mtc.anohe: Analysis of heterogeneity (ANOHE)

Description

(EXPERIMENTAL) Generate an analysis of heterogeneity for the given network. Three types of model are estimated: unrelated study effects, unrelated mean effects, and consistency. Output of the summary function can passed to plot for a visual representation.

Usage

mtc.anohe(network, ...)

Arguments

network

An object of S3 class mtc.network.

...

Arguments to be passed to mtc.run or mtc.model. This can be used to set the likelihood/link or the number of iterations, for example.

Value

For mtc.anohe: an object of class mtc.anohe. This is a list with the following elements:
result.useThe result for the USE model (see mtc.run).
result.umeThe result for the UME model (see mtc.run).
result.consThe result for the consistency model (see mtc.run).
For summary: an object of class mtc.anohe.summary. This is a list with the following elements:
cons.modelGenerated consistency model.
studyEffectsStudy-level effect summaries (multi-arm trials downweighted).
pairEffectsPair-wise pooled effect summaries (from the UME model).
consEffectsConsistency effect summaries.
indEffectsIndirect effect summaries (back-calculated).
isquared.compPer-comparison I-squared statistics.
isquared.globGlobal I-squared statistics.

encoding

utf8

Details

Analysis of heterogeneity is intended to be a unified set of statistics and a visual display that allows the simultaneous assessment of both heterogeneity and inconsistency in network meta-analysis [van Valkenhoef et al. 2014b (draft)].

mtc.anohe returns the MCMC results for all three types of model. To get appropriate summary statistics, call summary() on the results object. The summary can be plotted.