netimpact: Determine the importance of individual studies in network...

In netmeta: Network Meta-Analysis using Frequentist Methods

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,
  verbose = FALSE
)

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

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.

Author(s)

Guido Schwarzer [e-mail], Gerta Rücker [e-mail]

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

Examples

data(Franchini2012)

# Only consider first two studies (to reduce runtime of example)
#
studies <- unique(Franchini2012$Study)
p1 <- pairwise(list(Treatment1, Treatment2, Treatment3),
  n = list(n1, n2, n3),
  mean = list(y1, y2, y3), sd = list(sd1, sd2, sd3),
  data = subset(Franchini2012, Study %in% studies[1:2]),
  studlab = Study)

net1 <- netmeta(p1)
ni1 <- netimpact(net1, verbose = TRUE)
ni1

netgraph(ni1)

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