View source: R/decomp.design.R
decomp.design | R Documentation |
This function performs a design-based 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 square-root of the
between-study variance |
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 meta-analysis and the assessment of the homogeneity and consistency assumption, a generalized Cochran's Q statistic for multivariate meta-analysis can be used as shown in Krahn et al. (2013). This Q statistic can be decomposed in a sum of within-design Q statistics and one between-designs Q statistic that incorporates the concept of design inconsistency, see Higgins et al. (2012).
For assessing the inconsistency in a random effects model, the
between-designs Q statistic can be calculated based on a full
design-by-treatment interaction random effects model (see Higgins
et al., 2012). This Q statistic will be automatically given in the
output (\tau^2
estimated by the method of moments (see Jackson
et al., 2012). Alternatively, the square-root of the between-study
variance can be prespecified by argument tau.preset
to
obtain a between-designs Q statistic (in Q.inc.random
), its
design-specific 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 between-designs 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 meta-analysis 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 design-specific decomposition
of the within-designs Q statistic ( |
Q.inc.detach |
Data frame with between-designs 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 between-designs Q statistic
( |
Q.inc.random.preset |
Data frame with between-designs 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 square-root of
between-study variance |
residuals.inc.detach |
Matrix with residuals, i.e. design-specific 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@uni-mainz.de
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
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
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 meta-analysis 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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