View source: R/decomp.design.R
decomp.design  R Documentation 
This function performs a designbased decomposition of Cochran's Q for assessing the homogeneity in the whole network, the homogeneity within designs, and the homogeneity/consistency between designs. It allows also an assessment of the consistency assumption after detaching the effect of single designs.
decomp.design( x, tau.preset = x$tau.preset, warn = TRUE, nchar.trts = x$nchar.trts )
x 
An object of class 
tau.preset 
An optional value for the squareroot of the betweenstudy variance τ^2 (see Details). 
warn 
A logical indicating whether warnings should be printed. 
nchar.trts 
A numeric defining the minimum number of characters used to create unique treatment names. 
In the context of network metaanalysis and the assessment of the homogeneity and consistency assumption, a generalized Cochran's Q statistic for multivariate metaanalysis can be used as shown in Krahn et al. (2013). This Q statistic can be decomposed in a sum of withindesign Q statistics and one betweendesigns Q statistic that incorporates the concept of design inconsistency, see Higgins et al. (2012).
For assessing the inconsistency in a random effects model, the
betweendesigns Q statistic can be calculated based on a full
designbytreatment interaction random effects model (see Higgins
et al., 2012). This Q statistic will be automatically given in the
output (τ^2 estimated by the method of moments (see Jackson
et al., 2012). Alternatively, the squareroot of the betweenstudy
variance can be prespecified by argument tau.preset
to
obtain a betweendesigns Q statistic (in Q.inc.random
), its
designspecific contributions Q.inc.design.random.preset
) as
well as residuals after detaching of single designs
(residuals.inc.detach.random.preset
).
Since an inconsistent treatment effect of one design can
simultaneously inflate several residuals, Krahn et al. (2013)
suggest for locating the inconsistency in a network to fit a set of
extended models allowing for example for a deviating effect of each
study design in turn. The recalculated betweendesigns Q statistics
are given in list component Q.inc.detach
. The change of the
inconsistency contribution of single designs can be investigated in
more detail by a net heat plot (see function
netheat). 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. These designs are not included in
Q.inc.detach
.
Network metaanalysis with a single design: NULL
. Otherwise,
a list containing the following components:
Q.decomp 
Data frame with Q statistics (variable 
Q.het.design 
Data frame with designspecific decomposition
of the withindesigns Q statistic ( 
Q.inc.detach 
Data frame with betweendesigns Q statistics
( 
Q.inc.design 
A named vector with contributions of single
designs to the between design Q statistic given in

Q.inc.random 
Data frame with betweendesigns Q statistic
( 
Q.inc.random.preset 
Data frame with betweendesigns Q
statistic ( 
Q.inc.design.random.preset 
A named vector with contributions
of single designs to the between design Q statistic based on a
random effects model with prespecified squareroot of
betweenstudy variance 
residuals.inc.detach 
Matrix with residuals, i.e. designspecific direct estimates minus the corresponding network estimates after detaching the design of the column. 
residuals.inc.detach.random.preset 
Matrix with residuals
analogous to 
call 
Function call. 
version 
Version of R package netmeta used to create object. 
Ulrike Krahn ulrike.krahn@bayer.com, Jochem König koenigjo@unimainz.de
Higgins JPT, Jackson D, Barrett JK, Lu G, Ades AE, White IR (2012): Consistency and inconsistency in network metaanalysis: concepts and models for multiarm studies. Research Synthesis Methods, 3, 98–110
Krahn U, Binder H, König J (2013): A graphical tool for locating inconsistency in network metaanalyses. BMC Medical Research Methodology, 13, 35
Jackson D, White IR and Riley RD (2012): Quantifying the impact of betweenstudy heterogeneity in multivariate metaanalyses. Statistics in Medicine, 31, 3805–20
netmeta, netheat
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 metaanalysis with placebo as reference treatment # net1 < netmeta(TE, seTE, treat1, treat2, studlab, data = Senn2013.5, sm = "MD", reference = "plac") # Decomposition of Cochran's Q # decomp.design(net1)
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