Summary method for objects of class meta.
# 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, ...)
An object of class meta.
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 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 or risk differences, i.e. if argument
sm is equal to "PLOGIT", "PLN",
"PRAW", "PAS", "PFT", or "RD".
A numeric defining a scaling factor for printing of
single incidence rates or incidence rate differences, i.e. if
argument sm is equal to "IR", "IRLN",
"IRS", "IRFT", or "IRD".
A character specifying the time unit used to calculate rates, e.g. person-years.
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 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.
A logical indicating whether the use of
summary.meta in connection with metacum or
metainf should result in a warning.
Additional arguments (ignored).
An object of class summary.meta.
A logical indicating whether information on title of meta-analysis, comparison and outcome should be printed at the beginning of the printout.
A numeric specifying the number of characters to print from label for the grouping variable.
Minimal number of significant digits, 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.
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 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.
A logical indicating whether a warning should be printed if backtransformed proportions and rates are below 0 and backtransformed proportions are above 1.
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.
Statistic H for residual heterogeneity (a list with
elements TE, lower, upper) - if byvar is not missing.
Statistic I2 for residual heterogeneity (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.
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 Rusing 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 single proportions or
risk differences, e.g. pscale=1000 means that proportions
are expressed as events per 1000 observations. This is useful in
situations with (very) low event probabilities.
Argument irscale can be used to rescale single rates or rate
differences, e.g. irscale=1000 means that rates are
expressed as events per 1000 time units, e.g. person-years. This is
useful in situations with (very) low rates. Argument irunit
can be used to specify the time unit used in individual studies
(default: "person-years"). This information is printed in summaries
and forest plots if argument irscale is not equal to 1.
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)
m1 <- metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c,
data = Fleiss93cont, sm = "SMD",
studlab = paste(study, year))
summary(m1)
summary(update(m1, byvar = c(1, 2, 1, 1, 2), bylab = "group"))
forest(update(m1, byvar = c(1, 2, 1, 1, 2), bylab = "group"))
# }
# NOT RUN {
# Use unicode characters to print tau^2 and I^2
print(summary(m1), text.tau2 = "\u03c4\u00b2", text.I2 = "I\u00b2")
# }
# NOT RUN {
# }
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