rankogram: Calculate rankogram

In netmeta: Network Meta-Analysis using Frequentist Methods

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

rankogram(
  x,
  nsim = 1000,
  common = x$common,
  random = x$random,
  small.values = x$small.values,
  cumulative.rankprob = FALSE,
  nchar.trts = x$nchar.trts,
  warn.deprecated = gs("warn.deprecated"),
  ...
)

## S3 method for class 'rankogram'
print(
  x,
  common = x$common,
  random = x$random,
  cumulative.rankprob = x$cumulative.rankprob,
  nchar.trts = x$nchar.trts,
  digits = gs("digits.prop"),
  legend = TRUE,
  warn.deprecated = gs("warn.deprecated"),
  ...
)

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 probabilites should be printed.
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 for printing.
digits	Minimal number of significant digits, see print.default.
legend	A logical indicating whether a legend should be printed.

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).

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

,

Author(s)

Theodoros Papakonstantinou [e-mail], Guido Schwarzer [e-mail]

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

Examples

data(Woods2010)
p1 <- pairwise(treatment, event = r, n = N, studlab = author,
               data = Woods2010, sm = "OR")
net1 <- netmeta(p1, small.values = "desirable")

ran1 <- rankogram(net1, nsim = 100)
ran1
print(ran1, cumulative.rankprob = TRUE)

plot(ran1)

netmeta documentation built on June 23, 2024, 9:06 a.m.