All functions

DR.comparisons()

Adds placebo comparisons for dose-response relationship

add_index()

Add arm indices and agent identifiers to a dataset

alog_pcfb

Studies of alogliptin for lowering blood glucose concentration in patients with type II diabetes

calc.edx()

Calculates values for EDx from an Emax model, the dose at which x% of the maximal response (Emax) is reached

changepd()

Update model fit statistics depending on calculation for pD

check.network()

Check if all nodes in the network are connected (identical to function in MBNMAtime)

cumrank()

Plot cumulative ranking curves from MBNMA models

default.priors()

Sets default priors for JAGS model code

demax()

Emax dose-response function

devdev()

Dev-dev plot for comparing deviance contributions from two models

devplot()

Plot deviance contributions from an MBNMA model

dexp()

Exponential dose-response function

dfpoly()

Fractional polynomial dose-response function

ditp()

Integrated Two-Component Prediction (ITP) function

dloglin()

Log-linear (exponential) dose-response function

dmulti()

Agent-specific dose-response function

dnonparam()

Non-parameteric dose-response functions

dpoly()

Polynomial dose-response function

drop.comp()

Drop treatments from multi-arm (>2) studies for node-splitting

drop.disconnected()

Drop studies that are not connected to the network reference treatment

dspline()

Spline dose-response functions

duser()

User-defined dose-response function

fitplot()

Plot fitted values from MBNMA model

gen.parameters.to.save()

Automatically generate parameters to save for a dose-response MBNMA model

genspline()

Generates spline basis matrices for fitting to dose-response function

get.prior()

Get current priors from JAGS model code

get.relative()

Calculates league table of effects between treatments in MBNMA and/or NMA models

getjagsdata()

Prepares data for JAGS

gout

Studies of treatments for Serum Uric Acid reduction in patients with gout

inconsistency.loops()

Identify comparisons in loops that fulfill criteria for node-splitting

mbnma.comparisons()

Identify unique comparisons within a network

plot(<mbnma.network>) mbnma.network()

Create an mbnma.network object

mbnma.nodesplit() plot(<nodesplit>)

Node-splitting model for testing consistency at the treatment level using MBNMA

mbnma.run()

Run MBNMA dose-response models

mbnma.update()

Update MBNMA to monitor deviance nodes in the model

mbnma.validate.data()

Validates that a dataset fulfills requirements for MBNMA

mbnma.write()

Write MBNMA dose-response model JAGS code

nma.nodesplit() plot(<nma.nodesplit>)

Node-splitting model for testing consistency at the treatment-level

plot(<nma>) nma.run()

Run an NMA model

norm2lnorm()

Convert normal distribution parameters to corresponding log-normal distribution parameters

osteopain

Studies of treatments for pain relief in patients with osteoarthritis

pDcalc()

Calculate plugin pD from a JAGS model with univariate likelihood for studies with repeated measurements

plot(<mbnma>)

Forest plot for results from dose-response MBNMA models

plot(<mbnma.predict>)

Plots predicted responses from a dose-response MBNMA model

plot(<mbnma.rank>)

Plot histograms of rankings from MBNMA models

predict(<mbnma>)

Predict responses for different doses of agents in a given population based on MBNMA dose-response models

print(<mbnma.network>)

Print mbnma.network information to the console

print(<mbnma.predict>)

Print summary information from an mbnma.predict object

print(<mbnma.rank>)

Prints summary information about an mbnma.rank object

print(<nma.nodesplit>)

Prints summary results from an nma.nodesplit object

print(<nodesplit>)

Prints summary results from a nodesplit object

print(<relative.array>)

Print posterior medians (95% credible intervals) for table of relative effects/mean differences between treatments/classes

psoriasis100

Studies of biologics for treatment of moderate-to-severe psoriasis (100% improvement)

psoriasis75

Studies of biologics for treatment of moderate-to-severe psoriasis (>=75% improvement)

psoriasis90

Studies of biologics for treatment of moderate-to-severe psoriasis (>=90% improvement)

rank()

Set rank as a method

rank(<mbnma>)

Rank parameter estimates

rank(<mbnma.predict>)

Rank predicted doses of different agents

rank(<relative.array>)

Rank relative effects obtained between specific doses

recode.agent()

Assigns agent or class variables numeric identifiers

ref.synth()

Synthesise single arm dose = 0 / placebo studies to estimate E0

rescale.link()

Rescale data depending on the link function provided

ssi_closure

Studies of wound closure methods to reduce Surgical Site Infections (SSI)

ssri

Studies of Selective Serotonin Reuptake Inhibitors (SSRIs) for major depression

summary(<mbnma>)

Print summary of MBNMA results to the console

summary(<mbnma.network>)

Print summary mbnma.network information to the console

summary(<mbnma.predict>)

Produces a summary data frame from an mbnma.predict object

summary(<mbnma.rank>)

Generates summary data frames for an mbnma.rank object

summary(<nma.nodesplit>)

Generates a summary data frame for nma.nodesplit objects

summary(<nodesplit>)

Generates a summary data frame for nodesplit objects

triptans

Studies of triptans for headache pain relief