Dose-Response MBNMA Models
Add arm indices and agent identifiers to a dataset
Calculates values for EDx from an Emax model, the dose at which x% of ...
Update model fit statistics depending on calculation for pD
Check if all nodes in the network are connected (identical to function...
Plot cumulative ranking curves from MBNMA models
Sets default priors for JAGS model code
Emax dose-response function
Dev-dev plot for comparing deviance contributions from two models
Plot deviance contributions from an MBNMA model
Exponential dose-response function
Fractional polynomial dose-response function
Integrated Two-Component Prediction (ITP) function
Log-linear (exponential) dose-response function
Agent-specific dose-response function
Non-parameteric dose-response functions
Polynomial dose-response function
Adds placebo comparisons for dose-response relationship
Drop treatments from multi-arm (>2) studies for node-splitting
Drop studies that are not connected to the network reference treatment
Spline dose-response functions
User-defined dose-response function
Plot fitted values from MBNMA model
Automatically generate parameters to save for a dose-response MBNMA mo...
Generates spline basis matrices for fitting to dose-response function
Get current priors from JAGS model code
Calculates league table of effects between treatments in MBNMA and/or ...
Prepares data for JAGS
Identify comparisons in loops that fulfill criteria for node-splitting
Identify unique comparisons within a network
Create an mbnma.network object
Node-splitting model for testing consistency at the treatment level us...
Run MBNMA dose-response models
Update MBNMA to monitor deviance nodes in the model
Validates that a dataset fulfills requirements for MBNMA
Write MBNMA dose-response model JAGS code
MBNMAdose for dose-response Model-Based Network Meta-Analysis
Node-splitting model for testing consistency at the treatment-level
Run an NMA model
Convert normal distribution parameters to corresponding log-normal dis...
Calculate plugin pD from a JAGS model with univariate likelihood for s...
Pipe operator
Plots predicted responses from a dose-response MBNMA model
Plot histograms of rankings from MBNMA models
Forest plot for results from dose-response MBNMA models
Predict responses for different doses of agents in a given population ...
Print mbnma.network information to the console
Print summary information from an mbnma.predict object
Prints summary information about an mbnma.rank object
Prints summary results from an nma.nodesplit object
Prints summary results from a nodesplit object
Print posterior medians (95% credible intervals) for table of relative...
Rank predicted doses of different agents
Rank parameter estimates
Set rank as a method
Rank relative effects obtained between specific doses
Assigns agent or class variables numeric identifiers
Synthesise single arm dose = 0 / placebo studies to estimate E0
Rescale data depending on the link function provided
Print summary mbnma.network information to the console
Produces a summary data frame from an mbnma.predict object
Generates summary data frames for an mbnma.rank object
Print summary of MBNMA results to the console
Generates a summary data frame for nma.nodesplit objects
Generates a summary data frame for nodesplit objects
Fits Bayesian dose-response model-based network meta-analysis (MBNMA) that incorporate multiple doses within an agent by modelling different dose-response functions, as described by Mawdsley et al. (2016) <doi:10.1002/psp4.12091>. By modelling dose-response relationships this can connect networks of evidence that might otherwise be disconnected, and can improve precision on treatment estimates. Several common dose-response functions are provided; others may be added by the user. Various characteristics and assumptions can be flexibly added to the models, such as shared class effects. The consistency of direct and indirect evidence in the network can be assessed using unrelated mean effects models and/or by node-splitting at the treatment level.