R: Run Time-Course Model-Based Network Meta-Analysis (MBNMA) Models

# Run Time-Course Model-Based Network Meta-Analysis (MBNMA) Models

## Documentation for package ‘MBNMAtime’ version 0.2.6

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

add_index | Add follow-up time and arm indices to a dataset
alog_pcfb | Studies of alogliptin for lowering blood glucose concentration in patients with type II diabetes
binplot | Plot relative effects from NMAs performed at multiple time-bins
copd | Studies comparing Tiotropium, Aclidinium and Placebo for maintenance treatment of moderate to severe chronic obstructive pulmonary disease
cumrank | Plot cumulative ranking curves from MBNMA models
default.priors | Sets default priors for JAGS model code
devplot | Plot deviance contributions from an MBNMA model
diabetes | Studies comparing treatments for type 2 diabetes
fitplot | Plot fitted values from MBNMA model
gen.parameters.to.save | Automatically generate parameters to save for a time-course MBNMA model
genmaxcols | Get large vector of distinct colours using Rcolorbrewer
genspline | Generates spline basis matrices for fitting to time-course function
get.closest.time | Create a dataset with a single time point from each study closest to specified time
get.earliest.time | Create a dataset with the earliest time point only
get.latest.time | Create a dataset with the latest time point only
get.model.vals | Get MBNMA model values
get.prior | Get current priors from JAGS model code
get.relative | Calculates relative effects/mean differences at a particular time-point
getjagsdata | Prepares data for JAGS
getnmadata | Prepares NMA data for JAGS
goutSUA_CFB | Studies of treatments for reducing serum uric acid in patients with gout
goutSUA_CFBcomb | Studies of combined treatments for reducing serum uric acid in patients with gout
hyalarthritis | Studies comparing hyaluronan (HA)–based viscosupplements for osteoarthritis
inconsistency.loops | Identify comparisons in loops that fulfil criteria for node-splitting
mb.comparisons | Identify unique comparisons within a network (identical to MBNMAdose)
mb.make.contrast | Convert arm-based MBNMA data to contrast data
mb.network | Create an 'mb.network' object
mb.nodesplit | Perform node-splitting on a MBNMA time-course network
mb.nodesplit.comparisons | Identify comparisons in time-course MBNMA datasets that fulfil criteria for node-splitting
mb.run | Run MBNMA time-course models
mb.update | Update MBNMA to obtain deviance contributions or fitted values
mb.validate.data | Validates that a dataset fulfils requirements for MBNMA
mb.write | Write MBNMA time-course models JAGS code
nma.run | Run an NMA model
obesityBW_CFB | Studies of treatments for reducing body weight in patients with obesity
osteopain | Studies of pain relief medications for osteoarthritis
pDcalc | Calculate plugin pD from a JAGS model with univariate likelihood for studies with repeated measurements
plot.mb.network | Create an 'mb.network' object
plot.mb.predict | Plots predicted responses from a time-course MBNMA model
plot.mb.rank | Plot histograms of rankings from MBNMA models
plot.mbnma | Forest plot for results from time-course MBNMA models
plot.nodesplit | Perform node-splitting on a MBNMA time-course network
predict.mbnma | Predict effects over time in a given population based on MBNMA time-course models
print.mb.network | Print mb.network information to the console
print.mb.predict | Print summary information from an mb.predict object
print.mb.rank | Prints a summary of rankings for each parameter
print.nodesplit | Prints basic results from a node-split to the console
print.relative.array | Print posterior medians (95% credible intervals) for table of relative effects/mean differences between treatments/classes
radian.rescale | Calculate position of label with respect to vertex location within a circle
rank | Set rank as a method
rank.mb.predict | Rank predictions at a specific time point
rank.mbnma | Rank parameters from a time-course MBNMA
rankauc | Calculates ranking probabilities for AUC from a time-course MBNMA
ref.comparisons | Identify unique comparisons relative to study reference treatment within a network
ref.synth | Synthesise single arm studies with repeated observations of the same treatment over time
ref.validate | Checks the validity of ref.resp if given as data frame
remove.loops | Removes any loops from MBNMA model JAGS code that do not contain any expressions
replace.prior | Replace original priors in an MBNMA model with new priors
summary.mb.network | Print summary mb.network information to the console
summary.mb.predict | Prints summary of mb.predict object
summary.mbnma | Print summary MBNMA results to the console
summary.nodesplit | Takes node-split results and produces summary data frame
temax | Emax time-course function
tfpoly | Fractional polynomial time-course function
timeplot | Plot raw responses over time by treatment or class
titp | Integrated Two-Component Prediction (ITP) function
tloglin | Log-linear (exponential) time-course function
tpoly | Polynomial time-course function
tspline | Spline time-course functions
tuser | User-defined time-course function
write.beta | Adds sections of JAGS code for an MBNMA model that correspond to beta parameters
write.check | Checks validity of arguments for mb.write
write.cor | Adds correlation between time-course relative effects
write.likelihood | Adds sections of JAGS code for an MBNMA model that correspond to the likelihood
write.model | Write the basic JAGS model code for MBNMA to which other lines of model code can be added
write.ref.synth | Write MBNMA time-course models JAGS code for synthesis of studies investigating reference treatment
write.timecourse | Adds sections of JAGS code for an MBNMA model that correspond to alpha parameters