netmeta (version 3.1-1)

netrank: Frequentist method to rank treatments in network

Description

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

Usage

netrank(
  x,
  small.values = x$small.values,
  method,
  nsim,
  common = x$common,
  random = x$random,
  warn.deprecated = gs("warn.deprecated"),
  ...
)
# S3 method for netrank
print(
  x,
  common = x$common,
  random = x$random,
  sort = TRUE,
  digits = gs("digits.prop"),
  warn.deprecated = gs("warn.deprecated"),
  ...
)

Value

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.

Arguments

x

An object of class netmeta or rankogram.

small.values

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

method

A character string specifying whether the "P-score" or "SUCRA" ranking metric will be calculated.

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 print.data.frame function (used internally).

sort

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

digits

Minimal number of significant digits, see print.default.

Author

Gerta Rücker , Guido Schwarzer , Theodoros Papakonstantinou

Details

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.

References

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

See Also

netmeta, rankogram, plot.rankogram

Examples

data(smokingcessation)

pw1 <- pairwise(list(treat1, treat2, treat3),
  event = list(event1, event2, event3), n = list(n1, n2, n3),
  data = smokingcessation, sm = "OR")
net1 <- netmeta(pw1)

netrank(net1)

# \donttest{
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)
# }