print.meta: Print and summary method for objects of class meta

Description

Print and summary method for objects of class meta.

Usage

## S3 method for class 'meta':
print(x, sortvar, level=x$level, level.comb=x$level.comb,
        comb.fixed=x$comb.fixed, comb.random=x$comb.random,
        details=FALSE, ma=TRUE, digits=max(4, .Options$digits - 3), ...)
## S3 method for class 'metabias':
print(x, ...)
## S3 method for class 'meta':
summary(object, byvar=object$byvar,
        bylab=object$bylab, print.byvar=object$print.byvar,
        bystud=FALSE,
        level=object$level, level.comb=object$level.comb,
        comb.fixed=object$comb.fixed, comb.random=object$comb.random,
        print.CMH=object$print.CMH, warn=object$warn, ...)
## S3 method for class 'summary.meta':
print(x, digits = max(3, .Options$digits - 3),
        print.byvar=x$print.byvar,
        comb.fixed=x$comb.fixed, comb.random=x$comb.random,
        header=TRUE, print.CMH=x$print.CMH, bylab.nchar=35, ...)

Arguments

An object of class meta, metabias, or summary.meta.

object

An object of class meta.

sortvar

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

level

The level used to calculate confidence intervals for individual studies.

level.comb

The level used to calculate confidence intervals for pooled estimates.

comb.fixed

A logical indicating whether a fixed effect meta-analysis should be conducted.

comb.random

A logical indicating whether a random effects meta-analysis should be conducted.

header

A logical indicating whether information on title of meta-analysis, comparison and outcome should be printed at the beginning of the printout.

details

A logical indicating whether further details of individual studies should be printed.

A logical indicating whether the summary results of the meta-analysis should be printed.

byvar

An optional vector containing grouping information (must be of same length as x$TE).

bylab

A character string with a label for the grouping variable.

bylab.nchar

A numeric specifying the number of characters to print from label for the grouping variable.

bystud

A logical indicating whether results of individual studies should be printed by grouping variable.

digits

Minimal number of significant digits, see print.default.

print.byvar

A logical indicating whether the name of the grouping variable should be printed in front of the group labels. By default, the value of print.byvar is set to TRUE.

warn

A logical indicating whether the use of summary.meta in connection with metacum or metainf should result in a warning.

print.CMH

A logical indicating whether result of the Cochran-Mantel-Haenszel test for overall effect should be printed.

...

Additional arguments

Value

A list is returned by the function summary.meta with the following elements:
studyResults for individual studies (a list with elements TE, seTE, lower, upper, z, p, level).
fixedResults for fixed effect model (a list with elements TE, seTE, lower, upper, z, p, level).
randomResults for random effects model (a list with elements TE, seTE, lower, upper, z, p, level).
kNumber of studies combined in meta-analysis.
QHeterogeneity statistic Q.
tauSquare-root of between-study variance.
HHeterogeneity statistic H (a list with elements TE, lower, upper).
I2Heterogeneity statistic I2 (a list with elements TE, lower, upper), see Higgins & Thompson (2002).
k.allTotal number of studies.
Q.CMHCochran-Mantel-Haenszel test statistic for overall effect.
smA character string indicating underlying summary measure.
methodA character string with the pooling method.
callFunction call.
ci.labLabel for confidence interval.
within.fixedResult for fixed effect model within groups (a list with elements TE, seTE, lower, upper, z, p, level) - if byvar is not missing.
within.randomResult for random effects model within groups (a list with elements TE, seTE, lower, upper, z, p, level) - if byvar is not missing.
k.wNumber of studies combined within groups - if byvar is not missing.
Q.b.fixedHeterogeneity statistic Q between groups (based on fixed effect model) - if byvar is not missing.
Q.b.randomHeterogeneity statistic Q between groups (based on random effects model) - if byvar is not missing.
Q.wHeterogeneity statistic Q within groups - if byvar is not missing.
bylabLabel for grouping variable - if byvar is not missing.
by.levsLevels of grouping variable - if byvar is not missing.
comb.fixed, comb.random, print.CMHAs defined above.
versionVersion of R package meta used to create object.

Details

Review Manager 5 (RevMan 5) is the current software used for preparing and maintaining Cochrane Reviews (http://www.cc-ims.net/revman/). In RevMan 5, subgroup analyses can be defined and data from a Cochrane review can be imported to R using the function read.rm5. If a meta-analysis is then conducted using function metacr, information on subgroups is available in R (components byvar, bylab, and print.byvar, byvar in an object of class "meta"). Accordingly, by using function metacr there is no need to define subgroups in order to redo the statistical analysis conducted in the Cochrane review.

For subgroups (argument byvar not NULL), results for the fixed effect model will be printed if both arguments comb.fixed and comb.random are TRUE. In order to get results for the random effects model within subgroups, use comb.fixed==FALSE and comb.random==TRUE.

Note, for an object of type metaprop, exact binomial confidence intervals are calculated for individual study results using the R function binom.test internally. Accordingly, list elements TE, lower and upper in element study correspond to proportions and exact confidence limits on the natural scale (irrespective of the transformation used in meta-analysis). Contrary, meta-analysis results are transformed as defined by argument sm, i.e. list elements TE, lower and upper in elements fixed, random, within.fixed and within.random.

References

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

Higgins JPT & Thompson SG (2002), Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21, 1539--1558.

Examples

Run this code

data(Fleiss93cont)
meta1 <- metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, data=Fleiss93cont, sm="SMD")
summary(meta1)
summary(meta1, byvar=c(1,2,1,1,2), bylab="group")

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