netmeta (version 0.4-0)

netmeasures: Measures for characterizing a network meta-analysis

Description

This function provides measures for quantifying the direct evidence proportion, the mean path length and the minimal parallelism (the latter on aggregated and study level) as well as the evidence flow per design, see König et al. (2013). These measures support the critical evaluation of the network meta-analysis results by rendering transparent the process of data pooling.

Usage

netmeasures(x)

Arguments

x

An object of class netmeta.

Value

• A list containing the following components:
• proportion
  A named vector of the direct evidence proportion per design.
• meanpath
  A named vector of the mean path length per design.
• minpar
  A named vector of the minimal parallelism on aggregated level per design.
• minpar.study
  A named vector of the minimal parallelism on study level per design.
• H.tilde
  Design-based hat matrix with information on evidence flow per design. The number of rows is equal to the number of possible pairwise treatment comparisons and the number of columns is equal to the number of designs.

Details

The direct evidence proportion gives the contribution of direct effect estimates to a network meta-analysis combined for two-arm and multi-arm studies.

Concerning indirectness, comparisons with a mean path length beyond two should be interpreted with particular caution, as more than two direct comparisons have to be combined serially on average. Large indices of parallelism, either on study-level or on aggregated level, can be considered as supporting the validity of a network meta-analysis if there is only a small amount of heterogeneity.

The network estimates for two treatments are linear combinations of direct effect estimates comparing these or other treatments. The linear coefficients can be seen as the generalization of weights known from classical meta-analysis. These coefficients are given in the projection matrix $H$ of the underlying model. For multi-arm studies, the coefficients depend on the choice of the study-specific baseline treatment, but the flow of evidence can be made explicit as shown in König et al. (2013) and is given in H.tilde.

References

König J, Krahn U, Binder H (2013). Visualizing the flow of evidence in network meta-analysis and characterizing mixed treatment comparisons. Statistics in Medicine, 32(30), 5414--29.

See Also

netmeta

Examples

data(Senn2013)

##
## Generation of an object of class 'netmeta' with
## reference treatment 'plac', i.e. placebo
##
net1 <- netmeta(TE, seTE, treat1, treat2, studlab,
        data=Senn2013, sm="MD", reference="plac")

##
## Calculate measures
##        
m <- netmeasures(net1)

##
## Plot of minimal parallelism versus mean path length
##
plot(m$meanpath, m$minpar, pch="",
     xlab="Mean path length", ylab="Minimal parallelism")
text(m$meanpath, m$minpar, names(m$meanpath), cex=0.8)