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$irunit, digits = gs("digits"), digits.se = gs("digits.se"), digits.zval = gs("digits.zval"), digits.Q = gs("digits.Q"), digits.tau2 = gs("digits.tau2"), digits.H = gs("digits.H"), digits.I2 = gs("digits.I2"), digits.prop = gs("digits.prop"), digits.weight = gs("digits.weight"), warn.backtransf = FALSE, ...)
"print"(x, ...)
"summary"(object, comb.fixed=object$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$warn, ...)
"print"(x, digits = gs("digits"), comb.fixed=x$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$irunit, bylab.nchar=35, digits.zval = gs("digits.zval"), digits.Q = gs("digits.Q"), digits.tau2 = gs("digits.tau2"), digits.H = gs("digits.H"), digits.I2 = gs("digits.I2"), warn.backtransf = FALSE, ...)
cilayout(bracket="[", separator="; ")meta, metabias, or
summary.meta.meta.x$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:summary.meta (arguments:
byvar, level, level.comb, level.prediction) and print.summary.meta
(arguments: level, level.comb, level.prediction). This functionality
is now provided by R function update.meta (or directly
in meta-analysis functions, e.g., metabin,
metacont, metagen,
metacor, and metaprop). Review Manager 5 (RevMan 5) is the current software used for
preparing and maintaining Cochrane Reviews
(http://community.cochrane.org/tools/review-production-tools/revman-5). 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.
Crippa A, Khudyakov P, Wang M, Orsini N, Spiegelman D (2016), A new measure of between-studies heterogeneity in meta-analysis. Statistics in Medicine, 35, 3661--75.
Higgins JPT & Thompson SG (2002), Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21, 1539--58.
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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