwork.deviance.plot
Draws forest plotnetwork.forest.plot
Use coda package to find Gelman-Rubin diagnosticsnetwork.gelman.diag
Use coda package to plot Gelman-Rubin diagnostic plotnetwork.gelman.plot
Plotting comparison of posterior mean deviance in the consistency model and inconsistency modelnetwork.inconsistency.plot
Make a leverage plotnetwork.leverage.plot
Create a treatment rank plotnetwork.rank.tx.plot
Run the model using the network objectnetwork.run
Make a network object containing data, priors, and a JAGS model filenodesplit.network.data
Run the model using the nodesplit network objectnodesplit.network.run
Dopamine agonists as adjunct therapy in Parkinson's diseaseparkinsons
Dopamine agonists as adjunct therapy in Parkinson's diseaseparkinsons_contrast
Plot traceplot and posterior density of the result using contrast dataplot.contrast.network.result
Plot traceplot and posterior density of the resultplot.network.result
Plot traceplot and posterior density of the result using contrast dataplot.ume.network.result
Create a treatment rank tablerank.tx
Find relative effects for base treatment and comparison treatmentsrelative.effects
Make a summary table for relative effectsrelative.effects.table
Smoking cessation counseling programssmoking
Trials of statins for cholesterol lowering vs. placebo or usual carestatins
Calculate SUCRAsucra
Summarize result run by 'contrast.network.run'summary.contrast.network.result
Summarize result run by 'network.run'summary.network.result
Summarize result run by 'nodesplit.network.run'summary.nodesplit.network.result
Summarize result run by 'ume.network.run'summary.ume.network.result
Thrombolytic drugs and percutaneous transluminal coronary angioplastythrombolytic
Make a network object for the unrelated mean effects model (inconsistency model) containing data, priors, and a JAGS model fileume.network.data
Run the model using the network objectume.network.run
Calculate correlation matrix for multinomial heterogeneity parameter.variance.tx.effects