netmeta (version 0.9-4)

netrank: Frequentist method to rank treatments in network

Description

Ranking treatments in frequentist network meta-analysis without resampling methods.

Usage

netrank(x, small.values="good")
# S3 method for netrank
print(x,
      comb.fixed = x$x$comb.fixed, comb.random = x$x$comb.random,
      sort=TRUE, digits=max(4, .Options$digits - 3), ...)

Arguments

x

An object of class netmeta (netrank function) or netrank (print function).

comb.fixed

A logical indicating whether to print P-scores for fixed effect model.

comb.random

A logical indicating whether to print P-scores for random effects model.

small.values

A character string specifying whether small treatment effects indicate a beneficial ("good") or harmful ("bad") effect, can be abbreviated.

sort

A logical indicating whether printout should be sorted by decreasing P-score.

digits

Minimal number of significant digits, see print.default.

…

Additional arguments passed on to print.data.frame function (used internally).

Value

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 effect model.

Pmatrix.fixed

Numeric matrix based on pairwise one-sided p-values for fixed effect 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.

Details

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 (R<U+00FC>cker 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).

References

R<U+00FC>cker G & Schwarzer G (2015), Ranking treatments in frequentist network meta-analysis works without resampling methods. BMC Medical Research Methodology, 15, 58, DOI:10.1186/s12874-015-0060-8 .

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(2), 163--171.

See Also

netmeta

Examples

data(Senn2013)

net1 <- netmeta(TE, seTE, treat1, treat2, studlab,
                data=Senn2013, sm="MD")
net2 <- netmeta(TE, seTE, treat1, treat2, studlab,
                data=Senn2013, sm="MD",
		comb.fixed=FALSE, comb.random=TRUE)
net3 <- netmeta(TE, seTE, treat1, treat2, studlab,
                data=Senn2013, sm="MD",
		comb.random=TRUE)

nr1 <- netrank(net1)
nr1
print(nr1, sort=FALSE)

nr2 <- netrank(net2)
nr2
print(nr2, sort=FALSE)

nr3 <- netrank(net3)
nr3
print(nr3, sort="fixed")
print(nr3, sort=FALSE)