netmeta (version 3.2-0)

rankogram.netmeta: Calculate rankogram

Description

This function calculates the probabilities of each treatment being at each possible rank and the SUCRAs (Surface Under the Cumulative RAnking curve) in frequentist network meta-analysis.

Usage

# S3 method for netmeta
rankogram(
  x,
  nsim = gs("nsim"),
  common = x$common,
  random = x$random,
  small.values = x$small.values,
  cumulative.rankprob = FALSE,
  keep.samples = FALSE,
  nchar.trts = x$nchar.trts,
  warn.deprecated = gs("warn.deprecated"),
  ...
)

Value

An object of class rankogram with corresponding print

and plot function. The object is a list containing the following components:

ranking.matrix.common

Numeric matrix giving the probability of each treatment being at each possible rank for the common effects model.

ranking.common

SUCRA values for the common effects model.

ranking.matrix.random

Numeric matrix giving the probability of each treatment being at each possible rank for the random effects model.

ranking.random

SUCRA values for the random effects model.

cumrank.matrix.common

Numeric matrix giving the cumulative ranking probability of each treatment for the common effects model.

cumrank.matrix.random

Numeric matrix giving the cumulative ranking probability of each treatment for the random effects model.

nsim, common, random

As defined above

,

small.values, x

As defined above

,

Arguments

x

An object of class netmeta.

nsim

Number of simulations.

common

A logical indicating to compute ranking probabilities and SUCRAs for the common effects model.

random

A logical indicating to compute ranking probabilities and SUCRAs for the random effects model.

small.values

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

cumulative.rankprob

A logical indicating whether cumulative ranking probabilities should be printed.

keep.samples

A logical indicating whether to keep the generated samples.

nchar.trts

A numeric defining the minimum number of characters used to create unique treatment names.

warn.deprecated

A logical indicating whether warnings should be printed if deprecated arguments are used.

...

Additional arguments (ignored).

Author

Theodoros Papakonstantinou , Guido Schwarzer

Details

We derive a matrix showing the probability of each treatment being at each possible rank. To this aim, we use resampling from a multivariate normal distribution with estimated network effects as means and corresponding estimated variance covariance matrix. We then summarise them using the ranking metric SUCRAs (Surface Under the Cumulative RAnking curve).

References

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, netrank, plot.rankogram, dat.woods2010

Examples

pw1 <- pairwise(treatment, event = r, n = N, studlab = author,
  data = dat.woods2010, sm = "OR")
net1 <- netmeta(pw1, small.values = "desirable")

set.seed(1909) # get reproducible results
ran1 <- rankogram(net1, nsim = 100)
ran1
print(ran1, cumulative.rankprob = TRUE)

# Also print mean ranks
summary(ran1)

plot(ran1)