gemtc (version 0.7-1)

mtc.model: Generate network meta-analysis models

Description

The mtc.model function generates network meta-analysis models from an mtc.network object.

Usage

mtc.model(network, type = "consistency", factor = 2.5, n.chain = 4,
  likelihood=NULL, link=NULL, linearModel="random",
  om.scale=NULL, hy.prior=mtc.hy.prior("std.dev", "dunif", 0, "om.scale"),
  dic=TRUE, ...)

Arguments

network

An object of S3 class mtc.network

type

A string literal indicating the type of model (either "consistency", "nodesplit", "ume", or "use").

factor

Variance scaling factor for the starting values

n.chain

Number of chains in the model

likelihood

The likelihood to be used. If unspecified, a suitable likelihood will be inferred for the given data.

link

The link function to be used. If unspecified, a suitable link function will be inferred for the given data.

linearModel

The type of linear model to be generated. Can be "random" for a random effects model, or "fixed" for a fixed effect model.

om.scale

Outcome measure scale. Represents a "very large" difference on the analysis' outcome scale. This is used to set vague priors. For the log odds-ratio, values between 2 and 5 are considered reasonable. For continuous outcomes, this depends heavily on the sp

hy.prior

Heterogeneity prior. See mtc.hy.prior.

dic

When set to TRUE, deviance and fitted values will be monitored to allow computation of the Deviance Information Criterion (DIC) at residual.

...

Additional arguments to be passed to the type-specific model generation function.

Value

• An object of class mtc.model. The following elements are descriptive:
• type
  The type of model
• network
  Network the model was generated from
• tree
  Spanning tree formed by the basic parameters
• var.scale
  The scaling factor used to over-disperse starting values
• likelihood
  The likelihood used
• link
  The link function used
• om.scale
  The scale for the variance parameters
• These elements determine the model run by JAGS/BUGS:
• n.chain
  The number of chains
• code
  Model code in JAGS/BUGS syntax. Use cat() for proper formatting.
• data
  Data in JAGS/BUGS compatible format
• inits
  Initial values in JAGS/BUGS compatible format
• monitors
  The nodes of the JAGS/BUGS model to monitor
• dic
  Whether to compute the DIC (necessary nodes will be monitored automatically)
• These latter fields can be modified to alter the statistical model, but such changes may break the model or assumptions made elsewhere in the package.

encoding

utf8

link

[Warn et al. 2002]

itemize

• consistency: ordinary consistency model. No additional parameters.[Dias et al. 2013a, van Valkenhoef et al. 2012]

item

• nodesplit: node-splitting model. Removes both arms used to estimate the direct evidence from the network of indirect evidence, rather than just one of those arms. This means that three-arm trials do not contribute any evidence in the network of indirect evidence. When relative effect data are present, these are transformed appropriately (using an assumption of normality) to enable this direct/indirect evidence split. Additional parameters: t1 and t2, which indicate the comparison to be split. [Dias et al. 2010, van Valkenhoef et al. 2014a (draft)]
• use: unrelated study effects. Models the effects within each study as if the studies are independent. No additional parameters. [van Valkenhoef et al. 2014b (draft)]
• ume: unrelated mean effects. Models the effects within each comparison as if they are independent. Does not properly handle multi-arm trials, and warns when they are present in the network. No additional parameters. [Dias et al. 2013b, van Valkenhoef et al. 2014b (draft)]

Visualization

Calling the generic plot method on an S3 mtc.model object will show a graph with the treatments as vertices and the comparisons as edges. The lines with solid arrows represent basic parameters, and the other lines represent comparisons that are not associated with any parameter but do have direct evidence from trials.

html{ The example code will generate the following graph:

parkinson.png

{Graph showing structure of a consistency model generated from parkison example data} } latex{ The example code will generate the following graph:

parkinson.pdf

{options: width=3in} } The default layout algorithm is igraph::layout.circle, other layout algorithms can be used by passing them as an optional argument called layout to plot. The igraph::layout.fruchterman.reingold algorithm also seems to produce nice results and may be better for large graphs. The default up-to version 0.1-2 was igraph::layout.kamada.kawai but could produce overlapping edges.

Details

The mtc.model function generates an object of S3 class mtc.model, which can be visualized by the generic plot function or summarized by the generic summary function.

These likelihood/links are supported:

• normal/identity: for continuous (mean difference) data.

Required columns:[mean, std.err]or[mean, std.dev, sampleSize].

Result: relative mean difference. binom/logit: for dichotomous data.

Required columns [responders, sampleSize].

Result: (log) odds ratio. binom/log: for dichotomous data.

Required columns [responders, sampleSize].

Result: (log) risk ratio. binom/cloglog: for rate (survival) data - equal follow-up in each arm.

Required columns [responders, sampleSize].

Result: (log) hazard ratio. poisson/log: for rate (survival) data.

Required columns [responders, exposure].

Result: (log) hazard ratio.

See Also

mtc.network, mtc.run

Examples

model <- mtc.model(parkinson)
plot(model)
summary(model)