meta.
"print"(x, sortvar, comb.fixed=x$comb.fixed, comb.random=x$comb.random, prediction=x$prediction, details=FALSE, ma=TRUE, backtransf=x$backtransf,
pscale=x$pscale, irscale = x$irscale, irunit = x$irunit, digits = .settings$digits, digits.se = .settings$digits.se, digits.zval = .settings$digits.zval, digits.Q = .settings$digits.Q, digits.tau2 = .settings$digits.tau2, digits.H = .settings$digits.H, digits.I2 = .settings$digits.I2, digits.prop = .settings$digits.prop, digits.weight = .settings$digits.weight,
warn.backtransf = FALSE, ...)
"print"(x, ...)
"summary"(object, comb.fixed=object$comb.fixed, comb.random=object$comb.random, prediction=object$prediction, backtransf=object$backtransf,
pscale=object$pscale, irscale = object$irscale, irunit = object$irunit, bylab=object$bylab, print.byvar=object$print.byvar,
byseparator=object$byseparator, bystud=FALSE,
print.CMH=object$print.CMH, warn=object$warn, ...)
"print"(x, digits = .settings$digits, comb.fixed=x$comb.fixed, comb.random=x$comb.random, prediction=x$prediction, print.byvar=x$print.byvar, byseparator=x$byseparator,
print.CMH=x$print.CMH, header=TRUE, backtransf=x$backtransf,
pscale=x$pscale, irscale = x$irscale, irunit = x$irunit, bylab.nchar=35, digits.zval = .settings$digits.zval, digits.Q = .settings$digits.Q, digits.tau2 = .settings$digits.tau2, digits.H = .settings$digits.H, digits.I2 = .settings$digits.I2,
warn.backtransf = FALSE, ...)
cilayout(bracket="[", separator="; ")meta, metabias, or
summary.meta.meta.x$TE).backtransf=TRUE, results for
sm="OR" are printed as odds ratios rather than log odds
ratios and results for sm="ZCOR" are printed as
correlations rather than Fisher's z transformed correlations, for
example.sm is equal to
"PLOGIT", "PLN", "PRAW", "PAS", or
"PFT".sm is equal to "IR",
"IRLN", "IRS", or "IRFT".print.default.summary.meta in connection with metacum or
metainf should result in a warning.print.default.print.default.print.default.print.default.print.default.print.default.print.default.print.default.summary.meta with the
following elements:update.meta (or directly in R
functions metabin, metacont,
metagen, metacor, and
metaprop). Review Manager 5 (RevMan 5) is the current software used for
preparing and maintaining Cochrane Reviews
(http://tech.cochrane.org/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.
Note, for an object of type metaprop, starting with version
3.7-0 of meta, list elements TE, lower and
upper in element study correspond to transformed
proportions and confidence limits (regardless whether exact
confidence limits are calculated; argument ciexact=TRUE in
metaprop function). Accordingly, the following results are based on
the same transformation defined by argument sm: list elements
TE, lower and upper in elements study,
fixed, random, within.fixed and
within.random.
R function cilayout can be utilised to change the layout to print
confidence intervals (both in printout from print.meta and
print.summary.meta function as well as in forest plots). The default
layout is "[lower; upper]". Another popular layout is "(lower -
upper)" which is used throughout an R session by using R command
cilayout("(", " - ").
Argument pscale can be used to rescale proportions,
e.g. pscale=1000 means that proportions are expressed as
events per 1000 observations. This is useful in situations with
(very) low event probabilities.
Higgins JPT & Thompson SG (2002), Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21, 1539--1558.
update.meta, metabin, metacont, metagendata(Fleiss93cont)
meta1 <- metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c,
data=Fleiss93cont, sm="SMD",
studlab=paste(study, year))
summary(meta1)
summary(update(meta1, byvar=c(1,2,1,1,2), bylab="group"))
forest(update(meta1, byvar=c(1,2,1,1,2), bylab="group"))
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