Bayesian Network Meta-Analysis using 'JAGS'
bnma: A package for network meta analysis using Bayesian methods
Find deviance statistics such as DIC and pD.
Find deviance statistics such as DIC and pD.
Make a network object for contrast-level data containing data, priors,...
Make a contrast network deviance plot
Make a leverage plot
Run the model using the network object
Draws network graph using igraph package
Generate autocorrelation diagnostics using coda package
Generate autocorrelation plot using coda package
Make a covariate plot
Create a treatment cumulative rank plot
Make a network object containing data, priors, and a JAGS model file
Make a deviance plot
Draws forest plot
Use coda package to find Gelman-Rubin diagnostics
Use coda package to plot Gelman-Rubin diagnostic plot
Plotting comparison of posterior mean deviance in the consistency mode...
Make a leverage plot
Create a treatment rank plot
Run the model using the network object
Make a network object containing data, priors, and a JAGS model file
Run the model using the nodesplit network object
Plot traceplot and posterior density of the result using contrast data
Plot traceplot and posterior density of the result
Plot traceplot and posterior density of the result using contrast data
Create a treatment rank table
Find relative effects for base treatment and comparison treatments
Make a summary table for relative effects
Calculate SUCRA
Summarize result run by contrast.network.run
Summarize result run by network.run
Summarize result run by nodesplit.network.run
Summarize result run by ume.network.run
Make a network object for the unrelated mean effects model (inconsiste...
Run the model using the network object
Calculate correlation matrix for multinomial heterogeneity parameter.
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.