netrank | R Documentation |
Ranking treatments in frequentist network meta-analysis 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 P-scores or SUCRAs for the common effects model. |
random |
A logical indicating whether to print P-scores 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 P-score. |
digits |
Minimal number of significant digits, see
|
Treatments are ranked based on a network meta-analysis. Ranking is performed by a ranking metric: P-score or SUCRA.
P-scores 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 meta-analysis. The number of simulations is determine by
argument nsim
.
The interpretation of P-scores and SUCRAs is comparable.
The P-score of treatment i is defined as the mean of all 1 - P[j] where P[j] denotes the one-sided P-value 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 P-values will be small and the P-score will be large. Vice versa, if treatment i is worse than most other treatments, the P-score is small.
The P-score 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 P-scores or SUCRAs for the common effects model. |
Pmatrix.common |
Numeric matrix based on pairwise one-sided p-values for the common effects model. |
ranking.random |
A named numeric vector with P-scores or SUCRAs for the random effects model. |
Pmatrix.random |
Numeric matrix based on pairwise one-sided p-values of the random effects model. |
small.values , method , x |
As defined above. |
version |
Version of R package netmeta used to create object. |
Gerta Rücker gerta.ruecker@uniklinik-freiburg.de, Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de, Theodoros Papakonstantinou dev@tpapak.com
Rücker G, Schwarzer G (2017): Resolve conflicting rankings of outcomes in network meta-analysis: 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 multiple-treatment meta-analysis: an overview and tutorial. Journal of Clinical Epidemiology, 64, 163–71
netmeta
, rankogram
,
plot.rankogram
data(smokingcessation)
p1 <- pairwise(list(treat1, treat2, treat3),
event = list(event1, event2, event3), n = list(n1, n2, n3),
data = smokingcessation, sm = "OR")
net1 <- netmeta(p1)
netrank(net1)
## Not run:
data(Senn2013)
net2 <- netmeta(TE, seTE, treat1, treat2, studlab,
data = Senn2013, sm = "MD", random = FALSE)
nr2 <- netrank(net2)
nr2
print(nr2, sort = FALSE)
net3 <- netmeta(TE, seTE, treat1, treat2, studlab,
data = Senn2013, sm = "MD")
nr3 <- netrank(net3)
nr3
print(nr3, sort = "common")
print(nr3, sort = FALSE)
net4 <- netmeta(TE, seTE, treat1, treat2, studlab,
data = Senn2013, sm = "MD")
nr4 <- netrank(net4, method = "SUCRA", nsim = 100)
nr4
print(nr4, sort = "common")
print(nr4, sort = FALSE)
## End(Not run)
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