netrank  R Documentation 
Ranking treatments in frequentist network metaanalysis with and without resampling methods.
netrank( x, small.values = x$small.values, method, nsim, common = x$common, random = x$random, warn.deprecated = gs("warn.deprecated"), ... ) ## S3 method for class 'netrank' print( x, common = x$common, random = x$random, sort = TRUE, digits = gs("digits.prop"), warn.deprecated = gs("warn.deprecated"), ... )
x 
An object of class 
small.values 
A character string specifying whether small
treatment effects indicate a beneficial ( 
method 
A character string specifying whether the

nsim 
Number of simulations to calculate SUCRAs. 
common 
A logical indicating whether to print Pscores or SUCRAs for the common effects model. 
random 
A logical indicating whether to print Pscores or SUCRAs for the random effects model. 
warn.deprecated 
A logical indicating whether warnings should be printed if deprecated arguments are used. 
... 
Additional arguments passed on to

sort 
A logical indicating whether printout should be sorted by decreasing Pscore. 
digits 
Minimal number of significant digits, see

Treatments are ranked based on a network metaanalysis. Ranking is performed by a ranking metric: Pscore or SUCRA.
Pscores are based solely on the point estimates and standard errors of the network estimates. They measure the extent of certainty that a treatment is better than another treatment, averaged over all competing treatments (Rücker and Schwarzer 2015).
The Surface Under the Cumulative RAnking curve (SUCRA) is the rank
of treatment i within the range of treatments, measured on a
scale from 0 (worst) to 1 (best) (Salanti et al. 2011). A
resampling method is used to calculate SUCRAs for frequentist
network metaanalysis. The number of simulations is determine by
argument nsim
.
The interpretation of Pscores and SUCRAs is comparable.
The Pscore of treatment i is defined as the mean of all 1  P[j] where P[j] denotes the onesided Pvalue of accepting the alternative hypothesis that treatment i is better than one of the competing treatments j. Thus, if treatment i is better than many other treatments, many of these Pvalues will be small and the Pscore will be large. Vice versa, if treatment i is worse than most other treatments, the Pscore is small.
The Pscore of treatment i can be interpreted as the mean extent of certainty that treatment i is better than another treatment.
An object of class netrank
with corresponding print
function. The object is a list containing the following components:
ranking.common 
A named numeric vector with Pscores or SUCRAs for the common effects model. 
Pmatrix.common 
Numeric matrix based on pairwise onesided pvalues for the common effects model. 
ranking.random 
A named numeric vector with Pscores or SUCRAs for the random effects model. 
Pmatrix.random 
Numeric matrix based on pairwise onesided pvalues of the random effects model. 
small.values, method, x 
As defined above. 
version 
Version of R package netmeta used to create object. 
Gerta Rücker ruecker@imbi.unifreiburg.de, Guido Schwarzer sc@imbi.unifreiburg.de, Theodoros Papakonstantinou dev@tpapak.com
Rücker G, Schwarzer G (2017): Resolve conflicting rankings of outcomes in network metaanalysis: Partial ordering of treatments. Research Synthesis Methods, 8, 526–36
Salanti G, Ades AE, Ioannidis JP (2011): Graphical methods and numerical summaries for presenting results from multipletreatment metaanalysis: an overview and tutorial. Journal of Clinical Epidemiology, 64, 163–71
netmeta
, rankogram
data(Senn2013) net1 < netmeta(TE, seTE, treat1, treat2, studlab, data = Senn2013, sm = "MD", random = FALSE) nr1 < netrank(net1) nr1 print(nr1, sort = FALSE) ## Not run: net2 < netmeta(TE, seTE, treat1, treat2, studlab, data = Senn2013, sm = "MD") nr2 < netrank(net2) nr2 print(nr2, sort = "common") print(nr2, sort = FALSE) ## End(Not run) ## Not run: net3 < netmeta(TE, seTE, treat1, treat2, studlab, data = Senn2013, sm = "MD") nr3 < netrank(net3, method = "SUCRA", nsim = 100) nr3 print(nr3, sort = "common") print(nr3, sort = FALSE) ## End(Not run)
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