Print and summary method for objects of class meta.
# S3 method for 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,
digits=gs("digits"), digits.se=gs("digits.se"),
digits.tau2=gs("digits.tau2"), digits.I2=gs("digits.I2"),
digits.prop=gs("digits.prop"), digits.weight=gs("digits.weight"),
big.mark=gs("big.mark"),
warn.backtransf=FALSE,
...)# S3 method for metabias
print(x, ...)
# S3 method for meta
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, ...)
# S3 method for summary.meta
print(x,
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=gs("digits"),
digits.zval=gs("digits.zval"),
digits.pval=max(gs("digits.pval"), 2),
digits.pval.Q=max(gs("digits.pval.Q"), 2),
digits.Q=gs("digits.Q"), digits.tau2=gs("digits.tau2"),
digits.H=gs("digits.H"), digits.I2=gs("digits.I2"),
scientific.pval=gs("scientific.pval"), big.mark=gs("big.mark"),
print.I2=gs("print.I2"), print.H=gs("print.H"),
print.Rb=gs("print.Rb"),
text.tau2=gs("text.tau2"), text.I2=gs("text.I2"),
text.Rb=gs("text.Rb"),
warn.backtransf=FALSE,
...)
cilayout(bracket="[", separator="; ")
An object of class meta, metabias, or
summary.meta.
An object of class meta.
An optional vector used to sort the individual studies
(must be of same length as x$TE).
A logical indicating whether a fixed effect meta-analysis should be conducted.
A logical indicating whether a random effects meta-analysis should be conducted.
A logical indicating whether a prediction interval should be printed.
A character string with a label for the grouping variable.
A logical indicating whether the name of the grouping variable should be printed in front of the group labels.
A character string defining the separator between label and levels of grouping variable.
A logical indicating whether information on title of meta-analysis, comparison and outcome should be printed at the beginning of the printout.
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.
A logical indicating whether printed results
should be back transformed. If 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.
A numeric giving scaling factor for printing of single
event probabilities, i.e. if argument sm is equal to
"PLOGIT", "PLN", "PRAW", "PAS", or
"PFT".
A numeric defining a scaling factor for printing of
rates, i.e. if argument sm is equal to "IR",
"IRLN", "IRS", or "IRFT".
A character specifying the time unit used to calculate rates, e.g. person-years.
A numeric specifying the number of characters to print from label for the grouping variable.
A logical indicating whether results of individual studies should be printed by grouping variable.
A logical indicating whether result of the Cochran-Mantel-Haenszel test for overall effect should be printed.
Minimal number of significant digits, see
print.default.
Minimal number of significant digits for standard
deviations and standard errors, see print.default.
Minimal number of significant digits for z- or
t-value, see print.default.
Minimal number of significant digits for p-value
of overall treatment effect, see print.default.
Minimal number of significant digits for
p-value of heterogeneity test, see print.default.
Minimal number of significant digits for
heterogeneity statistic Q, see print.default.
Minimal number of significant digits for
between-study variance, see print.default.
Minimal number of significant digits for H
statistic, see print.default.
Minimal number of significant digits for I-squared
and Rb statistic, see print.default.
Minimal number of significant digits for
proportions, see print.default.
Minimal number of significant digits for
weights, see print.default.
A logical specifying whether p-values should be printed in scientific notation, e.g., 1.2345e-01 instead of 0.12345.
A character used as thousands separator.
A logical specifying whether heterogeneity statistic I^2 should be printed.
A logical indicating whether the use of
summary.meta in connection with metacum or
metainf should result in a warning.
A logical indicating whether a warning should be printed if backtransformed proportions and rates are below 0 and backtransformed proportions are above 1.
A character with bracket symbol to print lower confidence interval: "[", "(", "{", "".
A character string with information on separator between lower and upper confidence interval.
A logical specifying whether heterogeneity statistic H should be printed.
A logical specifying whether heterogeneity statistic Rb should be printed.
Text printed to identify between-study variance tau^2.
Text printed to identify heterogeneity statistic I^2.
Text printed to identify heterogeneity statistic Rb.
In print.meta, additional arguments are passed
on to print.summary.meta called internally; otherwise, this
argument is ignored.
A list is returned by the function summary.meta with the
following elements:
Results for individual studies (a list with elements TE, seTE, lower, upper, z, p, level, df).
Results for fixed effect model (a list with elements TE, seTE, lower, upper, z, p, level, df).
Results for random effects model (a list with elements TE, seTE, lower, upper, z, p, level, df).
Number of studies combined in meta-analysis.
Heterogeneity statistic Q.
Square-root of between-study variance.
Standard error of square-root of between-study variance.
Scaling factor utilised internally to calculate common tau-squared across subgroups.
Heterogeneity statistic H (a list with elements TE, lower, upper).
Heterogeneity statistic I2 (a list with elements TE, lower, upper), see Higgins & Thompson (2002).
Heterogeneity statistic Rb (a list with elements TE, lower, upper), see Crippa et al. (2016).
Total number of studies.
Cochran-Mantel-Haenszel test statistic for overall effect.
A character string indicating underlying summary measure.
A character string with the pooling method.
Function call.
Label for confidence interval.
A logical indicating whether method by Hartung and Knapp was used.
A character string indicating which method is used to estimate the between-study variance tau-squared.
A logical indicating whether tau-squared is assumed to be the same across subgroups.
Result for fixed effect model within groups (a
list with elements TE, seTE, lower, upper, z, p, level, df,
harmonic.mean) - if byvar is not missing.
Result for random effects model within groups
(a list with elements TE, seTE, lower, upper, z, p, level, df,
harmonic.mean) - if byvar is not missing.
Number of studies combined within groups - if byvar
is not missing.
Heterogeneity statistic Q within groups - if byvar
is not missing.
Heterogeneity statistic Q between groups (based on
fixed effect model) - if byvar is not missing.
Heterogeneity statistic Q between groups (based on
random effects model) - if byvar is not missing.
Square-root of between-study variance within subgroups
- if byvar is not missing.
Scaling factor utilised internally to calculate common tau-squared across subgroups.
Heterogeneity statistic H within subgroups (a list with
elements TE, lower, upper) - if byvar is not missing.
Heterogeneity statistic I2 within subgroups (a list with
elements TE, lower, upper) - if byvar is not missing.
Heterogeneity statistic Rb within subgroups (a list with
elements TE, lower, upper) - if byvar is not missing.
Levels of grouping variable - if byvar is not
missing.
Title of meta-analysis / systematic review.
Comparison label.
Outcome label.
Original data (set) used to create meta object.
Information on subset of original data used in meta-analysis.
As defined above.
Version of R package meta used to create object.
Note, in R package meta, version 3.0-0 some arguments have
been removed from R functions 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.
Cooper H & Hedges LV (1994), The Handbook of Research Synthesis. Newbury Park, CA: Russell Sage Foundation.
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.
# NOT RUN {
data(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"))
# }
# NOT RUN {
# Use unicode characters to print tau^2 and I^2
#
print(summary(meta1), text.tau2="\u03c4\u00b2", text.I2="I\u00b2")
# }
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