MBNMAdose (version 0.4.1)

plot.nma: Run an NMA model

Description

Used for calculating treatment-level NMA results, either when comparing MBNMA models to models that make no assumptions regarding dose-response , or to estimate split results for overlay.split. Results can also be compared between consistency (UME=FALSE) and inconsistency (UME=TRUE) models to test the validity of the consistency assumption at the treatment-level.

Usage

# S3 method for nma
plot(x, bydose = TRUE, scales = "free_x", ...)
nma.run(
  network,
  method = "common",
  likelihood = NULL,
  link = NULL,
  priors = NULL,
  warn.rhat = TRUE,
  n.iter = 20000,
  drop.discon = TRUE,
  UME = FALSE,
  pd = "pd.kl",
  parameters.to.save = NULL,
  ...
)

Arguments

x

An object of class("nma")

bydose

A boolean object indicating whether to plot responses with dose on the x-axis (TRUE) to be able to examine potential dose-response shapes, or to plot a conventional forest plot with all treatments on the same plot (FALSE)

scales

Should scales be fixed ("fixed", the default), free ("free"), or free in one dimension ("free_x", "free_y")?

...

Arguments to be sent to ggplot2::ggplot()

network

An object of class mbnma.network.

method

Indicates the type of split (treatment-level) NMA to perform when overlay.split=TRUE. Can take either "common" or "random".

likelihood

A string indicating the likelihood to use in the model. Can take either "binomial", "normal" or "poisson". If left as NULL the likelihood will be inferred from the data.

link

A string indicating the link function to use in the model. Can take any link function defined within JAGS (e.g. "logit", "log", "probit", "cloglog"), be assigned the value "identity" for an identity link function, or be assigned the value "smd" for modelling Standardised Mean Differences using an identity link function. If left as NULL the link function will be automatically assigned based on the likelihood.

priors

A named list of parameter values (without indices) and replacement prior distribution values given as strings using distributions as specified in JAGS syntax (see jagsmanual;textualMBNMAdose).

warn.rhat

A boolean object to indicate whether to return a warning if Rhat values for any monitored parameter are >1.02 (suggestive of non-convergence).

n.iter

number of total iterations per chain (including burn in; default: 20000)

drop.discon

A boolean object that indicates whether or not to drop disconnected studies from the network.

UME

A boolean object to indicate whether to fit an Unrelated Mean Effects model that does not assume consistency and so can be used to test if the consistency assumption is valid.

pd

Can take either:

• pv only pV will be reported (as automatically outputted by R2jags).

• plugin calculates pD by the plug-in method spiegelhalter2002MBNMAdose. It is faster, but may output negative non-sensical values, due to skewed deviances that can arise with non-linear models.

• pd.kl calculates pD by the Kullback-Leibler divergence plummer2008MBNMAdose. This will require running the model for additional iterations but is a more robust calculation for the effective number of parameters in non-linear models.

• popt calculates pD using an optimism adjustment which allows for calculation of the penalized expected deviance plummer2008MBNMAdose.

parameters.to.save

A character vector containing names of parameters to monitor in JAGS

Methods (by generic)

• plot: Plot outputs from treatment-level NMA models

  Results can be plotted either as a single forest plot, or facetted by agent and plotted with increasing dose in order to identify potential dose-response relationships.

Examples

# \donttest{
# Run random effects NMA on the alogliptin dataset
alognet <- mbnma.network(alog_pcfb)
nma <- nma.run(alognet, method="random")
print(nma)
plot(nma)

# Run common effects NMA keeping treatments that are disconnected in the NMA
goutnet <- mbnma.network(gout)
nma <- nma.run(goutnet, method="common", drop.discon=FALSE)

# Run an Unrelated Mean Effects (UME) inconsistency model on triptans dataset
tripnet <- mbnma.network(triptans)
ume <- nma.run(tripnet, method="random", UME=TRUE)
# }