Function reference • MBNMAtime

All functions

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

plot(<mb.network>) mb.network(): Create an mb.network object

plot(<nodesplit>) 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.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

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