netmeta (version 3.2-0)

netheat: Net heat plot

Description

This function creates a net heat plot, a graphical tool for locating inconsistency in network meta-analyses.

Usage

netheat(
  x,
  random = FALSE,
  tau.preset = NULL,
  showall = TRUE,
  nchar.trts = x$nchar.trts,
  ...
)

Arguments

x

An object of class netmeta.

random

A logical indicating whether the net heat plot should be based on a random effects model.

tau.preset

An optional value for the square-root of the between-study variance \(\tau^2\) for a random effects model on which the net heat plot will be based.

showall

A logical indicating whether results should be shown for all designs or only a sensible subset (see Details).

nchar.trts

A numeric defining the minimum number of characters used to create unique treatment names.

...

Additional arguments.

Author

Ulrike Krahn

Details

The net heat plot is a matrix visualization proposed by Krahn et al. (2013) that highlights hot spots of inconsistency between specific direct evidence in the whole network and renders transparent possible drivers.

In this plot, the area of a gray square displays the contribution of the direct estimate of one design in the column to a network estimate in a row. In combination, the colors show the detailed change in inconsistency when relaxing the assumption of consistency for the effects of single designs. The colors on the diagonal represent the inconsistency contribution of the corresponding design. The colors on the off-diagonal are associated with the change in inconsistency between direct and indirect evidence in a network estimate in the row after relaxing the consistency assumption for the effect of one design in the column. Cool colors indicate an increase and warm colors a decrease: the stronger the intensity of the color, the greater the difference between the inconsistency before and after the detachment. So, a blue colored element indicates that the evidence of the design in the column supports the evidence in the row. A clustering procedure is applied to the heat matrix in order to find warm colored hot spots of inconsistency. In the case that the colors of a column corresponding to design \(d\) are identical to the colors on the diagonal, the detaching of the effect of design \(d\) dissolves the total inconsistency in the network.

The pairwise contrasts corresponding to designs of three- or multi-arm studies are marked by '_' following the treatments of the design.

Designs where only one treatment is involved in other designs of the network or where the removal of corresponding studies would lead to a splitting of the network do not contribute to the inconsistency assessment. By default (showall = TRUE), these designs are not incorporated into the net heat plot. If showall = FALSE, additional designs with minimal contribution to the inconsistency Q statistic are not incorporated (i.e., designs with abs(Q.inc.design) <= .Machine$double.eps^0.5).).

In the case of random = TRUE, the net heat plot is based on a random effects model generalised for multivariate meta-analysis in which the between-study variance \(\tau^2\) is estimated by the method of moments (see Jackson et al., 2012) and embedded in a full design-by-treatment interaction model (see Higgins et al., 2012).

References

Krahn U, Binder H, König J (2013): A graphical tool for locating inconsistency in network meta-analyses. BMC Medical Research Methodology, 13, 35

Jackson D, White IR and Riley RD (2012): Quantifying the impact of between-study heterogeneity in multivariate meta-analyses. Statistics in Medicine, 31, 3805--20

Higgins JPT, Jackson D, Barrett JK, Lu G, Ades AE, White IR (2012): Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Research Synthesis Methods, 3, 98--110

See Also

netmeta

Examples

data(Senn2013)

# Only consider first five studies (to reduce runtime of example)
#
studies <- unique(Senn2013$studlab)
Senn2013.5 <- subset(Senn2013, studlab %in% studies[1:5])

# Conduct network meta-analysis with placebo as reference treatment
#
net1 <- netmeta(TE, seTE, treat1, treat2, studlab,
  data = Senn2013.5, sm = "MD", reference = "plac")
        
# Generate a net heat plot based on the common effects model
#
netheat(net1) 

# \donttest{
# Conduct network meta-analysis with placebo as reference treatment
#
net2 <- netmeta(TE, seTE, treat1, treat2, studlab,
  data = Senn2013, sm = "MD", reference = "plac")
        
# Generate a net heat plot based on the random effects model
#
netheat(net2) 
# }