netmeta (version 3.2-0)

netimpact: Determine the importance of individual studies in network meta-analysis

Description

This function measures the importance of individual studies in network meta-analysis by the reduction of the precision if the study is removed / ignored from the network (Rücker et al., 2020).

Usage

netimpact(
  x,
  seTE.ignore = 100 * max(x$seTE, na.rm = TRUE),
  event.ignore = 0.01,
  nchar.trts = x$nchar.trts,
  nchar.studlab = x$nchar.studlab,
  verbose = FALSE
)

Value

An object of class "netimpact" with corresponding netgraph and print function. The object is a list containing the following components:

impact.common

A matrix with contributions of individual studies (columns) to comparisons (rows) under the common effects model.

impact.random

A matrix with contributions of individual studies (columns) to comparisons (rows) under the random effects model.

ignored.comparisons

List with comparisons of ignored study.

seTE.ignore, event.ignore, x

As defined above.

nets

List of all network meta-analyses (removing a single study).

version

Version of R package netmeta used to create object.

Arguments

x

An object of class netmeta.

seTE.ignore

Assumed (large) standard error in order to mimicking the removal of individual studies from the network meta-analysis (ignored for netmetabin objects).

event.ignore

Assumed event number mimicking the removal of individual studies from the network meta-analysis (considered for netmetabin objects).

nchar.trts

A numeric defining the minimum number of characters used to create unique treatment names (see Details).

nchar.studlab

A numeric defining the minimum number of characters used to create unique study labels.

verbose

A logical indicating whether information on the estimation progress should be printed.

Author

Guido Schwarzer , Gerta Rücker

References

Rücker G, Nikolakopoulou A, Papakonstantinou T, Salanti G, Riley RD, Schwarzer G (2020): The statistical importance of a study for a network meta-analysis estimate. BMC Medical Research Methodology, 20, 190

See Also

netmeta, netmetabin, netgraph.netimpact, print.netimpact, dat.franchini2012

Examples

# Only consider first two studies (to reduce runtime of example)
#
studies <- unique(dat.franchini2012$Study)
pw1 <- pairwise(list(Treatment1, Treatment2, Treatment3),
  n = list(n1, n2, n3),
  mean = list(y1, y2, y3), sd = list(sd1, sd2, sd3),
  data = subset(dat.franchini2012, Study %in% studies[1:2]),
  studlab = Study)

net1 <- netmeta(pw1)
ni1 <- netimpact(net1, verbose = TRUE)
ni1

netgraph(ni1)

# \donttest{
netgraph(ni1, plastic = TRUE)

pw2 <- pairwise(list(Treatment1, Treatment2, Treatment3),
  n = list(n1, n2, n3),
  mean = list(y1, y2, y3), sd = list(sd1, sd2, sd3),
  data = dat.franchini2012,
  studlab = Study)

net2 <- netmeta(pw2)
ni2 <- netimpact(net2, verbose = TRUE)
netgraph(ni2)
# }