metainf.meta {meta}

Influence analysis in meta-analysis using leave-one-out method

Description

Performs an influence analysis. Pooled estimates are calculated omitting one study at a time.

Usage

## S3 method for class 'meta'
metainf(x, pooled, sortvar, no = 1, ...)

metainf(x, ...)

## Default S3 method:
metainf(x, ...)

Arguments

x

An object of class meta.

pooled

A character string indicating whether a common effect or random effects model is used for pooling. Either missing (see Details), "common" or "random", can be abbreviated.

sortvar

An optional vector used to sort the individual studies (must be of same length as x$TE).

no

A numeric specifying which meta-analysis results to consider.

...

Additional arguments (ignored).

Details

Performs a influence analysis; pooled estimates are calculated omitting one study at a time. Studies are sorted according to sortvar.

Information from object x is utilised if argument pooled is missing. A common effect model is assumed (pooled="common") if argument x$common is TRUE; a random effects model is assumed (pooled="random") if argument x$random is TRUE and x$common is FALSE.

Value

An object of class "meta" and "metainf" with corresponding generic functions (see meta-object).

The following list elements have a different meaning:

TE, seTE

Estimated treatment effect and standard error of pooled estimate in influence analysis.

lower, upper

Lower and upper confidence interval limits.

statistic

Statistic for test of overall effect.

pval

P-value for test of overall effect.

studlab

Study label describing omission of studies.

w

Sum of weights from common effect or random effects model.

TE.common, seTE.common

Value is NA.

TE.random, seTE.random

Value is NA.

Q

Value is NA.

Author(s)

Guido Schwarzer [e-mail]

References

Cooper H & Hedges LV (1994): The Handbook of Research Synthesis. Newbury Park, CA: Russell Sage Foundation

See Also

metabin, metacont, print.meta

Examples

data(Fleiss1993bin)
m1 <- metabin(d.asp, n.asp, d.plac, n.plac,
  data = Fleiss1993bin, studlab = study, sm = "RR", method = "I")
m1
metainf(m1)
metainf(m1, pooled = "random")

forest(metainf(m1))
forest(metainf(m1), layout = "revman5")
forest(metainf(m1, pooled = "random"))

metainf(m1, sortvar = study)
metainf(m1, sortvar = 7:1)

m2 <- update(m1, title = "Fleiss1993bin meta-analysis", backtransf = FALSE)
metainf(m2)

data(Fleiss1993cont)
m3 <- metacont(n.psyc, mean.psyc, sd.psyc, n.cont, mean.cont, sd.cont,
  data = Fleiss1993cont, sm = "SMD")
metainf(m3)