More flexible printing of treatment labels in forest plots provided
in arguments ‘label.e’ and ‘label.c’
User-visible changes
forest.meta():
new arguments ‘just.label.e’ and ‘just.label.c’ to define the
justification of treatment labels
arguments ‘label.e.attach’ and ‘label.c.attach’ can be vectors to
define columns to print treatment labels
Bug fixes
forest.metacum() / forest.metainf():
use ‘method.tau = “DL”’ in internal calls to metagen() to get rid of
error “Fisher scoring algorithm did not converge.” (results for
metacum() or metainf() are not changed by the use of the DL
estimator)
User-defined weights can be provided in meta-analysis
functions
Major revision of R function metaadd() to add meta-analysis
results to existing meta-analysis object
R function longarm() can be used with dose-response data
Bug fixes
forest.meta():
calculate and print correct totals if studies have been excluded
from the meta-analysis (issue #73)
region for clinically important difference (CID) can be restricted
to meta-analysis results
bug fix for p-values below 0.0001 in R object created with metacum()
or metainf()
metaprop():
use generalised linear mixed model if argument ‘method’ is missing
and argument ‘sm’ is equal to “PLO”, “PLOG”, or “PLOGI” (i.e., the
abbreviation of “PLOGIT”)
subgroup():
calculate correct totals in subgroups if studies have been excluded
from the meta-analysis
Do not print information on continuity correction for generalized
linear mixed model and argument ‘method.ci != “NAsm”’
new argument ‘method.common.ci’ to choose IVhet method
new arguments ‘weights’, ‘weights.common’, and ‘weights.random’ to
provide user-defined weights
metaadd():
argument ‘type’ can be equal to ‘tau2’ to provide estimate for the
between-study variance (argument ‘TE’) and its confidence interval
(arguments ‘lower’ and ‘upper’)
new argument ‘se’ to provide standard error
new argument ‘df’ to provide degrees of freedom for random effects
estimate or prediction interval
argument ‘method’ replaces arguments ‘method.common’,
‘method.random’, ‘method.tau’ and ‘method.predict’
new arguments ‘agent1’, ‘agent2’, ‘dose1’, ‘dose2’, and ‘sep.ag’ for
dose-response data
forest.meta(), settings.meta():
new argument ‘cid.pooled.only’ to restrict CID region to
meta-analysis results
meta, version 8.1-0
(2025-05-02)
Major changes
New functions cidprop() and plot.cidprop() to calculate and plot
expected proportions of comparable studies with clinically important
benefit or harm which are derived from the prediction interval
Rewrite of R function metacum() for cumulative meta-analysis with
dedicated print and forest functions
Rewrite of R function metainf() for leave-one-out meta-analysis
with dedicated print and forest functions
Remove R code for cumulative or leave-one-out meta-analysis from
R function forest.meta()
In forest plots, information for additional columns can be
printed in the lines with pooled effects using the new argument
‘data.pooled’
R packages **ggplot2*, tibble, and
scales added to Imports
R packages gridExtra and ggpubr
added to Suggests
Bug fixes
forest.meta():
consider setting for list element ‘null.effect’ for metamean(),
metaprop() and metarate() objects to fix (issue #67)
remove duplicated columns from forest plots with RevMan5 layout and
risk of bias information
use correct column labels for log transformed treatment estimates
and standard errors (list elements TE and seTE) if argument ’backtransf
= TRUE
update.meta():
consider setting for list element ‘rho’ for metabin(), metacor(),
metainc(), metamean(), metaprop(), metarate()
fix bug for penalised logistic regression of single study
metaprop():
use p2logit() to calculate the transformed null effect
metaprop(), metarate():
for random intercept logistic regression model consider input for
argument ‘null.effect’ instead of using 0
User-visible changes
New function [.pairwise
In help pages, use requireNamespace() for R commands using R
packages listed under Suggests
meta, version 8.0-1
(2024-10-31)
Revise web links
meta, version 8.0-0
(2024-10-30)
Major changes
By default, prediction intervals are based on k - 1
instead of k - 2 degrees of freedom (Veroniki et al., 2019, RSM)
where k corresponds to the number of studies in the
meta-analysis; see help(“meta-package”) for more details on
methods to calculate prediction intervals
I2 statistic can be calculated from between-study
variance instead of Q statistic (new argument
‘method.I2’)
R functions pairwise() and subset.pairwise() moved from R package
netmeta to meta
R function pairwise() can be used with dose-response
data
Information on colour of labels on left and right side of null
effect in forest plots can be stored in meta-analysis object
In forest plots,
the heterogeneity statistic Q, its p-value, and the
I2 statistic are printed with the same number of digits as in
printouts
the text for subgroups is printed in “black” instead of “darkgray”
(argument ‘col.subgroup’)
Level of confidence intervals for heterogeneity statistics can be
specified by the user (argument ‘level.hetstat’); in previous version of
R package meta confidence intervals for tau2
and tau were always 95%-CIs while confidence intervals for
I2 and H were based on the value for argument
‘level.ma’
First argument to R functions metabin(), metacont(), metagen(),
and metainc() can be a pairwise() object
In funnel.meta(), arguments ‘pch’, ‘cex’, and ‘cex.studlab’ can
be of same length as the number of studies
New auxiliary function setvals() to easily define the input for
arguments ‘pch’, ‘cex’, ‘col’, ‘bg’, ‘text’, and ‘cex.studlab’ in
funnel.meta(); e.g., to use different colours for subgroups
new arguments ‘agent’ and ‘dose’ to provide information for
dose-response data
new argument ‘varnames’ to change variable names for effect estimate
and its standard error
bubble.metareg():
argument ‘backtransf = TRUE’ is recognized for additional summary
measures, i.e., “PLOGIT”, “PLN”, “PAS”, “IRLN”, “IRS”, “ZCOR”
settings.meta():
default setting can be defined for ‘print.I2.ci’
additional settings, e.g., for R package netmeta,
can be changed
Bug fixes
forest.meta():
print “logVR” instead of “logVE” or “VE” for not back-transformed
vaccine efficacy / effectiveness (sm = “VE”)
additional check whether input for argument ‘sortvar’ is a function
(for example, using a variable ‘order’ resulted in an error due to the R
function order())
bubble.metareg():
use of vector instead of single value for argument ‘pch’ resulted in
wrong order of plotting symbols
metagen():
set list elements ‘approx.TE’ or ‘approx.seTE’ to NULL if no
approximation has been used
print.summary.meta():
recognize the arguments ‘scientific.pval’, ‘zero.pval’,
‘JAMA.pval’
runGLMM():
use results of common effect model as fallback for error “Cannot fit
ML model” and print corresponding warning
pairwise():
inconsistent values for Cohen’s d if data was already provided in
contrast based format
argument ‘append = FALSE’ didn’t work
Internal changes
metamean(): use mean value if median is not provided to approximate
missing standard deviation
R package metadat added to Depends (to access
meta-analysis datasets)
R package robvis added to Suggests (for risk of
bias assessment)
New functions rob(), barplot.rob() and traffic_light() for risk
of bias assessment (RoB)
New function read.cdir() to import Cochrane data package from
Cochrane review of interventions
New function blup.meta() to calculate best linear unbiased
predictors (BLUPs)
New functions estimates(), estimates.meta(), and
estimates.blup.meta() to extract meta-analysis results
Lower and upper confidence interval limits of individual study
results stored as transformed limits for meta-analysis with single
proportions or rates (in previous versions of meta, CI
limits were back transformed for individual studies and not back
transformed for meta-analysis results)
Changes for forest plots:
forest plot can be directly saved to a file using common graphics
device drivers (height of file is determined automatically)
BMJ layout implemented (layout = “BMJ”)
details on meta-analysis methods can be shown in plot
risk of bias assessment automatically added for meta-analyses with
RoB assessment
point estimates can be plotted as circles or diamonds instead of
squares
default settings for columns on left or right side of forest plot
can be defined in settings.meta()
common effect and random effects confidence intervals and prediction
intervals are truncated if lower / upper limit is outside the limits of
the x-axis
New general setting “BMJ”, i.e., R command
settings.meta(“BMJ”), to print results according to BMJ style
and formating checklist, see, for example, BMJ
Medicine
R function metabind() can return both common effect and random
effects results as well as prediction intervals
Internal functions for (back) transformations made visible /
accessible to the user; see help(“meta-transf”)
Within-cluster correlation can be specified for three-level model
(by default, rho = 0)
Use approximate formulae for Hedges’ g and Cohen’s d for RevMan 5
settings; see help page of settings.meta()
Argument ‘pscale’ or ‘irscale’ could be used in principle with
any effect measure which is useful for user-specified summary measure in
metagen()
New R function plot.meta() which calls forest.meta()
internally
Do not export the following R functions but rely on their generic
functions:
new argument ‘rho’ to specify within-cluster correlation in
three-level model
print.meta():
show group sample sizes in printouts
print confidence intervals based on t- and normal distribution for
metacont() or metamean() objects with a single study and argument
‘method.ci = “t”’
new argument ‘print.Q’ to suppress printing of heterogeneity
statistic Q and test of heterogeneity
default for argument ‘details.methods’ can be defined using
settings.meta()
do not print degrees of freedom for Hartung-Knapp or Kenward-Rogers
intervals if argument
gs():
first argument can be a character vector instead of a character
string to get the default setting of several arguments (which would be
returned as a list)
new argument ‘unname’ to return named arguments (if unname =
FALSE)
metamerge():
can be used with object created with copas() or limitmeta() from R
package metasens as only input (which adds the
respective results to the standard meta-analysis object)
arguments ‘label1’ and ‘label2’ can be used to provide defaults to
amend labels for common effect or random effects model, prediction
intervals and subgroups
new arguments ‘label1.common’, ‘label2.common’, ‘label1.random’,
‘label2.random’, ‘label1.predict’, ‘label2.predict’, ‘label1.subgroup’,
and ‘label2.subgroup’
metaadd():
argument ‘data’ can be a meta-analysis object created with R package
meta
new arguments ‘method.common’, ‘method.random’, ‘method.tau’,
‘method.random.ci’, and ‘method.predict’
metabind():
new arguments ‘common’, ‘random’ and ‘prediction’ replacing argument
‘pooled’
forest.meta():
new arguments ‘file’, ‘width’, ‘rows.gr’, ‘func.gr’, ‘args.gr’, and
‘dev.off’ to directly store a forest plot in a file
new arguments ‘rob’, ‘rob.col’, ‘rob.symbols’, ‘rob.attach’,
‘rob.xpos’, ‘rob.legend’, ‘fs.rob’, ‘fs.rob.symbols’, ‘ff.rob’,
‘ff.rob.symbols’, ‘colgap.rob’ and ‘just.rob’ for risk of bias
assessment
new arguments ‘details’, ‘fs.details’ and ‘ff.details’ to add
details on meta-analytical methods
point estimates can be plotted as circles instead of squares or
diamonds (arguments ‘type.study’, ‘type.common’, ‘type.random’,
‘type.subgroup’, ‘type.subgroup.common’, ‘type.subgroup.random’)
new arguments ‘col.circle’ and ‘col.circle.lines’ to define the
colour of circles
new argument ‘digits.n’ and ‘digits.event’ to specify the number of
significant digits for sample sizes and number of events
justification of results for effect + confidence interval can be
specified by argument ‘just’ if argument ‘layout = “RevMan5”’
settings.meta():
new argument ‘overall.hetstat’ to specify whether to show
information on between-study heterogeneity
new argument ‘width’ to specify width of graphics device
new arguments ‘print.tau2’, ‘print.tau2.ci’, ‘print.tau’ and
‘print.tau.ci’ to specify whether to show (confidence intervals for)
tau^2 or tau in printouts
new arguments ‘leftcols’, ‘rightcols’, ‘leftlabs’, ‘rightlabs’,
‘label.e.attach’ and ‘label.c.attach’ to changes defaults for
corresponding arguments in forest.meta()
bubble.meta():
new arguments ‘pscale’ and ‘irscale’ added
metaprop(), metarate():
list elements ‘lower’ and ‘upper’ contain the transformed lower and
upper confidence interval limits for individual studies
automatically calculated limits on x-axis were to narrow for some
settings
metamean():
argument ‘null.effect’ was ignored to calculate the test statistic
and p-value for individual studies (list elements ‘statistic’ and
‘pval’)
metabin():
use continuity correction if sm = “VE”
metareg():
error if input to argument ‘formula’ was the name of an R
function
print.summary.meta():
print correct backtransformed subgroup results for metabind
objects with metaprop objects with Freeman-Tukey transformation as
input
Internal changes
New internal function gh() to determine height of graphics
file
New internal function smlab() to determine the label for the
summary measure
Use of vcalc() from R package metafor to
calculate the variance-covariance matrix in three-level model with
within-cluster correlation not equal to 0
funnel.meta(): list element of meta-analysis object can be
directly specified in arguments ‘text’, ‘col’ and ‘bg’, e.g., argument
‘text = studlab’ to use study labels instead of plotting
symbols
Input to chkchar() can be a numeric vector
settings.meta(): set defaults for arguments ‘forest.tau2,’
‘forest.tau’, ‘forest.I2’, ‘forest.Q’, ‘forest.pval.Q’, and ‘forest.Rb’
for BMJ, JAMA and RevMan5 layout
meta, version 6.5-0
(2023-06-06)
Major changes
In R function metamerge(), user can decide whether to keep or
ignore information from second meta-analysis on study weights and
heterogeneity statistics
New function metaadd() to add pooled results from external
analysis to meta-analysis object
Function update.meta() considers arguments ‘method.mean’ and
‘method.sd’
Variables with group specific information can be merged into a
single variable in longarm()
Additional thresholds can be specified to plot vertical lines in
forest plots, e.g., to mark large, moderate and small effects
Baujat plot can be used to evaluate influence of studies on
random effects estimate
Seed can be specified in meta-analysis functions to calculate
reproducible bootstrap prediction intervals
User-visible changes
metamerge():
new arguments ‘common1’, ‘random1’, ‘prediction1’, ‘common2’,
‘random2’, ‘prediction2’ to specify whether to keep common effect
results, random effects results or prediction interval from first or
second meta-analysis
new arguments ‘keep’, ‘keep.Q’, ‘keep.I2’ and ‘keep.w’ to determine
whether additional information from second meta-analysis should be
kept
new arguments ‘common’, ‘random’, ‘prediction’, ‘overall’ and
‘overall.hetstat’ to specify which results to print
new arguments ‘hetlabel1’, ‘hetlabel2’, ‘text.common1’,
‘text.common2’, ‘text.random1’, ‘text.random2’, ‘text.predict1’ and
‘text.predict2’ to label results from first or second meta-analysis
longarm():
new arguments ‘id1’ and ‘id2’ to specify last character(s) of
variable names with group specific information
Bug fixes
metabias():
do not conduct test for funnel plot asymmetry for three-level model
(the test did not consider the cluster structure)
forest.meta():
header line was concealed by equivalence region
error if argument ‘resid.hetstat = TRUE’ was used for subgroup
meta-analysis without common between-study variance estimate in
subgroups (argument ‘tau.common = FALSE’ in meta-analysis
functions)
read.rm5():
fix bug for error message “In gsub(”“,”EUR”, txt) : unable to
translate ‘<80>’ to a wide string” due to change in default
settings in R function gsub()
Print header line in forest plots with JAMA or RevMan5
layout
Bug fixes
forest.meta():
no error for subgroup meta-analysis conducted with metarate() using
argument ‘n’ to specify the sample size
same square sizes for a meta-analysis with or without subgroups
no error for Revman5 and JAMA layout in meta-analyses with more than
one random effects method
metarate():
calculate number of observations in subgroups if argument ‘n’ is
provided
metabind(), forest.metabind():
use correct study / method labels if no subgroup results are
present
settings.meta():
argument ‘addrows.below.overall’ not ‘addrow.below.overall’
metagen():
calculate correct prediction interval limits for three-level
models
number of studies in meta-analysis equal to number of non-missing
estimates and standard errors (only number of non-missing standard
errors was considered)
User-visible changes
All meta-analysis functions:
print prediction interval(s) if argument ‘method.predict’ is not
missing
metabin, metagen(), metainc():
argument ‘sm = “VE”’ can be used for meta-analysis of vaccine
efficacy or vaccine effectiveness
metagen(), settings.meta():
new argument ‘transf’
forest.meta():
new argument ‘header.line’ to add header line
new argument ‘digits.TE’ to specify number of digits for transformed
treatment estimates (list element ‘TE’)
use more informative column labels for ‘TE’ and ‘seTE’
metabin(), metainc(), metaprop() and metarate():
for GLMMs, stop with error if argument ‘adhoc.hakn.ci’ or
‘adhoc.hakn.pi’ is unequal to “”
nnt.meta(), nnt.default():
new argument ‘small.values’ to specify whether small treatment
effects indicate a beneficial or harmful effect
settings.meta():
new argument ‘digits.TE.forest’ to set default for argument
‘digits.TE’ in forest.meta()
Print blank space before negative upper confidence interval limit
if separator is equal to “-”
New help page meta-sm summarising available summary
measures
Help page of nnt() updated
Change maintainer’s email address
Internal changes
New internal functions transf(), cor2z(), p2asin(), logVR2VE()
and VE2logVR()
chknumeric():
new argument ‘integer’ to check for integer values
List element ‘df.Q.b.random’ with degrees of freedom for test of
subgroup differences under random effects model is a list instead of a
vector if more than one random effects method was used (argument
‘method.random.ci’)
Several changes for meta-analysis using generalised linear mixed
or three-level models
meta, version 6.0-0
(2022-09-17)
Major changes
Meta-analysis object can contain results of several common effect
or random effects methods, e.g., random effects meta-analysis with or
without Hartung-Knapp method
Kenward-Roger method implemented to estimate confidence or
prediction interval (Partlett
& Riley, 2017)
Rewrite of function metamerge() to merge pooled results of two
meta-analyses into a single meta-analysis object
Defaults for appearance of forest plots can be defined for the R
session
R package pimeta
added to suggested packages in order to calculate bootstrap approach for
prediction interval
New argument ‘method.random.ci’ replaces argument ‘hakn’ to
select method to calculate confidence interval for random effects
estimate
Major update of help pages:
help page for meta-package revised; content with details in
meta-analysis functions moved to this help page
new help page meta-object describing content of
meta-analysis functions; corresponding content moved from individual
help pages
Bug fixes
forest.meta():
do not print label for subgroups with no information to print, e.g.,
if argument ‘study.results = FALSE’, for subgroups with only one or no
study contributing to pooled estimate in the subgroup
do not show empty row before label on x-axis (argument ‘xlab’) if
argument ‘label.left’ or ‘label.right’ is provided for meta-analysis
without reference value (argument ‘ref’), e.g., meta-analysis of single
means or proportions
metareg():
use Paule-Mandel estimator if used in meta-analysis (instead of REML
estimator)
User-visible changes
metabin(), metacont(), metacor(), metagen(), metainc(), metamean(),
metaprop() and metarate():
argument ‘hakn’ replaced by ‘method.random.ci’
argument ‘adhoc.hakn’ replaced by ‘adhoc.hakn.ci’
new arguments ‘method.predict’ and ‘adhoc.hakn.pi’
settings.meta():
several new arguments added to define defaults for forest plots; see
printout of command settings.meta(print = TRUE)
forest.meta():
argument ‘col.by’ has been replaced by ‘col.subgroup’,
argument ‘bysort’ has been replaced by ‘sort.subgroup’
Use term ‘common effect model’ instead of ‘fixed effect model’ in
the documentation and argument ‘common’ instead of ‘fixed’ to (not) show
results for common effect model
Three-level model implemented in all meta-analysis
functions
For continuity corrections, new argument ‘method.incr’ replaces
arguments ‘allincr’ and ‘addincr’ for meta-analysis with binary outcome
or incidence rates
Exact Poisson confidence limits can be calculated for individual
studies in meta-analysis of single rates
Show information on statistical significance and between-study
heterogeneity in forest plots of cumulative or leave-one-out
meta-analysis
Calculate Cochran’s Q directly in meta for
classic inverse variance meta-analysis (instead of taking it from
metafor package)
By default, do not print warnings for deprecated arguments; this
can be changed with command ‘settings.meta(warn.deprecated =
TRUE)’
Bug fixes
Use correct standard error for Cox and Snell’s method in smd2or()
and or2smd()
Three-level model did not work if variable from dataset was
provided as input to argument ‘id’ in metacont()
Argument ‘tau.common = TRUE’ was ignored in subgroup analysis of
three-level model in metacont()
Argument ‘level’ was ignored in the calculation of confidence
limits for individual studies in metacont() and metamean() if argument
‘method.ci = “t”’
Show correct studies in forest plot with subgroups and missing
treatment effects if argument ‘allstudies = FALSE’
Show points in bubble plot of meta-regression with GLMM
User-visible changes
For three-level models,
argument ‘id’ has been replaced by ‘cluster’
cluster variable is shown in forest plots
New arguments ‘common’ and ‘cluster’ in functions metabin(),
metacont(), metacor(), metagen(), metainc(), metamean(), metaprop() and
metarate()
New function subset.longarm() to select subset of a longarm
object
New argument ‘method.ci’ in function metarate()
New argument ‘method.ci.rate’ in function
settings.meta()
New argument ‘method.incr’ in functions metabin(), metainc(),
metaprop() and metarate()
print.summary.meta():
for a single study and metabin() with method = “MH”, sm = “RR” and
RR.Cochrane = FALSE, print results using a continuity correction for
sample sizes of 1x incr (individual study) and 2x incr (meta-analysis of
single study)
Internal changes
forest.meta():
use meta:::formatN() instead of format() for formatting
print study label “1” instead of “” for a single study
metarate():
list elements ‘lower’ and ‘upper’ contain untransformed confidence
limits for individual studies
New internal function update_needed() to check whether update of
meta object is needed
metabin(), metacont(), metacor(), metagen(), metainc(),
metamean(), metaprop() and metarate():
new list element ‘k.TE’ with number of estimable effects
meta, version 5.2-0
(2022-02-04)
Major changes
Forest plot for meta-analysis with subgroups:
more flexible printing of subgroup results
by default, do not show subgroup results (pooled estimates and
information on heterogeneity) for subgroups consisting of a single
study
Prediction intervals in subgroups can be shown independently of
prediction interval for overall meta-analysis in printouts and forest
plots
Bubble plot shows relative treatment effects on original scale
instead of log scale and reference line is shown
Trim and fill, limit meta-analysis and Copas selection model
objects can be used in function metabind()
New function longarm() to transform data from pairwise
comparisons to long arm-based format
New auxiliary function labels.meta() to create study labels for
forest plots in JAMA or Lancet layout
Printing of spaces in confidence intervals can be
suppressed
Help page of forest.meta() updated
Bug fixes
Use correct standard error to calculate prediction interval if
Hartung-Knapp method was used
In forest plots, show correct degrees of freedom for test of
effect in subgroups for Hartung-Knapp method
In update.meta(), consider input for arguments ‘pscale’,
‘irscale’ and ‘irunit’ for meta-analysis objects created with
metagen()
User-visible changes
forest.meta():
new argument ‘subgroup.hetstat’
arguments ‘subgroup’, ‘subgroup.hetstat’, ‘prediction.subgroup’,
‘test.effect.subgroup’, ‘test.effect.subgroup.fixed’ and
‘test.effect.subgroup.random’ can be a logical vector of same length as
number of subgroups
arguments ‘lab.e’, ‘lab.c’, ‘lab.e.attach.to.col’ and
‘lab.c.attach.to.col’ replaced by ‘label.e’, ‘label.c’, ‘label.e.attach’
and ‘label.c.attach’
first argument can be of class ‘limitmeta’ or ‘copas’
bubble.metareg():
new argument ‘backtransf’ to (not) back transform relative treatment
effects on y-axis
new arguments ‘ref’, ‘col.ref’, ‘lty.ref’ and ‘lwd.ref’ for
reference line
settings.meta():
arguments ‘print’, ‘reset’ and ‘setting’ can be used like any other
setting; for example, it is possible to fully reset the settings and
switch to the RevMan 5 settings
R commands ‘settings.meta(“print”)’ and ‘settings.meta()’ produce
the same printout
new global setting ‘prediction.subgroup’ for prediction intervals in
subgroups
new global settings ‘CIlower.blank’ and ‘CIupper.blank’
cilayout():
new arguments ‘lower.blank’ and ‘upper.blank’ to suppress printing
of spaces in confidence intervals
additional checks for length of arguments
Internal changes
metagen():
new list elements ‘seTE.hakn’ and ‘seTE.hakn.adhoc’ (with standard
error for Hartung-Knapp method) and ‘seTE.classic’ for classic random
effects inverse variance method
forest.meta():
new code to assign missing column labels
Internal function formatCI() considers values for ‘lower.blank’
and ‘upper.blank’ in cilayout()
New internal function catch() to catch value for an
argument
meta, version 5.1-1
(2021-12-02)
Major changes
For meta-analysis of single proportions,
export p-value of exact binomial test for individual studies if
Clopper-Pearson method (method.ci = “CP”) is used to calculate
confidence intervals for individual studies
do not export p-value for individual studies if argument ‘method.ci’
is not equal to “CP” or “NAsm” (normal approximation based on summary
measure)
Bug fixes
Meta-analysis of continuous outcomes using Hedges’ g or Cohen’s d
as summary measure resulted in inestimable SMDs in
individual studies if the total sample size was larger than 343 and
argument ‘exact.smd’ was TRUE (default)
Forest plot creation for meta-analysis of single means with
subgroups resulted in an error
Internal changes
New internal function ciClopperPearson() to calculate confidence
limits and p-value for exact binomial method
Exported list elements changed for internal functions
ciAgrestiCoull(), ciSimpleAsymptotic() and ciWilsonScore()
meta, version 5.1-0
(2021-11-17)
Major changes
By default, use exact formulae in estimation of the standardised
mean difference (Hedges’ g, Cohen’s d) and its standard error (White & Thomas,
2005)
Bug fixes
Use of metagen() with argument ‘id’ (three-level model) does not
result in an error if all estimates come from a single study
Internal changes
Fix errors due to extended checks of arguments equal to NULL in R
package metafor, version 3.1 or above
meta, version 5.0-1
(2021-10-20)
Major changes
For backward compatibility, use Q statistic based on Mantel-Haenszel
estimate (argument ‘Q.Cochrane’) by default to calculate
DerSimonian-Laird estimator of the between-study variance
Bug fixes
For small sample sizes, use correct entry from Table 2 in Wan et. (2014) to
approximate standard deviation from median and related statistics
meta, version 5.0-0
(2021-10-11)
Major changes
Behaviour of print.meta() and print.summary.meta() switched (to
be in line with other print and summary functions in R)
New default settings:
Restricted maximum likelihood (REML) instead of DerSimonian-Laird
estimator used as default to estimate between-study heterogeneity
(argument ‘method.tau’)
Do not use Q statistic based on Mantel-Haenszel estimate to
calculate DerSimonian-Laird estimator of the between-study variance
(argument ‘Q.Cochrane’)
Print ‘Common effect model’ instead of ‘Fixed effect model’
Default settings of meta, version 4 or lower,
can be used with command settings.meta(“meta4”) - this does not
change the new behaviour of print.meta() and
print.summary.meta()
Renamed arguments:
‘fixed’ instead of ‘comb.fixed’
‘random’ instead of ‘comb.random’
‘level.ma’ instead of ‘level.comb’
‘subgroup’ instead of ‘byvar’
‘subgroup.name’ instead of ‘bylab’
‘print.subgroup.name’ instead of ‘print.byvar’
‘sep.subgroup’ instead of ‘byseparator’
‘nchar.subgroup’ instead of ‘bylab.nchar’
Internal changes
Function gs() can be used to access internal settings
Store internal auxiliary functions in files meta-aux.R to
meta-xlab.R
meta, version 4.19-2
(2021-09-29)
Bug fixes
Forest plots of meta-analyses assuming a common between-study
heterogeneity variance in subgroups resulted in an error (bug was
introduced in meta, version 4.16-0)
For GLMMs, export Wald-type Q statistic for residual
heterogeneity instead of missing value
meta, version 4.19-1
(2021-09-14)
Bug fixes
metagen():
set random effects weights equal to zero for estimates with standard
errors equal to NA (to fix error bubble.metareg)
metareg():
for three-level model, use ‘test = “t”’ instead of ‘test = “knha”’
in internal call of rma.mv()
User-visible changes
summary.meta():
print tau2 and tau for subgroups with single study if argument
‘tau.common = TRUE’
bubble.metareg():
show regression lines for a single categorical covariate
meta, version 4.19-0
(2021-08-05)
Major changes
Subgroup analysis for three-level model fully
implemented
New default for forest plots to show results of test for subgroup
differences in meta-analyses with subgroups
Calculation of weights for three-level random effects model using
weights.rma.mv() with argument ‘type = “rowsum”’ from R package
metafor
Print study label provided by argument ‘studlab’ for
meta-analysis with a single study
Total number of observations and events printed in summaries (if
available)
Bug fixes
metagen():
treatment estimates for three-level models with subgroups were not
based on common between-study variance despite argument ‘tau.common =
TRUE’
metareg():
use rma.mv() from R package metafor for three-level
models instead of rma.uni()
new argument ‘test.subgroup’ to print results of test for subgroup
differences
print.meta():
for three-level models, column with grouping information added to
study details
metagen():
default for estimation of between-study variance has changed for
three-level models with subgroups, i.e., tau2 is allowed to be different
in subgroups by default
Internal changes
metagen():
new variable ‘.idx’ with running index in meta-analysis dataset
(list element ‘data’)
new logical list element ‘three.level’ indicating whether
three-level model was used
meta, version 4.18-2
(2021-06-11)
Bug fixes
For argument ‘adhoc.hakn = “ci”’, directly compare width of
confidence intervals of Hartung-Knapp method and classic random effects
meta-analysis
meta, version 4.18-1
(2021-05-11)
Major changes
Calculate correct upper limit for confidence intervals of I2 and H2
in very homogeneous meta-analyses (i.e., if Q < k - 1)
Bug fixes
forest.meta():
correct order of p-values for homogeneity tests within subgroups if
argument ‘bysort = TRUE’
calcH():
set H = 1 in calculation of confidence interval for H if H < 1
(i.e., if Q < k - 1)
metabias():
bug fix for linear regression tests using metafor,
version 2.5-86
metabind():
bug fix for a single meta-analysis object
Internal changes
metabias.bias():
argument ‘…’ passed on to rma.uni()
metagen():
set list element ‘df.hakn’ to NA instead of NULL if condition met
for argument ‘adhoc.hakn = “ci”’
meta, version 4.18-0
(2021-03-05)
Major changes
Prediction intervals for subgroups implemented
Bug fixes
metacont():
use correct variance formula for Glass’ delta
metainc():
update command resulted in an error Arguments ‘event.e’ and
‘n.e’ must have the same length for meta-analysis with subgroups
(due to list elements ‘n.e.w’ and ‘n.c.w’ which were interpreted as
‘n.e’ and ‘n.c’ containing missing values instead of being NULL)
print.meta():
use of argument ‘details = TRUE’ resulted in an error in
meta-analyses with duplicated study labels
Consider argument ‘adhoc.hakn’ to calculate confidence intervals in
random effects subgroup meta-analyses
User-visible changes
print.meta():
column with information on subgroups added to details if argument
‘details = TRUE’
forest.meta():
new argument ‘text.predict.w’ to label the prediction interval in
subgroups
arguments ‘text.fixed.w’ and ‘text.random.w’ checked for correct
length
Ad hoc variance correction for Hartung-Knapp method not
available for GLMMs
Internal changes
metacont():
get rid of warnings ‘Unknown or uninitialised column’ if argument
‘subset’ is used
use regtest() from R package metafor internally for
linear regression tests
new print layout providing more details
New dataset Pagliaro1992 for meta-analysis on prevention of first
bleeding in cirrhosis (Pagliaro et al.,
1992)
Bug fixes
update.meta():
do not switch to three-level model if method.tau = “ML”
User-visible changes
metabias():
use name of first author to select test for funnel plot asymmetry
instead of “rank”, “linreg”, “mm”, “count”, and “score” (can be
abbreviated; old names are still recognised)
print.metabias():
new arguments ‘digits.stat’, ‘digits.se’, ‘digits.pval’,
‘scientific.pval’, ‘big.mark’, ‘zero.pval’, ‘JAMA.pval’
Internal changes
linregcore():
complete rewrite using rma.uni() and regtest() from R package
metafor
meta, version 4.16-2
(2021-01-27)
Bug fixes
drapery():
use correct limits on y-axis for argument ‘type = “zvalue”’
User-visible changes
funnel.meta():
inverse of square root of sample size can be plotted on y-axis
(argument ‘yaxis = “invsqrtsize”’)
forest.meta():
consider input for argument ‘hetstat’ to print heterogeneity
statistics for overall results (see argument ‘overall.hetstat’)
studies with missing values for subgroup variable (argument ‘byvar’)
can be excluded from meta-analysis using argument ‘subset’
Internal changes
funnel.meta():
try to derive sample sizes from list elements ‘n.e’ or ‘n.c’ if
argument ‘yaxis = “size”’
meta, version 4.16-1
(2021-01-19)
Bug fixes
For argument ‘adhoc.hakn = “ci”’, use correct query to determine
whether confidence interval of Hartung-Knapp method is smaller than
classic random effects meta-analysis (Hybrid method 2 in Jackson et
al., 2017)
meta, version 4.16-0
(2021-01-18)
Major changes
Three-level meta-analysis models can be fitted for generic and
continuous outcomes (Van den Noortgate et.,
2013) by calling rma.mv() from R package metafor
internally
Measures I2 and H for residual heterogeneity are based on Q
statistic for residual heterogeneity (instead of taken directly from
metafor package)
Additional ad hoc method implemented if confidence
interval of Hartung-Knapp method is smaller than classic random effects
meta-analysis (Hybrid method
2 in Jackson et al., 2017)
For funnel plot of a diagnostic test accuracy meta-analysis, use
effective sample size (Deeks et.,
2005) by default on the y-axis
New function metamerge() to merge pooled results of two
meta-analyses into a single meta-analysis object
Bug fixes
metabin():
Mantel-Haenszel method of risk differences did not use continuity
correction in case of studies with a zero cell count (argument ‘MH.exact
= FALSE’)
metabin(), metainc(), metaprop(), metarate():
for GLMMs, confidence limits for classic random effects
meta-analysis were calculated instead of confidence limits for
Hartung-Knapp if argument ‘hakn = TRUE’
metabin(), metainc(), metaprop(), metarate():
works for GLMMs with zero events or number of events equal to number
of patients in all studies
forest.meta():
print results for test of subgroup effect in correct order if
argument ‘bysort = TRUE’
read.rm5():
list elements ‘method’ and ‘sm’ had been encoded as a factor instead
of character under R-versions below 4.0 which resulted in an error using
metacr()
User-visible changes
Do not print empty confidence intervals for heterogeneity
statistics
metacont(), metagen(), update.meta():
new argument ‘id’ to specify which estimates belong to the same
study (or laboratory) in order to use three-level model
metabind():
argument ‘…’ can be a single list of meta-analysis objects
meta-analyses can use different methods, e.g., different estimators
of the between-study variance
All meta-analysis functions:
argument ‘adhoc.hakn = “iqwig6”’ instead of ‘adhoc.hakn = “ci”’ uses
the ad hoc method for Hartung-Knapp method described in General
Methods 6.0 (IQWiG, 2020)
argument ‘adhoc.hakn = “ci”’ uses the ad hoc method
described in Jackson et al. (2017)
forest.meta():
column heading “Mean” instead of “MLN” for meta-analysis object
created with metamean() with arguments ‘sm = “MLN”’ and ‘backtransf =
TRUE’
study labels specified by argument ‘studlab’ tried to catch from
meta-analysis object
do not print statistic for residual heterogeneity if argument
‘tau.common = FALSE’ was used to conduct subgroup meta-analysis
metainc():
square root transformed incidence rate difference added as new
summary measure (sm = “IRSD”)
New arguments ‘text.fixed’, ‘text.random’, ‘text.predict’,
‘text.w.fixed’ and ‘text.w,random’ in meta-analysis functions
settings.meta():
new general setting “geneexpr” to print scientific p-values and not
calculate confidence interval for between-study heterogeneity variance
tau2
argument ‘method.tau.ci’ can be specified as a global setting
text for fixed effect and random effects model as well as prediction
interval can be specified (arguments ‘text.fixed’, ‘text.random’,
‘text.predict’, ‘text.w.fixed’, ‘text.w.randon’)
print.meta(), print.summary.meta():
do not print information on continuity correction for exact
Mantel-Haenszel method with single study
metareg() can be used in loops to provide argument
‘formula’
New auxiliary function JAMAlabels() to create study labels in
JAMA layout
Internal changes
Calculate measures of residual heterogeneity in hetcalc()
meta, version 4.15-1
(2020-09-30)
Bug fixes
metacr():
set summary measure to “OR” for Peto odds ratio
meta, version 4.15-0
(2020-09-29)
Major changes
Deeks’ linear regression test for funnel plot asymmetry of funnel
plots of diagnostic test accuracy studies implemented (Deeks et.,
2005)
Effective sample size (Deeks et.,
2005) can be used on y-axis of funnel plot
Discard infinite estimates and standard errors from calculation
of heterogeneity measures
Diagnostic odds ratio (sm = “DOR”) added as new effect measure in
metabin() and metagen()
User-visible changes
forest.meta(), forest.metabind():
arguments ‘digits.zval’ and ‘print.zval’ replaced by ‘digits.stat’
and ‘print.stat’
print.summary.meta(), settings.meta():
argument ‘digits.zval’ replaced by ‘digits.stat’
metacr():
do not print a warning for inverse variance meta-analysis with
binary outcome
Help page for tests of funnel plot asymmetry updated
Help pages for metabin() and metainc() updated
meta, version 4.14-0
(2020-09-09)
Major changes
Median and related statistics can be used in meta-analysis with
continuous outcomes to approximate means and standard deviations (Wan et., 2014; Luo et al., 2018; Shi et al., 2020)
RevMan 5 analysis datasets can be imported directly using the
RM5-file
R package xml2 added to Imports (RM5-files are
in XML-format)
Confidence intervals for individual studies can be based on
quantile of t-distribution (only implemented for mean differences and
raw untransformed means at the moment)
For the generic inverse variance method,
methods by Luo et
al. (2018) implemented to estimate mean from sample size, median and
other statistics
method by Shi et
al. (2020) implemented to estimate the standard deviation from
sample size, median, interquartile range and range
Bug fixes
forest.meta():
show all studies with estimable treatment effects if argument
‘allstudies = FALSE’
metabind():
works with meta-analysis objects created with metacor()
calculate correct p-value for heterogeneity test if input are
subgroup analyses of the same dataset
calculate correct p-value for within-subgroup heterogeneity test if
input are subgroup analyses of the same dataset
metacum():
works with Hartung-Knapp method
metagen():
list element ‘seTE’ contained standard deviation instead of standard
error for method by Wan et. (2014) to
estimate mean and its standard error from median and other
statistics
User-visible changes
read.rm5():
direct import of RM5-file possible
new argument ‘debug’ for debug messages while importing RM5-files
directly
metacr():
overall results not shown if this was specified in the Cochrane
review (only applies to imported RM5-files)
metagen(), metacont(), metamean():
new argument ‘method.mean’ to choose method to estimate mean from
sample size, median and other statistics
new argument ‘method.sd’ to choose method to estimate standard
deviation from sample size, median, interquartile range and range
new argument ‘method.ci’ to choose method for confidence intervals
of individual studies (only applies to mean differences and raw
untransformed means at the moment)
metacont():
new arguments to estimate mean and standard deviation from median
and related statistics: ‘median.e’, ‘q1.e’, ‘q3.e’, ‘min.e’, ‘max.e’,
‘median.c’, ‘q1.c’, ‘q3.c’, ‘min.c’, ‘max.c’, ‘method.mean’,
‘method.sd’, ‘approx.mean.e’, ‘approx.mean.c’, ‘approx.sd.e’,
‘approx.sd.c’
metamean():
new arguments to estimate mean and standard deviation from median
and related statistics: ‘median’, ‘q1’, ‘q3’, ‘min’, ‘max’,
‘method.mean’, ‘method.sd’, ‘approx.mean’, ‘approx.sd’
forest():
by default, show number of participants in forest plot if this
information is available for meta-analysis objects created with
metagen()
automatically format p-values for individual studies if added to
forest plot using argument ‘leftcols’ or ‘rightcols’
Datasets renamed from Fleiss93, Fleiss93cont and Olkin95 to
Fleiss1993bin, Fleiss1993cont and Olkin1995
More sensible variable names in datasets Fleiss1993bin,
Fleiss1993cont and Olkin1995
Internal changes
Previous R function read.rm5() for CSV-files renamed to
read.rm5.csv()
New auxiliary functions extract_outcomes(), oct2txt() and
read.rm5.rm5() to import RevMan 5 analysis datasets
ci():
list element ‘z’ renamed to ‘statistic’ as calculations can also be
based on the t-distribution; list element ‘z’ is still part of the
output for backward compatibility, however, could be removed in a future
update
metagen():
list elements ‘zval’, ‘zval.fixed’ and ‘zval.random’ renamed to
‘statistic’, ‘statistic.fixed’ and ‘statistic.random’; list elements
‘zval’, ‘zval.fixed’ and ‘zval.random’ are still part of the output for
backward compatibility, however, could be removed in a future
update
Internal functions TE.seTE.iqr.range(), TE.seTE.iqr() and
TE.seTE.range() renamed to mean.sd.iqr.range(), mean.sd.iqr() and
mean.sd.range()
mean.sd.iqr.range():
new arguments ‘method.mean’ and ‘method.sd’
mean.sd.iqr(), mean.sd.range():
new argument ‘method.mean’
chkchar(), chkcolor(), chklevel(), chknumeric():
argument ‘single’ replaced by ‘length’ (which can be used to test
for a specific vector length instead whether it is a single value)
(argument ‘single’ is still available for backward compatibility,
however, will be removed in a future update)
meta, version 4.13-0
(2020-07-02)
Major changes
Rely on generic functions from R package
metafor, e.g., to produce forest or funnel plots (since
R version 4.0.0 generic functions from an R package do not consider
corresponding functions from another R package which can result in
errors if R packages meta and metafor
are both loaded)
R function funnel.default() removed from meta
due to conflict with metafor
meta, version 4.12-0
(2020-05-04)
Major changes
Sample size method for meta-analysis of binary data with the odds
ratio as summary measure implemented (Bakbergenuly et al.,
2020)
Do not use predict.rma() from metafor package to
calculate prediction intervals for generalised linear mixed
models
User-visible changes
drapery():
study IDs or study labels can be printed at the top of the drapery
plot to identify individual studies
more flexible plots, e.g., colours can be specified for individual
studies based on p-value of treatment effect
possible value for argument ‘type’ renamed from “cvalue” to “zvalue”
as drapery plots show test statistics, not critical values
funnel.meta(), funnel.default():
argument ‘log’ is considered for relative summary measures, e.g.,
odds or risk ratio
metaprop():
can be used with non-integer number of events and sample sizes
metabias.meta(), metabias.default():
third component of list element ‘estimate’ renamed from “slope” to
“intercept” for linear regression tests
settings.meta():
new possible general settings: “iqwig5” and “iqwig6”,
respectively
Use Markdown for NEWS
meta, version 4.11-0
(2020-02-20)
Major changes
New arguments ‘overall’ and ‘overall.hetstat’ in meta-analysis
functions to control printing of overall meta-analysis results (useful
to only show subgroup results)
For GLMMs, use Wald-type Q statistic to calculate I2 of residual
heterogeneity in meta-analysis with subgroups (instead of
likelihood-ratio Q statistic)
Bug fixes
For GLMMs with subgroups, conduct the correct test for subgroup
differences (bug was introduced in meta, version
4.9-7)
summary.meta():
export the correct harmonic mean for fixed effect and random effects
model (part of list elements ‘fixed’ and ‘random’)
metabind():
do not produce an error if argument ‘warn’ or ‘prediction’ is not
unique in meta-analyses
User-visible changes
forest.meta():
possible to print results for test of an overall effect or subgroup
differences even if meta-analysis results are not shown
new defaults for arguments ‘overall’ and ‘overall.hetstat’ (which
are now considered from meta-analysis objects)
print.summary.meta():
for meta-analysis with subgroups, print information on Q and I^2
with fixed effect results and information on tau and tau^2 with random
effects results (previously, information on Q, I^2, tau, and tau^2 was
reported twice)
Internal changes
do not calculate confidence limits for tau2 and tau in intermediate
calculations of other quantities (i.e., use argument ‘method.tau.ci =
““’)
meta, version 4.10-0
(2020-01-29)
Major changes
New function drapery() to generate a drapery plot which is based on
p-value curves
Bug fixes
funnel.meta():
print contours in contour-enhanced funnel plots at correct position
for relative effect measures (bug was introduced in
meta, version 4.9-8)
User-visible changes
update.meta():
do not print a warning concerning argument ‘Q.Cochrane’ if argument
‘sm = “ASD”’ for meta-analysis objects created with metabin()
print.summary.meta():
do not print z- and p-values if test for an overall effect was not
conducted; see argument ‘null.effect’ in metamean(), metaprop(), and
metarate()
meta, version 4.9-9
(2019-12-19)
Bug fixes
forest.meta():
printing an additional column on the right side of the forest plot
does not result in an error (bug was introduced in
meta, version 4.9-8)
User-visible changes
labbe():
new argument ‘pos.studlab’
argument checks implemented
baujat(), bubble():
argument ‘pos’ replaced by ‘pos.studlab’
argument checks implemented
meta, version 4.9-8
(2019-12-16)
Major changes
Confidence intervals for the between-study variance tau2 and its
square root tau are calculated
Print tau as well as confidence intervals for tau2 and tau in
outputs
Square root of between-study variance can be printed in forest
plots instead of between-study variance tau2; in addition, the
confidence interval for tau2 or tau can be printed
Use R package metafor to estimate between-study
variance tau2 for DerSimonian-Laird and Paule-Mandel method (which has
been already used for all other methods to estimate tau2)
For Mantel-Haenszel (MH) method, report results as MH method
(instead of inverse variance, IV) for meta-analysis of binary outcome
with a single study (results are identical for MH and IV method in this
situation)
Number of studies printed without digits in forest plots for R
objects created with metabind()
P-values can be printed according to JAMA reporting
standards
In subgroup analyses, print the group labels instead of levels if
the grouping variable is a factor
In funnel plot, print funnel around random effects (instead of
fixed effect) estimate if only random effects meta-analysis is
conducted; only show funnel if either fixed effect or random effects
meta-analysis was conducted
New functions to calculate the number needed to treat from the
results of a meta-analysis
Equivalence limits can be added to forest plots
Font family can be specified in forest plots
Print Wald-type test of heterogeneity for generalised linear
mixed models (problem fixed in R package metafor,
version 2.1-0)
Bug fixes
forest.meta():
(always) print correct length for reference line
(always) print label on x-axis at the correct vertical position
(always) print graph labels on the left and right side of the forest
plot at the correct vertical position
no error if additional numeric variable is added to the right side
of the forest plot (argument ‘rightcols’)
summary.meta():
consider argument ‘bylab’
metaprop():
allow values 0 and 1 for argument ‘null.effect’
User-visible changes
forest.meta():
new arguments ‘lower.equi’, ‘upper.equi’, ‘lty.equi’, ‘col.e’ and
‘fill.equi’ to add equivalence limits
new argument ‘fontfamily’ to specify the font family
forest.metabind():
information on heterogeneity printed for each meta-analysis
Internal changes
ciAgrestiCoull():
set lower confidence limit to 0 for negative values
set upper confidence limit to 1 for values above 1
subgroup meta-analyses return new list element ‘pval.Q.w’
meta, version 4.9-5
(2019-04-11)
Major changes
For the generic inverse variance method, treatment estimates and
standard errors of individual studies can be derived from
p-value or confidence limits
sample size, median, interquartile range and / or range (Wan et
al. (2014), BMC Med Res Meth, 14, 135)
New functions for the conversion of effect measures:
smd2or() - from standardised mean difference to log odds ratio
or2smd() - from log odds ratio to standardised mean difference
Harbord test for funnel plot asymmetry implemented for risk ratio
as effect measure
Generalised linear mixed model is the new default method for
meta-analysis of single proportions using the logit
transformation
R packages metafor and lme4
moved from Suggests to Imports
Suppress printing of Wald-type test of heterogeneity for
generalised linear mixed models (problem in R function rma.glmm() from R
package metafor, version 2.0-0)
Use roxygen2 for development of R package
meta
User-visible changes
metagen():
new arguments ‘pval’, ‘df’, ‘lower’, ‘upper’, ‘level.ci’, ‘median’,
‘q1’, ‘q3’, ‘min’, ‘max’, ‘approx.TE’, ‘approx.seTE’ to approximate
treatment estimates and / or standard errors from other information
forest.meta():
printing of leading zeros in p-values can be suppressed (new
argument ‘zero.pval’)
rounding of values for additional numerical columns possible (new
arguments ‘digits.addcols’, ‘digits.addcols.left’, and
‘digits.addcols.right’)
argument ‘big.mark’ is considered for additional columns
new arguments ‘type.subgroup.fixed’, ‘type.subgroup.random’, and
‘lab.NA.weight’
settings.meta(), gs():
argument names can be abbreviated
Major update of help pages of metagen() and metaprop()
Bug fixes
metacum(), metainf():
consider argument ‘method’ for meta-analysis objects created with
metaprop() or metarate()
forest.meta():
argument ‘studlab’ can be used with objects created with metacum()
or metainf()
subgroup():
return subgroup sample sizes for objects created with metagen()
Internal changes
New internal functions TE.seTE.ci(), TE.seTE.iqr(),
TE.seTE.iqr.range(), TE.seTE.range(), and seTE.ci.pval() to approximate
treatment estimates or standard errors from other information
setchar():
new argument ‘stop.at.error’
metagen():
list element ‘data’ contains the dataset of the meta-analysis object
(i.e., list element ‘data’) instead of the whole meta-analysis
object
meta, version 4.9-4
(2019-01-02)
Major changes
Information on residual heterogeneity in meta-analyses with
subgroups shown in printouts and forest plots
User-visible changes
forest.meta():
new arguments ‘resid.hetstat’ and ‘resid.hetlab’ to control printing
of information on residual heterogeneity in meta-analyses with
subgroups
Bug fixes
forest.meta():
works in meta-analyses with subgroups if argument ‘allstudies =
FALSE’
meta, version 4.9-3
(2018-11-29)
Major changes
New argument ‘control’ in meta-analysis functions which is passed on
to R function rma.uni() or rma.glmm() from R package
metafor to control the iterative process to estimate
the between-study variance tau^2
ignore missing values in covariate to calculate limits on
x-axis
works if dataset used to create meta-analysis object is a tibble
instead of a data frame
Internal changes
metabind():
argument ‘tau.common’ only considered for subgroup analyses
hetcalc():
argument ‘control’ passed on to R function rma.uni() from R package
metafor
metacum(), metainf(), subgroup():
argument ‘control’ from meta-analysis objects considered
meta, version 4.9-2
(2018-06-06)
Major changes
All p-values of Q statistics are list elements of meta-analysis
objects
Bug fixes
metareg():
consider argument ‘intercept = FALSE’ if argument ‘formula’ has been
provided
Internal changes
New internal function replaceNULL()
meta, version 4.9-1
(2018-03-21)
Major changes
Subgroup results consider the exclusion of individual studies
(bug fix)
For generalised linear mixed models, between-study variance set
to NA if only a single study is considered in meta-analysis
Bug fixes
metamean():
use of argument ‘byvar’ for subgroup analyses possible
metacor(), metamean(), metaprop(), metarate():
use as input to metabind() possible
Internal function subgroup():
consider argument ‘exclude’ in subgroup analyses
Internal function bylevs():
drop unused levels if subgroup variable is a factor variable
User-visible changes
print.summary.meta():
print information on Generalised Linear Mixed Model (GLMM) for
metarate() objects
print information on increments added to calculate confidence
intervals for individual studies (for metarate() with GLMM)
funnel.meta():
new arguments ‘ref.triangle’, ‘lty.ref’, ‘lwd.ref’, ‘col.ref’, and
‘lty.ref.triangle’ to add reference value (null effect) and
corresponding confidence intervals to the funnel plot
metabin():
new argument ‘pscale’ to change printout of risk differences
metainc():
new arguments ‘irscale’ and ‘irunit’ to change printout of incidence
rate differences
consider arguments ‘pscale’, ‘irscale’, and ‘irunit’ for
meta-analysis objects created with metabin() and metainc()
print.meta():
new argument ‘irunit’
Internal changes
metaprop():
for random effects model, rma.glmm() from package
metafor is called internally with argument ‘method =
“FE”’ if only a single study is available
metareg():
for generalised linear mixed models, fallback to fixed effect model
if number of studies is too small for random effects
meta-regression
asin2ir():
back transformation could result in (very small) negative zero
values due to imprecisions (-1e-19); these values are set to zero
now
subgroup():
code for metamean() added
chkchar():
new argument ‘nchar’ to test the length of character string(s)
New internal function is.untransformed() to check for effect
measures without (back) transformation
meta, version 4.9-0
(2017-12-06)
Major changes
New function metamean() to conduct meta-analysis of single
means
New function metabind() to combine meta-analysis objects, e.g. to
generate a forest plot with results of several subgroup
analyses
Subgroup analysis implemented for generalised linear mixed models
(GLMMs) with and without assumption of common between-study variance
(arguments ‘byvar’ and ‘tau.common’)
Axis direction can be reversed for x-axis in forest
plots
Source code version of meta can be installed
without compilation, i.e., without use of Rtools on Windows or
‘Command-line tools for Xcode’ on macOS
Rank test for funnel plot asymmetry uses cor() from R package
stats instead of internal C routine (negligibly slower,
however, no need for compilation of source installs)
Thousands separator can be used in printouts and forest plots for
large numbers
P-values equal to 0 are actually printed as “0” instead of “<
0.0001”
new argument ‘big.mark’ to specify character printed as thousands
separator, e.g., big.mark = “,” will result in printing of “1,000”
instead of “1000”
forest.meta():
sensible forest plot generated if first value in argument ‘xlim’ is
larger than second value, e.g. xlim = c(10, -10)
separator between label and levels of grouping variable (argument
‘byseparator’) is considered from meta-analysis object
for relative summary measures, e.g., odds ratio and risk ratio,
labels on x-axis are not rounded to two digits (which resulted in the
value 0 for a tick-mark at 0.001)
bug fix: lines for treatment effect in fixed effect and random
effects model start in center of diamond if argument ‘hetstat =
FALSE’
bug fix: argument ‘type.study’ will be sorted according to arguments
‘sortvar’
metaprop():
arguments ‘byvar’ and ‘tau.common’ can be used for GLMMs
Help page with overview of R functions in R package
meta updated
Internal changes
New internal functions:
is.log.effect() to check for treatment effects combined on log
scale
is.mean() to check whether summary measure refers to meta-analysis
of single means
Renamed internal functions:
formatCI() instead of p.ci()
formatN() instead of format.NA()
formatPT() instead of format.p()
Removed R functions:
format.tau() as functionality is now provided by formatPT()
C program kenscore.c as cor() from R package stats
is used instead to calculate Kendall’s tau
new arguments ‘col.fixed’ and ‘col.random’ to change colour of fixed
effect and random effects lines
Bug fixes
bubble.metareg():
works if covariate in metareg() is not part of dataset used to
generate meta-analysis object
forest.meta():
lines for treatment effect in fixed effect and random effects model
always start in center of diamond
metacum(), metainf():
argument ‘model.glmm’ considered for metabin() and metainc()
objects
print.summary.meta():
print transformed null effect for meta-analysis of single
correlations, proportions, or rates if argument ‘backtransf = FALSE’,
i.e., for metacor(), metaprop(), and metarate() objects
trimfill.meta():
argument ‘null.effect’ is considered to calculate p-value for fixed
effect and random effects model for metacor(), metaprop(), and
metarate() objects
Internal changes
New internal functions is.cor(), is.prop() and is.rate() to check
whether summary measure refers to meta-analysis of correlations,
proportions, or rates
new argument ‘exclude’ to exclude studies from meta-analysis
forest.meta():
new argument ‘spacing’ to determine line spacing
bug fix for for meta-analysis with standardized mean difference (sm
= “SMD”) and argument ‘layout = “RevMan5”’
R function ci() can be used with vectors or matrices of treatment
estimates and standard errors and a single value for argument ‘df’,
i.e., degrees of freedom (which is used in R package
netmeta to calculate prediction intervals for network
meta-analysis estimates)
metacum(), metainf():
argument ‘null.effect’ considered internally for objects generated
with metacor(), metagen(), metaprop() and metarate()
Calculate confidence interval for I2 in a meta-analysis with two
studies if the heterogeneity statistic Q is larger than 2
P-values can be printed in scientific notation
In forest plots, printing of z-values can be disabled and labels
for tests can be changed by user
User-visible changes
forest.meta():
new argument ‘print.zval’ to print (default) or not print z-value
for test of treatment effect
new argument ‘print.Q.subgroup’ to print (default) or not print
Chi-squared statistic for test of subgroup differences
bug fix: print first line above second line if argument ‘xlab’
consists of two lines (bug was introduced in meta,
version 4.8-0)
labels of additional columns are printed in correct line if label
consists of two lines
new argument ‘scientific.pval’ to print p-values in scientific
notation, e.g., 1.2345e-01 instead of 0.12345
arguments ‘label.test.overall.fixed’, ‘label.test.overall.random’,
‘label.test.subgroup.fixed’, ‘label.test.subgroup.random’,
‘label.test.effect.subgroup.fixed’, ‘label.test.effect.subgroup.random’
work as expected
new argument ‘text.subgroup.nohet’ to enable the user to change the
text “not applicable” in the line with heterogeneity statistics for a
subgroup with less than two studies contributing to the
meta-analysis
forest plot without any study contributing to meta-analysis can be
generated without an error, e.g., meta-analysis with binary outcome,
sm=“OR”, and all event numbers equal to zero
print.meta() and print.summary.meta():
new argument ‘scientific.pval’ to print p-values in scientific
notation, e.g., “1.2345e-01” instead of “0.12345”
new arguments ‘print.pval’ and ‘print.pval.Q’ to specify number of
significant digits for p-values
R command ‘help(meta)’ can be used to show brief overview of R
package meta
Substantially decrease number of automatically run examples for
forest.meta() as CRAN only allows a run time below 10 seconds for
examples provided on a help page
Internal changes
new internal function pvalQ() to calculate p-value from
heterogeneity tests
calcH():
Calculate confidence interval for H in a meta-analysis with two
studies if the heterogeneity statistic Q is larger than 2 (this
confidence interval is used in isquared() to calculate a confidence
interval for I2)
hetcalc():
heterogeneity statistic Q set to 0 for a single study contributing
to the meta-analysis (sometimes in this case Q was set to a value below
1e-30)
subgroup():
list element ‘df.Q.b’ set to 0 if number of studies in meta-analysis
is 0
format.p():
new argument ‘lab.NA’ to change value printed for NAs
forest.meta() and print.summary.meta():
use internal function pvalQ() instead of dedicated R code
meta, version 4.8-1
(2017-03-17)
User-visible changes
metacum(), metainf():
bug fix for meta-analysis objects without continuity correction,
i.e., metacont(), metacor(), metagen() (bug was introduced in
meta, version 4.8-0)
bug fix for metarate() objects due to improper use of metaprop()
internally
meta, version 4.8-0
(2017-03-12)
Major changes
Continuity correction can be specified for each individual study in
meta-analysis with proportions or incidence rates
User-visible changes
metabin(), metainc(), metaprop(), metarate():
argument ‘incr’ can be of same length as number of studies in
meta-analysis
metaprop():
bug fix in studies with missing information for events or sample
size and argument ‘method.ci = “CP”’
bug fix to calculate test for an overall effect
forest.meta():
bug fix to print summary label (argument ‘smlab’) above forest plot
if argument ‘fontsize’ is unequal to 12
by default, label on x-axis and text on top of forest plot are
printed in center of forest plot (arguments ‘xlab.pos’,
‘smlab.pos’)
print.summary.meta():
print number of studies for fixed effect meta-analysis using
Mantel-Haenszel method if different from number of studies in random
effects model (only if summary measure is “RD” or “IRD” and at least one
study has zero events)
metainc():
bug fix to consider argument ‘incr’ for incidence rate
difference
Internal changes
act on NOTE in CRAN checks with R version, 3.4.0, to register and
declare native C routine ‘kenscore’
metabin(), metainc():
new list element ‘k.MH’ with number of studies in meta-analysis
using Mantel-Haenszel method
set heterogeneity statistics tau2, H and I2 to NA if only a single
study contributes to meta-analysis
updateversion():
use R function update.meta() if version of meta
used to create R object is below 3.2
meta, version 4.7-1
(2017-02-13)
Major changes
Null hypothesis for test of an overall effect can be specified
for metacor(), metagen(), metaprop(), and metarate(); for all other
meta-analysis functions implicit a null effect of zero is assumed (for
relative effect measures, e.g., odds ratio and hazard ratio, the null
effect is defined on the log scale)
User can choose whether to print the following heterogeneity
quantities: I^2, H, Rb (by default, heterogeneity measure Rb is not
printed and thus revoking a change in meta,
4.7-0)
In forest plots with subgroups, study weights are summed up to
100 percent within each subgroup if no overall estimates are requested,
i.e., argument ‘overall = FALSE’ (like before, by default, weights are
not printed if argument ‘overall = FALSE’ and have to be explicitely
requested using argument ‘leftcols’ or ‘rightcols’)
User-visible changes
forest.meta():
print line with heterogeneity statistics directly below individual
study results if pooled effects are not shown in forest plot (overall =
FALSE)
print right and left labels (arguments ‘label.left’, ‘label.right’)
in correct line if arguments ‘overall = FALSE’ and ‘addrow = FALSE’
bug fix: do not stop with an error if ‘comb.fixed = FALSE’,
‘comb.random = FALSE’, and ‘overall.hetstat = TRUE’
new argument ‘null.effect’ to specify null hypothesis for test of an
overall effect, e.g., null.effect = 0.5 in metaprop() to test whether
the overall proportion is equal to 0.5
metagen():
Hartung-Knapp method only used for at least two studies in
meta-analysis
print.meta():
print covariate with subgroup information for each study, if
subgroup analysis is conducted (argument ‘byvar’)
print.summary.meta():
new arguments ‘print.I2’, ‘print.H’ and ‘print.Rb’ to specify
heterogeneity measures shown in output
new arguments ‘text.tau2’, ‘text.I2’ and ‘text.Rb’ to change text
printed to identify respective heterogeneity measure
only print information on double zero studies if argument
‘allstudies = TRUE’
print results for (empty) subgroup in meta-analysis with two studies
and one subgroup with missing treatment estimate
settings.meta():
new arguments ‘print.I2’, ‘print.H’, ‘print.Rb’, ‘text.tau2’,
‘text.I2’ and ‘text.Rb’ to modify printing of heterogeneity
measures
Internal changes
summary.meta():
bug fix renaming list element ‘ircale’ renamed to ‘irscale’
list element ‘within’ removed which has not been used since
meta, version 1.1-4
meta, version 4.7-0
(2016-12-16)
Major changes
Forest plots:
forest plots with RevMan 5 and JAMA layout
use of mathematical symbols for I^2, tau^2, etc.
individual study results can be omitted from forest plot (especially
useful to only print subgroup results)
Default settings of meta-analysis methods specified via gs()
instead of extracting elements of list .settings (which makes output of
args() easier to read, e.g., args(metabin))
Version of suggested R package metafor must be
at least 1.9-9 (due to change in arguments of rma.uni() and
rma.glmm())
User-visible changes
forest.meta():
argument ‘layout’:
new layout “JAMA” to produce forest plots according to the *JAMA
Network, Instructions for Authors”
RevMan 5 layout extended
arguments can be specified without using grid::unit(): ‘plotwidth’,
‘colgap’, ‘colgap.left’, ‘colgap.right’, ‘colgap.studlab’,
‘colgap.forest’, ‘colgap.forest.left’, ‘colgap.forest.right’
new argument ‘study.results’ to print (default) or omit individual
study results from forest plot
new argument ‘bottom.lr’ to change position of labels on left and
right side of forest plot
new arguments ‘col.label.right’ and ‘col.label.left’ to change
colour of labels on left and right side of forest plot
argument ‘weight’ replaced by ‘weight.study’ and new argument
‘weight.subgroup’ added to specify whether plotted subgroup results
should be of same or different size
new arguments ‘print.Rb’, ‘print.Rb.ci’ and ‘Rb.text’ for
heterogeneity measure Rb
new arguments to control printing: ‘digits.cor’, ‘digits.mean’,
‘digits.sd’, ‘digits.time’, ‘digits.zval’
new argument ‘print.subgroup.labels’ to print (default) or omit rows
with subgroup label from forest plot
new argument ‘type.subgroup’ to change plotting of subgroup
results
argument ‘addspace’ replaced by ‘addrow’
new argument ‘addrow.subgroups’ to add a blank line between subgroup
results
new argument ‘addrow.overall’ to add a blank before meta-analysis
results
new argument ‘blanks’ to enhance printing of test statistics,
heterogeneity measures, and p-values
new argument ‘colgap.studlab’ to specify space between column with
study labels and subsequent column
new arguments to change width of column with study labels (these
arguments are especially useful if only study labels are printed on left
side of forest plot):
‘calcwidth.fixed’ (consider text for fixed effect model)
‘calcwidth.random’ (consider text for random effects model)
‘calcwidth.hetstat’ (consider text for heterogeneity measures)
‘calcwidth.tests’ (consider text for tests of effect or subgroup
differences)
new column “effect.ci” with estimated treatment effect and
confidence interval in one column
use argument ‘test’ instead of ‘knha’ and ‘tdist’ for calls of
rma.uni() and rma.glmm(); change in R package metafor,
version 1.9-9
subgroup():
new measure Rb of between-study heterogeneity implemented
is.installed.package():
new check of version number of R package
use requireNamespace() instead of installed.packages()
format.p():
for small p-values, print “p < 0.01” or “p < 0.001” instead of
“p < 0.0001” if digits.pval is 2 or 3, respectively
new argument ‘zero’ to print “.001” instead of “0.001”, etc
meta-internal():
set defaults for arguments ‘smrate’ and ‘layout’
meta, version 4.6-0
(2016-10-12)
Major changes
New function metarate() to conduct meta-analysis of single
incidence rates
Peters’ test for funnel plot asymmetry implemented for
meta-analysis of single proportions
Meta-analysis of ratio of means added to metacont()
Justification of additional columns in forest plot can be
specified individually for each additional column
Justification of additional columns in forest plot can be
specified individually for each additional column
Calculation of Freeman-Tukey double arcsine transformation and
back transformation slightly changed in meta-analysis of single
proportions
By default, do not print a warning if back transformation for
metaprop() and metarate() objects results in values below 0 or above 1
(only for proportions); note, respective values are set to 0 or
1
User-visible changes
Help page with brief overview of meta package
added
Preferred citation of meta package in
publications changed; see output of command ‘citation(“meta”)’
new arguments ‘irscale’ and ‘irunit’ for meta-analysis objects
created with metarate()
settings.meta():
new arguments ‘smrate’ for meta-analysis objects created with
metarate()
funnel.meta(), funnel.default():
new argument ‘pos.studlab’ to change position of study labels
forest.meta():
new arguments ‘just.addcols.left’ and ‘just.addcols.right’ to
specify justification of additional columns on left and right side of
forest plot
metacont():
meta-analysis for ratio of means implemented (argument ‘sm =
“ROM”’)
new argument ‘backtransf’ for ratio of means (argument ‘sm =
“ROM”’)
metaprop():
change in Freeman-Tukey double arcsine transformation only visible
in printouts if argument ‘backtransf = FALSE’ or if list elements ‘TE’,
‘TE.fixed’, and ‘TE.random’ (as well as confidence intervals) are
extracted from a metaprop object
print.summary.meta():
bug fix in subgroup() to print correct results for subgroup analyses
of metaprop objects with argument ‘sm = “PFT”’
print.meta(), print.summary.meta():
new argument ‘warn.backtransf’ to specify whether a warning should
be printed if backtransformed proportions and rates are below 0 and back
transformed proportions are above 1
extension to handle meta-analysis objects created with
metarate()
metaprop(), asin2p():
calculation of Freeman-Tukey double arcsine transformation changed
to get similar estimates as arcsine transformation, i.e. multiply values
by 0.5
subgroup():
bux fix in calculation of harmonic mean of sample sizes for
metaprop() objects with argument ‘sm = “PFT”’ and event times for
metarate() objects with argument ‘sm = “IRFT”’
meta, version 4.5-0
(2016-08-17)
Major changes
New features in forest plots:
printing of columns on left side of forest plot can be omitted
total person time can be printed
text for fixed effect and random effects model can be omitted from
calculation of width for study labels
plot type for confidence intervals (square or diamond) can be
specified for each study as well as fixed effect and random effects
estimate
printing of test for treatment effect in subgroups possible
New function weights.meta() to calculate absolute and percentage
weights in meta-analysis
New argument ‘byseparator’ to define the separator between label
and subgroup levels which is printed in meta-analysis summaries and
forest plots - considered in all R functions dealing with meta-analysis
and subgroups
Argument ‘pscale’ - a scaling factor for printing of single event
probabilities - considered in all R functions for single proportions;
before this update, argument ‘pscale’ was only available in
forest.meta()
User-visible changes
forest.meta():
argument ‘ref’ considered for metaprop() objects
argument ‘leftcols = FALSE’ omits printing of columns on left side
of forest plot
new argument ‘pooled.times’ to print total person time
new argument ‘calcwidth.pooled’ to include or exclude text from
pooled estimates to determine width of study labels
new argument names (old names can still be used at the moment,
however, will result in an informative warning message):
‘type.study’, ‘type.fixed’, ‘type.random’ to use squares or diamonds
to plot treatment effects and confidence intervals
‘col.inside.fixed’, ‘col.inside.random’ with information on colour
to print confidence interval inside square
‘test.effect.subgroup’, ‘test.effect.subgroup.fixed’,
‘test.effect.subgroup.random’, ‘label.test.effect.subgroup.fixed’,
‘label.test.effect.subgroup.random’, ‘fs.test.effect.subgroup’,
‘ff.test.effect.subgroup’ to print results for test of treatment effect
in subgroups
bug fix to get correct length for reference line and lines for fixed
effect and random effects estimate if argument ‘test.overall =
TRUE’
bug fix to consider arguments ‘lab.e.attach.to.col’ and
‘lab.c.attach.to.col’ for metagen() objects
do not consider columns ‘n.e’ and ‘n.c’ as sample sizes for
metagen() or metainc() object if not used in original call
catmeth(), xlab():
new argument ‘pscale’
catmeth():
for GLMM, print information that continuity correction is only used
to calculate individual study results
metacum(), metainf():
list element ‘pscale’ added
metabin():
list elements ‘incr.e’ and ‘incr.c’ contain zeros for Peto
method
print warning that no continuity correction is used for Peto method
if any of the following arguments is used: ‘incr’, ‘allincr’, ‘addincr’,
‘allstudies’
metacr():
keep dataset used to conduct meta-analysis in list element
‘data’
paulemandel():
bug fix for error if used with a single study
settings.meta():
bug fix to work as expected for argument ‘method.tau’
meta, version 4.4-1
(2016-06-20)
User-visible changes
metareg(), update.meta():
bug fix for error if used with metaprop() object and argument
‘method = “GLMM”’
meta, version 4.4-0
(2016-05-13)
Major changes
Generalised linear mixed models (GLMMs) implemented by internal
call of rma.glmm() from R package metafor by Wolfgang
Viechtbauer
R packages lme4, numDeriv, and
BiasedUrn added to suggested packages which are
required by rma.glmm()
Print layout (especially number of printed digits) slightly
modified which impacts output from print.meta(), print.summary.meta(),
and forest.meta()
New arguments to change number of digits in printouts and forest
plots
User-visible changes
metabin(), metainc(), metaprop():
extension for meta-analysis based on GLMM; see argument ‘method’ and
‘model.glmm’ (not used in metaprop())
new argument ‘…’ to provide additional arguments to rma.glmm()
some arguments can be used for other meta-analysis methods than
inverse variance method: ‘method.tau’, ‘hakn’, ‘tau.common’, ‘TE.tau,
’tau.preset’
metabin():
do not print warning that inverse variance instead of
Mantel-Haenszel method is used for analysis of a single study
print warning if continuity correction (arguments ‘incr’, ‘allincr’,
‘addincr’, ‘allstudies’) is used with arcsine difference, Peto method,
or GLMM
check whether R package BiasedUrn is installed for
conditional hypergeometric-normal GLMM (method = “GLMM”, model.glmm =
“CM.EL”)
forest.meta():
extension to plot meta-analysis based on GLMM
argument ‘labels’ can be used instead of argument ‘label’ to change
labels on x-axis
funnel.meta():
print default labels on y-axis with capital first letter
metareg() and update.meta():
extension for meta-analysis based on GLMM
print.meta():
new arguments to control printing: ‘digits.se’, ‘digits.zval’,
‘digits.Q’, ‘digits.tau2’, ‘digits.H’, ‘digits.I2’, ‘digits.prop’,
‘digits.weight’
argument ‘…’ passed on to internal call of print.summary.meta()
print.summary.meta():
new arguments to control printing: ‘digits.zval’, ‘digits.Q’,
‘digits.tau2’, ‘digits.H’, ‘digits.I2’
print “–” for missing z-value instead of “NA”
only print confidence interval for H and I2 if lower and upper
limits are not NA
print Wald-type and Likelihood-Ratio heterogeneity test for
GLMMs
New function format.NA() to print other text than “NA” for
missing values
metagen():
only call paulemandel() if heterogeneity statistic Q is larger equal
than number of studies minus 1 (otherwise between-study heterogeneity
tau2 is set equal to 0)
metabin(), metainc(), metaprop(), summary.meta():
new list elements ‘model.glmm’, ‘.glmm.fixed’, ‘.glmm.random’,
‘version.metafor’
metabin(), summary.meta():
new list element ‘doublezeros’ for odds ratio or risk ratio as
summary measure
Set defaults for arguments ‘model.glmm’, ‘digits’, ‘digits.se’,
‘digits.zval’, ‘digits.Q’, ‘digits.tau2’, ‘digits.H’, ‘digits.I2’,
‘digits.prop’, ‘digits.weight’, ‘digits.pval’, ‘and
digits.pval.Q’
paulemandel():
more sensible warning if maximum number of iterations is
reached
maximum number of iterations increased from 25 to 100
format.p():
print trailing zeros
catmeth():
print information for GLMMs
print information whether studies with double zeros are included in
meta-analysis
is.installed.package():
new arguments for more flexible error and warning messages: ‘func’,
‘argument’, ‘value’, ‘chksettings’
meta, version 4.3-2
(2015-12-02)
metacont():
bug fix to calculate correct treatment estimates for individual
studies for Glass’s delta
metaprop():
print correct error message if number of events is larger than
number of observations
meta, version 4.3-1
(2015-11-13)
forest.meta():
new arguments ‘digits.se’, ‘digits.tau2’, ‘digits.pval’,
‘digits.pval.Q’, ‘digits.Q’, ‘digits.I2’ to control printing of standard
errors, p-values, tau2 and heterogeneity statistics
new arguments ‘test.overall’ and ‘test.subgroup’ controlling whether
information on test for overall effect and heterogeneity should be
printed
Internal function paulemandel():
bug fix to give studies with missing treatment effect and standard
error zero weight in random effects meta-analysis
do not stop estimation algorithm if estimated tau2 is negative
settings.meta():
bug fix for error if used with an unassigned argument
format.p(), format.tau():
new argument ‘digits’ to round p-values and tau2 values
use inverse variance instead of Mantel-Haenszel method if only a
single study has a non-missing treatment estimate or standard error
settings.meta():
code added for new arguments in forest.meta() to print information
on tests
Help pages of metareg() and forest.meta() and link to RevMan webpage
updated
meta, version 4.2-0
(2015-05-08)
Copyright changed (new names for Institute and Medical
Center)
metacont():
new argument ‘exact.smd’ to implement exact formulae for Hedges’ g
and Cohen’s d (White and Thomas (2005; Hedges, 1981)
use formula from Borenstein et al. (2009) to calculate standard
error for Cohen’s d
forest.meta():
bug fix to appropriately sort additional columns provided in
arguments ‘leftcols’ and ‘rightcols’ if argument ‘sortvar’ is not
missing
new argument ‘print.I2.ci’ to print confidence intervals for I2
forest.meta(), print.meta, print.summary.meta():
prediction interval can be printed if random effects estimate is not
shown
settings.meta(), catmeth(), update.meta():
code added for new argument ‘exact.smd’ in metacont()
ci(), kentau():
calculate p-values without floating point number representation
problems, e.g., the command ci(9, 1) does not result in a p-value of 0
but 2.257177e-19
Several help pages updated to reflect changes in metacont() and
RevMan 5 reference
meta, version 4.1-0
(2015-02-04)
Title of R package changed
metacont():
new argument ‘method.smd’ to implement Cohen’s d (argument
‘method.smd = “Cohen”’) and Glass’ delta (‘method.smd = “Glass”’) as
additional effect measures for the standardised mean difference (‘sm =
“SMD”’)
new argument ‘sd.glass’ to choose the denominator for Glass’
delta
update.meta():
new arguments ‘method.smd’ and ‘sd.glass’ added
summary.meta():
information for new arguments ‘method.smd’ and ‘sd.glass’ added to
summary.meta object
settings.meta():
code added for new arguments ‘method.smd’ and ‘sd.glass’ in
metacont()
forest.meta():
bug fix for staggered point estimates in metaprop() object with
subgroups
metagen():
bug fix to give studies with missing treatment effect but available
standard error zero weight in meta-analysis
paulemandel():
only consider studies without missing treatment effect and standard
error in calculation of between-study variance
chklevel():
print meaningful error message if confidence limit is outside the
range [0, 1]
catmeth():
print information on method to estimate standardised mean difference
in metacont()
Help pages updated for metacont() and update.meta()
meta, version 4.0-3
(2015-01-06)
metabin():
bug fix for error in printing of results for Mantel-Haenszel or Peto
method if any study has zero events in both groups
meta, version 4.0-2
(2014-12-06)
metabin():
bug fix for error if Peto method is used
argument ‘sm = “ASD”’ for arcsine difference instead of ‘sm = “AS”’
(abbreviations ‘sm = “AS”’ and ‘sm = “A”’ can still be used)
metabin(), metacont(), metacor(), metagen(), metainc(), and
metaprop():
weights ‘w.random.w’ are calculated from random effects
meta-analysis ignoring subgroup membership; internal function subgroup()
changed accordingly
argument ‘tau.common = TRUE’ if argument ‘tau.preset’ is not NULL in
subgroup analyses
meta, version 4.0-1
(2014-11-19)
forest.meta():
bug fix for meta-analyses with subgroups if additional columns were
provided in argument ‘leftcols’ or ‘rightcols’
meta, version 4.0-0
(2014-11-19)
Major revision
This update has been declared as major revision as R code to conduct
subgroup analyses has been moved from summary.meta() and forest.meta()
to metabin(), metacont(), metacor(), metagen(), metainc(), and
metaprop(). Accordingly, an R object generated with these functions
contains all results from subgroup analyses.
In the case of subgroups, the overall treatment effect in fixed
effect and random effects meta-analysis ignores subgroup membership. See
Borenstein et al. (2011), Introduction to Meta-Analysis, Wiley, Chapter
19, “Obtaining an overall effect in the presence of subgroups, Option
3.
Furthermore, several checks of function arguments have been
implemented in version 4.0-0 of meta.
Details
Function addvar() removed from R package meta as
functionality is provided by forest.meta()
forest.meta():
new meaning for argument ‘just’ which determines the justification
of all columns but study labels (argument ‘just.studlab’) and columns
added to the forest plot (argument ‘just.addcols’)
new argument ‘just.addcols’ to change justification of text in
additional columns
new arguments ‘text.I2’ and ‘text.tau2’
for metaprop objects, values “n” and “event” handled as standard
columns in argument ‘rightcols’ and ‘leftcols’, i.e. justification is
determined by argument ‘just.cols’
subgroup results printed with the same polygon height as overall
results, i.e. percentage weight is not considered to determine polygon
height for subgroups
bubble.metareg():
bug fix for meta-regression without intercept
bug fix for error in meta-regression using specific effect measure,
e.g. ‘sm = “RR”’, “OR”, or “HR”
consider settings for option ‘OutDec’ (character used as decimal
point in output conversions), e.g., options(OutDec = “,”) will print
“1,0” instead of “1.0”
print.meta(), print.summary.meta():
print ‘p-value’ instead of ‘p.value’
print.summary.meta():
remove code for R objects created with version 2.0-0 or lower of
meta
consider prediction interval to calculate limits on x-axis if
argument ‘prediction = TRUE’
bubble.metareg():
new argument ‘regline’ indicating whether regression line should be
added to plot
settings.meta():
new argument ‘print’ to print listing of all settings as function
call without arguments does not print settings any longer
list with previous settings can be provided as sole input
New functions:
backtransf() to control back transformation of effect measures
is.relative.effect() to check for relative effect measures
File DESCRIPTION:
R package grid defined as Imports instead of
Depends
Help pages updated to reflect changes in version 3.8-0
meta, version 3.7-1
(2014-07-29)
forest.meta():
bug fix to correctly sort lower and upper confidence interval limits
if argument ‘sortvar’ is used (bug was introduced in
meta, version 3.7-0)
argument ‘sortvar’ works without reference to meta-analysis object,
e.g., command forest(meta1, sortvar = TE) can be used instead of
forest(meta1, sortvar = m1$TE)
Help page of forest.meta():
examples using argument ‘sortvar’ added
meta, version 3.7-0
(2014-07-11)
metaprop():
new argument ‘method.ci’ to implement various methods to calculate
confidence intervals for individual studies (default: Clopper-Pearson
method which is also called ‘exact’ binomial method)
list elements ‘zval.fixed’, ‘pval.fixed’, ‘zval.random’ and
‘pval.random’ set to NA
New internal functions:
ciWilsonScore() used in metaprop()
ciAgrestiCoull() used in metaprop()
ciSimpleAsymptotic() used in metaprop()
estimate.missing() used in trimfill.meta() and
trimfill.default()
metacont():
new argument ‘pooledvar’ to conduct meta-analysis of mean
differences based on pooled variance for individual studies
update.meta():
function can be used to upgrade R objects created with older
versions of meta, i.e. all versions between 0.5 and
3.6-0
extended to objects of the following classes:
trimfill()
metacum()
metainf()
new arguments:
‘method.ci’ for metaprop() objects
‘pooledvar’ for metacont() objects
‘left’, ‘ma.fixed’, ‘type’ and ‘n.iter.max’ for trimfill()
objects
new list element ‘call.object’ with call used to generate
meta-analysis object
study labels will only be converted to characters for factor
variables
Help pages
updated to reflect changes in version 3.7-0
argument ‘tau.preset’ correctly described as the
square-root of the between-study variance
meta, version 3.6.0
(2013-05-27)
New functions:
baujat(), baujat.meta() for Baujat plot to explore heterogeneity in
meta-analysis
bubble(), bubble.metareg() for bubble plot to display the result of
a meta-regression
metareg():
class ‘metareg’ added
new list element ‘.meta’ with meta-analysis object used in function
call
update.meta():
argument ‘studlab’ fully functional (bug was introduced in
meta, version 3.2-0)
print.meta():
print study label for a single study in meta-analysis if argument
‘details = TRUE’; data.frame()) instead of cbind() used internally
New internal function is.installed.package() replaces
is.installed.metafor()
Help pages datasets amlodipine and cisapride:
execute examples for Hartung-Knapp method
Help pages merged:
forest(), forest.meta()
funnel(), funnel.meta()
labbe, labbe.metabin()
metabias(), metabias.meta()
trimfill(), trimfill.meta()
meta, version 3.5-1
(2014-05-14)
metabin():
inverse variance method used instead of Mantel-Haenszel method if
argument ‘tau.common = TRUE’
metareg():
tilde sign not necessary in argument ‘formula’ to make this function
more user friendly
forest.meta():
print common tau2 for subgroups if argument ‘tau.common = TRUE’ in
meta-analysis object
metagen():
arguments ‘n.e’ and ‘n.c’ can be part of the dataset provided in
argument ‘data’
DerSimonian-Laird method used instead of Paule-Mandel method if
argument ‘tau.common = TRUE’
metacor(), metainc(), and metaprop():
store value of arguments ‘title’, ‘complab’, and ‘outclab’ in
meta-analysis object
Some help pages (slightly) updated
meta, version 3.5-0
(2014-04-19)
New R function settings.meta() to define and print default
settings for meta-analyses in R package meta
metagen():
Hartung and Knapp method added; previously rma.uni() from R package
metafor was called for this method
Paule-Mandel method to estimate between-study variance implemented
using new internal function paulemandel() which is based on
mpaule.default() from R package metRology by S.L.R.
Ellison <s.ellison at lgc.co.uk> (Author of mpaule.default() is S.
Cowen <simon.cowen at lgc.co.uk> with amendments by S.L.R.
Ellison)
metacont():
studies with missing treatment estimate get zero weight in
meta-analysis
default values changed according to settings.meta()
metareg():
use argument ‘method.tau = “REML”’ if this argument is equal to “PM”
for meta-analysis object
Several help pages updated
meta, version 3.2-1
(2014-03-26)
forest.meta():
bug fix to show correct confidence limits for individual studies if
argument ‘level’ is not equal to the default 0.95. (bug was introduced
in meta, version 3.0-0)
heterogeneity statistics I2 and H added to R object
column names changed in list element ‘data’; columns starting with a
“.” used internally in update.meta()
string “byvar” is used as default label for grouping variable if
argument ‘bylab’ is not provided
metareg():
variable ‘.byvar’ used instead of ‘byvar’ to reflect change in list
element ‘data’
update.meta():
arguments ‘byvar’ and ‘subset’ fully functional
variables ‘.TE’, … used internally instead of TE, … to reflect
change in list element ‘data’
trimfill.default(), trimfill.meta():
heterogeneity statistics I2 and H added to R object
metagen():
bug fix to correctly calculate weights (list elements ‘w.fixed’ and
‘w.random’) if any standard error is missing or zero for the
Hartung-Knapp method (argument ‘hakn = TRUE’) or the DerSimonian Laird
method is not used (argument ‘method.tau’ not equal to “DL”)
summary.meta():
subgroup analysis implemented for metainc() objects
forest.meta():
groups will not be sorted automatically in alphabetical order (new
argument ‘bysort’). Use argument ‘bysort = FALSE’ to get the old
behaviour of forest.meta()
forest.meta(), summary.meta():
only (re)calculate heterogeneity statistics (Q, tau2, I2) for R
objects generated with older versions of R package
meta
catmeth():
new argument ‘tau.preset’ to print information if between-study
variance was pre-specified
print.meta(), print.summary.meta():
argument ‘tau.preset’ used in catmeth()
New internally used functions isquared() and calcH()
Some help pages updated
meta, version 3.1-2
(2013-12-01)
forest.meta():
bug fix for error in meta-analyses with subgroups using any but
metaprop() (bug was introduced in meta, version
3.1-1)
meta, version 3.1-1
(2013-11-19)
forest.meta():
bug fix to show random effects estimate in metaprop() objects with
subgroups using argument ‘sm = “PFT”’
meta, version 3.1-0
(2013-11-11)
New R function metainc() for meta-analysis of incidence
rates
Continuity correction:
metabin() and metaprop() do no longer print a warning in case of
studies with a zero cell frequency
instead information on continuity correction is given under “Details
on meta-analytical method” if a corresponding meta-analysis object is
printed
use correct variable names for ‘event’ and ‘n’ in list element
‘data’ if metaprop() is called without argument ‘data’
metabin():
inverse variance method (argument ‘sm = “Inverse”’) is used
automatically if argument ‘tau.common = TRUE’
bug fix for error if argument ‘tau.common = TRUE’ and ‘method =
“MH”’
catmeth():
print information on continuity correction for objects of class
“metabin”, “metaprop”, and “metainc”
summary.meta():
fixed effect and random effects estimates and confidence intervals
are only (re)calculated for R objects created with
meta, version < 2 if argument ‘level.comb’ has not
been used
trimfill.meta(), trimfill.default():
new list elements ‘lower.fixed’, ‘upper.fixed’, ‘zval.fixed’,
‘pval.fixed’, ‘lower.random’, ‘upper.random’, ‘zval.random’,
‘pval.random’ added to trimfill() object (bug was introduced in
meta, version 2.0-0)
New datasets smoking and lungcancer as examples for
metainc()
meta, version 3.0-1
(2013-09-17)
Major revision
This update has been declared as major revision as the user interface
changed by dropping some arguments: - print.meta(), forest.meta(),
summary.meta(): ‘level’, ‘level.prediction’ - print.meta(),
forest.meta(), metainf(), metacum(): ‘level.comb’ - in forest.meta(),
summary.meta(): ‘byvar’
This functionality is now provided by update.meta().
Details
New function update.meta() to update an existing meta-analysis
object created with metabin(), metacont(), metagen(), metaprop(), or
metacor()
New function cilayout() to change layout of confidence
intervals
new arguments ‘prediction’ and ‘level.predict’ (prediction interval
for a new study)
new argument ‘tau.common’ (common tau2 across subgroups)
help pages updated accordingly
metaprop():
new default summary measure (sm = “PLOGIT”)
deprecated argument ‘freeman.tukey’ removed
summary.meta():
new arguments ‘prediction’ and ‘level.predict’
list element ‘tau.common’ from meta-analysis object considered
correct values for list elements ‘incr’, ‘allincr’, and ‘addincr’
used in calculations for metaprop() objects
forest.meta():
new arguments for prediction interval: ‘prediction’,
‘level.predict’, ‘text.predict’, ‘col.predict’, ‘col.predict.lines’,
‘fs.predict’, ‘fs.predict.labels’, ‘ff.predict’,
’ff.predict.labels”
correct values for list elements ‘incr’, ‘allincr’, and ‘addincr’
used in calculations for metaprop() objects
information on confidence limit printed for pooled estimates if CI
level is different from CI level for individual studies
print.summary.meta():
new argument ‘prediction’
new list element ‘tau.common’
catmeth():
print information on use of common tau2 across subgroups
meta, version 2.3-0
(2013-05-12)
forest.meta():
results for fixed effect and random effects models only
(re)calculated for meta-analysis objects created with
meta, version < 2
metabin():
bug fix for error if argument ‘sm = “RR”’ and ‘allstudies = TRUE’ in
meta-analysis with zero events in both groups
meta, version 2.2-1
(2013-03-20)
forest.meta():
new argument ‘lab.NA’ to label missing values (in older version of R
package meta the fixed label ‘NA’ was used)
arguments ‘colgap.forest.left’ and ‘colgap.forest.right’ considered
instead of only ‘colgap.forest’
labbe.metabin(), labbe.default():
bug fix for error if any event probability is equal to NA
format.p():
bug fix for error if first argument contains any NAs
meta, version 2.2-0
(2013-03-12)
metabin():
studies with all events in both groups will be included in
meta-analysis by default (in older meta versions these
studies were only included if argument ‘allstudies = TRUE’)
standard error is positive for studies with all events in both
groups (see Hartung & Knapp (2001), Stat Med, equation (18))
forest.meta():
values provided by argument ‘xlim’ will be used as x-axis label for
relative effect measures like risk ratio or odds ratio
default values for arguments ‘smlab.pos’ and ‘xlab.pos’ changed to
always fall within plotting range
correct back transformation of Freeman-Tukey Double arcsine
transformation for metacum() and metainf() objects
asin2p():
values outside the admissible range are set to the boundary values
[0, 1]; a warning is printed in this casea
Help pages:
new argument ‘n.harmonic.mean’ documented for metacum() and
metainf()
meta, version 2.1-3
(2012-11-20)
forest.meta():
bug fix for metacum() or metainf() object with Freeman-Tukey double
arcsine transformation (error message: ‘Error in if (col\(range[1] <= TE.fixed & TE.fixed <=
col\)range[2]) …’)
treatment effect and 95% confidence interval is printed in the
correct order for objects of class “metaprop” if argument ‘sort’ or
‘order’ is used
symmetric forest plot by default (argument xlim = “s”)
new arguments:
‘smlab’, ‘smlab.pos’, ‘fs.smlab’, ‘fflab’ for label of summary
measure at top of figure
‘label.right’, ‘label.left’, ‘fs.lr’, ‘ff.lr’ for label on right and
left side below the x-axis
‘overall.hetstat’ to show heterogeneity information for overall
effect
funnel.default(), funnel.meta():
arguments ‘col.fixed’ and ‘col.random’ are recognised
metabias.default(), metabias.meta():
new argument ‘k.min’ to only conduct test for funnel plot asymmetry
if number of studies in meta-analysis is larger or equal to ‘k.min’
new argument ‘…’ (ignored at the moment)
trimfill.default(), trimfill.meta():
return ‘invisible(NULL)’ if number of studies is smaller than 3
New datasets: amlodipine, cisapride
File FLEISS93.MTV moved from directory data to directory
extdata
Several help pages updated
Some new help pages added
meta, version 1.6-1
(2010-10-28)
forest.meta():
number of events is printed in the correct order for objects of
class “metaprop” if argument ‘sort’ or ‘order’ is used
transformed proportions are printed for individual studies in column
‘TE’ if metagen() is used with argument ‘sm’ equal to either “PLN”,
“PLOGIT”, “PAS”, or “PFT”
as.data.frame.meta():
function works for meta-analyses with six studies which previously
resulted in an error message ‘Error: evaluation nested too deeply:
infinite recursion …’
new argument ‘…’
addvar():
option stringsAsFactors = FALSE added to internal call of
as.data.frame.meta()
additional checks for existence of columns ‘by.x’ and ‘by.y’
additional checks for situations with duplicate entries for columns
‘by.x’ and ‘by.y’ added
print.meta():
back transformed proportions are printed for individual studies if
metagen() is used with argument ‘sm’ equal to either “PLN”, “PLOGIT”,
“PAS”, or “PFT”
Examples in help pages (slightly) updated:
read.mtv(), read.rm5(), metacr()
meta, version 1.6-0
(2010-06-21)
forest.meta():
for subgroup analyses (i.e. groups defined by argument ‘byvar’),
result for both fixed effect and random effects model are printed (in
older versions of the meta package only results for
either fixed effect or random effects model could be printed)
new arguments ‘text.fixed.w’ and ‘text.random.w’ to specify label
for estimates within subgroups
new arguments to change colour of several parts of the plot:
‘col.i.inside.square’, ‘col.square’, ‘col.square.lines’, ‘col.diamond’,
‘col.diamond.fixed’, ‘col.diamond.random’, ‘col.diamond.lines’,
‘col.diamond.fixed.lines’, ‘col.diamond.random.lines’
new arguments to print information on heterogeneity measures:
‘print.I2’, ‘print.tau2’, ‘print.Q’, ‘print.pval.Q’, ‘hetstat’,
‘hetlab’
new arguments to change fontsize and fontface of several parts of
the plot: ‘fs.heading’, ‘fs.fixed’, ‘fs.random’, ‘fs.study’,
‘fs.fixed.labels’, ‘fs.random.labels’, ‘fs.study.labels’, ‘fs.hetstat’,
‘fs.axis’, ‘fs.xlab’, ‘ff.heading’, ‘ff.fixed’, ‘ff.random’, ‘ff.study’,
‘ff.fixed.labels’, ‘ff.random.labels’, ‘ff.study.labels’, ‘ff.hetstat’,
‘ff.axis’, ‘ff.xlab’
new arguments to change gap between columns: ‘colgap.left’,
‘colgap.right=colgap’, ‘colgap.forest’, ‘colgap.forest.left’,
‘colgap.forest.right’
new argument ‘just’ to change justification of text for additional
columns
new argument ‘addspace’ to print a blank line at top and bottom of
study results
new argument ‘new’ indicating whether a new figure should be printed
in an existing graphics window (internally, grid.newpage() is used if
argument ‘new = TRUE’)
no line is printed for the fixed effect or random effects model if
argument ‘lty.fixed = NULL’ or ‘lty.random = NULL’
symmetric forest plots can be produced by setting argument ‘xlim =
“s”’
print.summary.meta():
for subgroup analyses (i.e. groups defined by argument ‘byvar’),
result for test of heterogeneity printed separately for fixed effect and
random effects model
metabin(), summary.meta(), print.summary.meta():
new argument ‘print.CMH’ indicating whether Cochran-Mantel-Haenszel
test for overall effect should be printed (default ‘print.CMH =
FALSE’)
print warning that function was replaced by forest.meta()
New list element ‘version’ with information on version number of
meta package used to create an object; applies only to
object creating functions, e.g. metabin() and metabias()
Several help pages updated
Use file NEWS instead of ChangeLog to document changes
meta, version 1.1-8
(2010-01-12)
summary.meta(), print.summary.meta():
test for subgroup differences is not calculated and printed for
meta-analyses using the Mantel-Haenszel method for binary data
meta, version 1.1-7
(2010-01-11)
metabin(), metacont(), metagen(), metaprop():
sensible default value is used for argument ‘bylab’ if argument
‘byvar’ is not missing
meta, version 1.1-6
(2010-01-11)
forest():
additional columns are printed in the correct order if argument
‘sort’ or ‘order’ is used
meta, version 1.1-5
(2009-12-21)
forest():
new argument ‘digits’ specifying minimal number of significant
digits for treatment estimate and its confidence interval
meta, version 1.1-4
(2009-11-04)
summary.meta():
results for subgroups (if byvar != NULL) are calculated for both
fixed effect and random effects model:
list ‘within’ no longer returned by summary.meta()
lists ‘within.fixed’ and ‘within.random’ returned by
summary.meta()
variable name of subgroups is printed correctly
check whether input is an object of class “summary.meta”
print.summary.meta():
a warning is printed if both ‘comb.fixed’ and ‘comb.random’ are TRUE
and results for subgroups are supposed to be printed
Help pages of print.summary.meta() and forest() updated:
detailed information on printing and plotting of subgroup results if
both comb.fixed and comb.random are TRUE
Help page of metagen() updated:
new example with meta-analysis of survival data
meta, version 1.1-3
(2009-10-30)
Generic method for trim-and-fill method: trimfill(),
trimfill.default(), trimfill.meta()