netmeta (version 1.5-0)

rankogram: 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,
  comb.fixed = x$comb.fixed,
  comb.random = x$comb.random,
  small.values = x$small.values
)
# S3 method for rankogram
print(
  x,
  comb.fixed = x$comb.fixed,
  comb.random = x$comb.random,
  digits = max(3, .Options$digits - 4),
  ...
)

Arguments

x

An object of class netmeta.

nsim

Number of simulations.

comb.fixed

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

comb.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 ("good") or harmful ("bad") effect, can be abbreviated.

digits

Minimal number of significant digits, see print.default.

…

Additional arguments for printing.

Value

An object of class rankogram with corresponding print and plot function. The object is a list containing the following components:

ranking.matrix.fixed

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

ranking.fixed

SUCRA values for the fixed 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.

nsim, comb.fixed, comb.random

As defined above

,

small.values, x

As defined above

,

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

Examples

# NOT RUN {
data(Woods2010)
p1 <- pairwise(treatment, event = r, n = N, studlab = author,
               data = Woods2010, sm = "OR")
net1 <- netmeta(p1, small.values = "good")

ran1 <- rankogram(net1, nsim = 100)
ran1

plot(ran1)      

# }