Run Time-Course Model-Based Network Meta-Analysis (MBNMA) Models
Add follow-up time and arm indices to a dataset
Plot relative effects from NMAs performed at multiple time-bins
Plot cumulative ranking curves from MBNMA models
Sets default priors for JAGS model code
Plot deviance contributions from an MBNMA model
Plot fitted values from MBNMA model
Automatically generate parameters to save for a time-course MBNMA mode...
Get large vector of distinct colours using Rcolorbrewer
Generates spline basis matrices for fitting to time-course function
Create a dataset with a single time point from each study closest to s...
Create a dataset with the earliest time point only
Create a dataset with the latest time point only
Get MBNMA model values
Get current priors from JAGS model code
Calculates relative effects/mean differences at a particular time-poin...
Prepares data for JAGS
Prepares NMA data for JAGS
Studies of treatments for reducing serum uric acid in patients with go...
Identify comparisons in loops that fulfil criteria for node-splitting
Identify unique comparisons within a network (identical to MBNMAdose)
Convert arm-based MBNMA data to contrast data
Create an mb.network object
Identify comparisons in time-course MBNMA datasets that fulfil criteri...
Perform node-splitting on a MBNMA time-course network
Run MBNMA time-course models
Update MBNMA to obtain deviance contributions or fitted values
Validates that a dataset fulfils requirements for MBNMA
Write MBNMA time-course models JAGS code
MBNMAtime for Model-Based Network Meta-Analysis of longitudinal (time-...
Run an NMA model
Calculate plugin pD from a JAGS model with univariate likelihood for s...
Pipe operator
Plots predicted responses from a time-course MBNMA model
Plot histograms of rankings from MBNMA models
Forest plot for results from time-course MBNMA models
Predict effects over time in a given population based on MBNMA time-co...
Print mb.network information to the console
Print summary information from an mb.predict object
Prints a summary of rankings for each parameter
Prints basic results from a node-split to the console
Print posterior medians (95% credible intervals) for table of relative...
Calculate position of label with respect to vertex location within a c...
Rank predictions at a specific time point
Rank parameters from a time-course MBNMA
Set rank as a method
Calculates ranking probabilities for AUC from a time-course MBNMA
Identify unique comparisons relative to study reference treatment with...
Synthesise single arm studies with repeated observations of the same t...
Checks the validity of ref.resp if given as data frame
Removes any loops from MBNMA model JAGS code that do not contain any e...
Replace original priors in an MBNMA model with new priors
Print summary mb.network information to the console
Prints summary of mb.predict object
Print summary MBNMA results to the console
Takes node-split results and produces summary data frame
Emax time-course function
Fractional polynomial time-course function
Plot raw responses over time by treatment or class
Integrated Two-Component Prediction (ITP) function
Log-linear (exponential) time-course function
Polynomial time-course function
Spline time-course functions
User-defined time-course function
Adds sections of JAGS code for an MBNMA model that correspond to beta ...
Checks validity of arguments for mb.write
Adds correlation between time-course relative effects
Adds sections of JAGS code for an MBNMA model that correspond to the l...
Write the basic JAGS model code for MBNMA to which other lines of mode...
Write MBNMA time-course models JAGS code for synthesis of studies inve...
Adds sections of JAGS code for an MBNMA model that correspond to alpha...
Fits Bayesian time-course models for model-based network meta-analysis (MBNMA) that allows inclusion of multiple time-points from studies. Repeated measures over time are accounted for within studies by applying different time-course functions, following the method of Pedder et al. (2019) <doi:10.1002/jrsm.1351>. The method allows synthesis of studies with multiple follow-up measurements that can account for time-course for a single or multiple treatment comparisons. Several general time-course functions are provided; others may be added by the user. Various characteristics can be flexibly added to the models, such as correlation between time points and 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.