Frequentist method to rank treatments in network
Ranking treatments in frequentist network meta-analysis without resampling methods.
netrank(x, small.values = x$small.values) ## S3 method for class 'netrank' print( x, comb.fixed = x$x$comb.fixed, comb.random = x$x$comb.random, sort = TRUE, digits = max(4, .Options$digits - 3), ... )
x |
An object of class |
small.values |
A character string specifying whether small
treatment effects indicate a beneficial ( |
comb.fixed |
A logical indicating whether to print P-scores for the fixed effects (common effects) model. |
comb.random |
A logical indicating whether to print P-scores for the random effects model. |
sort |
A logical indicating whether printout should be sorted by decreasing P-score. |
digits |
Minimal number of significant digits, see
|
... |
Additional arguments passed on to
|
Treatments are ranked based on a network meta-analysis. Ranking is performed by P-scores. 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 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. This interpretation is comparable to that of the Surface Under the Cumulative RAnking curve (SUCRA) which 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).
An object of class netrank with corresponding print
function. The object is a list containing the following components:
Pscore.fixed |
A named numeric vector with P-scores for fixed effects model. |
Pmatrix.fixed |
Numeric matrix based on pairwise one-sided p-values for fixed effects model. |
Pscore.random |
A named numeric vector with P-scores for random effects model. |
Pmatrix.random |
Numeric matrix based on pairwise one-sided p-values of random effects model. |
small.values, x |
As defined above. |
version |
Version of R package netmeta used to create object. |
Gerta Rücker ruecker@imbi.uni-freiburg.de, Guido Schwarzer sc@imbi.uni-freiburg.de
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
data(Senn2013)
net1 <- netmeta(TE, seTE, treat1, treat2, studlab,
data = Senn2013, sm = "MD",
comb.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 = "fixed")
print(nr2, sort = FALSE)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.