Description Usage Arguments Details Value Note Author(s) References See Also Examples
Fixed effect and random effects metaanalysis based on estimates (e.g. log hazard ratios) and their standard errors. The inverse variance method is used for pooling.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61  metagen(
TE,
seTE,
studlab,
data = NULL,
subset = NULL,
exclude = NULL,
sm = "",
method.ci = if (missing(df)) "z" else "t",
level = gs("level"),
level.comb = gs("level.comb"),
comb.fixed = gs("comb.fixed"),
comb.random = gs("comb.random"),
overall = comb.fixed  comb.random,
overall.hetstat = comb.fixed  comb.random,
hakn = gs("hakn"),
adhoc.hakn = gs("adhoc.hakn"),
method.tau = gs("method.tau"),
method.tau.ci = if (method.tau == "DL") "J" else "QP",
tau.preset = NULL,
TE.tau = NULL,
tau.common = gs("tau.common"),
prediction = gs("prediction"),
level.predict = gs("level.predict"),
null.effect = 0,
method.bias = gs("method.bias"),
n.e = NULL,
n.c = NULL,
pval,
df,
lower,
upper,
level.ci = 0.95,
median,
q1,
q3,
min,
max,
method.mean = "Luo",
method.sd = "Shi",
approx.TE,
approx.seTE,
backtransf = gs("backtransf"),
pscale = 1,
irscale = 1,
irunit = "personyears",
title = gs("title"),
complab = gs("complab"),
outclab = "",
label.e = gs("label.e"),
label.c = gs("label.c"),
label.left = gs("label.left"),
label.right = gs("label.right"),
byvar,
bylab,
print.byvar = gs("print.byvar"),
byseparator = gs("byseparator"),
keepdata = gs("keepdata"),
warn = gs("warn"),
control = NULL
)

TE 
Estimate of treatment effect, e.g., log hazard ratio or risk difference. 
seTE 
Standard error of treatment estimate. 
studlab 
An optional vector with study labels. 
data 
An optional data frame containing the study information. 
subset 
An optional vector specifying a subset of studies to be used (see Details). 
exclude 
An optional vector specifying studies to exclude from metaanalysis, however, to include in printouts and forest plots (see Details). 
sm 
A character string indicating underlying summary measure,
e.g., 
method.ci 
A character string indicating which method is used to calculate confidence intervals for individual studies, see Details. 
level 
The level used to calculate confidence intervals for individual studies. 
level.comb 
The level used to calculate confidence intervals for pooled estimates. 
comb.fixed 
A logical indicating whether a fixed effect metaanalysis should be conducted. 
comb.random 
A logical indicating whether a random effects metaanalysis should be conducted. 
overall 
A logical indicating whether overall summaries should be reported. This argument is useful in a metaanalysis with subgroups if overall results should not be reported. 
overall.hetstat 
A logical value indicating whether to print heterogeneity measures for overall treatment comparisons. This argument is useful in a metaanalysis with subgroups if heterogeneity statistics should only be printed on subgroup level. 
hakn 
A logical indicating whether method by Hartung and Knapp should be used to adjust test statistics and confidence intervals. 
adhoc.hakn 
A character string indicating whether an ad
hoc variance correction should be applied in the case of an
arbitrarily small HartungKnapp variance estimate. Either

method.tau 
A character string indicating which method is
used to estimate the betweenstudy variance τ^2 and its
square root τ. Either 
method.tau.ci 
A character string indicating which method is
used to estimate the confidence interval of τ^2 and
τ. Either 
tau.preset 
Prespecified value for the square root of the betweenstudy variance τ^2. 
TE.tau 
Overall treatment effect used to estimate the betweenstudy variance tausquared. 
tau.common 
A logical indicating whether tausquared should be the same across subgroups. 
prediction 
A logical indicating whether a prediction interval should be printed. 
level.predict 
The level used to calculate prediction interval for a new study. 
null.effect 
A numeric value specifying the effect under the null hypothesis. 
method.bias 
A character string indicating which test is to
be used. Either 
n.e 
Number of observations in experimental group (or total sample size in study). 
n.c 
Number of observations in control group. 
pval 
Pvalue (used to estimate the standard error). 
df 
Degrees of freedom (used in test or to construct confidence interval). 
lower 
Lower limit of confidence interval (used to estimate the standard error). 
upper 
Upper limit of confidence interval (used to estimate the standard error). 
level.ci 
Level of confidence interval. 
median 
Median (used to estimate the treatment effect and standard error). 
q1 
First quartile (used to estimate the treatment effect and standard error). 
q3 
Third quartile (used to estimate the treatment effect and standard error). 
min 
Minimum (used to estimate the treatment effect and standard error). 
max 
Maximum (used to estimate the treatment effect and standard error). 
method.mean 
A character string indicating which method to use to approximate the mean from the median and other statistics (see Details). 
method.sd 
A character string indicating which method to use to approximate the standard deviation from sample size, median, interquartile range and range (see Details). 
approx.TE 
Approximation method to estimate treatment estimate (see Details). 
approx.seTE 
Approximation method to estimate standard error (see Details). 
backtransf 
A logical indicating whether results should be
back transformed in printouts and plots. If 
pscale 
A numeric giving scaling factor for printing of
single event probabilities or risk differences, i.e. if argument

irscale 
A numeric defining a scaling factor for printing of
single incidence rates or incidence rate differences, i.e. if
argument 
irunit 
A character specifying the time unit used to calculate rates, e.g. personyears. 
title 
Title of metaanalysis / systematic review. 
complab 
Comparison label. 
outclab 
Outcome label. 
label.e 
Label for experimental group. 
label.c 
Label for control group. 
label.left 
Graph label on left side of forest plot. 
label.right 
Graph label on right side of forest plot. 
byvar 
An optional vector containing grouping information
(must be of same length as 
bylab 
A character string with a label for the grouping variable. 
print.byvar 
A logical indicating whether the name of the grouping variable should be printed in front of the group labels. 
byseparator 
A character string defining the separator between label and levels of grouping variable. 
keepdata 
A logical indicating whether original data (set) should be kept in meta object. 
warn 
A logical indicating whether warnings should be printed (e.g., if studies are excluded from metaanalysis due to zero standard errors). 
control 
An optional list to control the iterative process to
estimate the betweenstudy variance τ^2. This argument
is passed on to 
This function provides the generic inverse variance method
for metaanalysis which requires treatment estimates and their
standard errors (Borenstein et al., 2010). The method is useful,
e.g., for pooling of survival data (using log hazard ratio and
standard errors as input). Arguments TE
and seTE
can
be used to provide treatment estimates and standard errors
directly. However, it is possible to derive these quantities from
other information.
Default settings are utilised for several arguments (assignments
using gs
function). These defaults can be changed for
the current R session using the settings.meta
function.
Furthermore, R function update.meta
can be used to
rerun a metaanalysis with different settings.
Missing treatment estimates can be derived from
confidence limits provided by arguments lower
and
upper
;
median, interquartile range and range (arguments
median
, q1
, q3
, min
, and max
);
median and interquartile range (arguments median
,
q1
and q3
);
median and range (arguments median
, min
and
max
).
For confidence limits, the treatment estimate is defined as the center of the confidence interval (on the log scale for relative effect measures like the odds ratio or hazard ratio).
If the treatment effect is a mean it can be approximated from
sample size, median, interquartile range and range. By default,
methods described in Luo et al. (2018) are utilized (argument
method.mean = "Luo"
):
equation (7) if sample size, median and range are available,
equation (11) if sample size, median and interquartile range are available,
equation (15) if sample size, median, range and interquartile range are available.
Instead the methods described in Wan et al. (2014) are used if
argument method.mean = "Wan"
):
equation (2) if sample size, median and range are available,
equation (14) if sample size, median and interquartile range are available,
equation (10) if sample size, median, range and interquartile range are available.
By default, missing treatment estimates are replaced successively
using these method, i.e., confidence limits are utilised before
interquartile ranges. Argument approx.TE
can be used to
overwrite this default for each individual study:
Use treatment estimate directly (entry ""
in argument
approx.TE
);
confidence limits ("ci"
in argument approx.TE
);
median, interquartile range and range ("iqr.range"
);
median and interquartile range ("iqr"
);
median and range ("range"
).
Missing standard errors can be derived from
pvalue provided by arguments pval
and (optional)
df
;
confidence limits (arguments lower
, upper
, and
(optional) df
);
sample size, median, interquartile range and range (arguments
n.e
and / or n.c
, median
, q1
,
q3
, min
, and max
);
sample size, median and interquartile range (arguments
n.e
and / or n.c
, median
, q1
and
q3
);
sample size, median and range (arguments n.e
and / or
n.c
, median
, min
and max
).
For pvalues and confidence limits, calculations are either based
on the standard normal or t distribution if argument
df
is provided. Furthermore, argument level.ci
can be
used to provide the level of the confidence interval.
Wan et al. (2014) describe methods to estimate the standard
deviation (and thus the standard error by deviding the standard
deviation with the square root of the sample size) from the sample
size, median and additional statistics. Shi et al. (2020) provide
an improved estimate of the standard deviation if the interquartile
range and range are available in addition to the sample size and
median. Accordingly, equation (11) in Shi et al. (2020) is the
default (argument method.sd = "Shi"
), if the median,
interquartile range and range are provided (arguments
median
, q1
, q3
, min
and
max
). The method by Wan et al. (2014) is used if argument
method.sd = "Wan"
and, depending on the sample size, either
equation (12) or (13) is used. If only the interquartile range or
range is available, equations (15) / (16) and (7) / (9) in Wan et
al. (2014) are used, respectively. The sample size of individual
studies must be provided with arguments n.e
and / or
n.c
. The total sample size is calculated as n.e
+
n.c
if both arguments are provided.
By default, missing standard errors are replaced successively using
these method, e.g., pvalue before confidence limits before
interquartile range and range. Argument approx.seTE
can be
used to overwrite this default for each individual study:
Use standard error directly (entry ""
in argument
approx.seTE
);
pvalue ("pval"
in argument approx.seTE
);
confidence limits ("ci"
);
median, interquartile range and range ("iqr.range"
);
median and interquartile range ("iqr"
);
median and range ("range"
).
For the mean difference (argument sm = "MD"
), the confidence
interval for individual studies can be based on the
standard normal distribution (method.ci = "z"
), or
tdistribution (method.ci = "t"
).
By default, the first method is used if argument df
is
missing and the second method otherwise.
Note, this choice does not affect the results of the fixed effect and random effects metaanalysis.
The following methods are available to estimate the betweenstudy variance τ^2.
Argument  Method 
method.tau = "DL"  DerSimonianLaird estimator (DerSimonian and Laird, 1986) 
method.tau = "PM"  PauleMandel estimator (Paule and Mandel, 1982) 
method.tau = "REML"  Restricted maximumlikelihood estimator (Viechtbauer, 2005) 
method.tau = "ML"  Maximumlikelihood estimator (Viechtbauer, 2005) 
method.tau = "HS"  HunterSchmidt estimator (Hunter and Schmidt, 2015) 
method.tau = "SJ"  SidikJonkman estimator (Sidik and Jonkman, 2005) 
method.tau = "HE"  Hedges estimator (Hedges and Olkin, 1985) 
method.tau = "EB"  Empirical Bayes estimator (Morris, 1983) 
Historically, the DerSimonianLaird method was the de facto
standard to estimate the betweenstudy variance τ^2 and is
still the default in many software packages including Review
Manager 5 (RevMan 5) and R package meta. However, its role
has been challenged and especially the PauleMandel and REML
estimators have been recommended (Veroniki et al.,
2016). Accordingly, the following R command can be used to use the
PauleMandel estimator in all metaanalyses of the R session:
settings.meta(method.tau = "PM")
The following methods to calculate a confidence interval for τ^2 and τ are available.
Argument  Method 
method.tau.ci = "J"  Method by Jackson (2013) 
method.tau.ci = "BJ"  Method by Biggerstaff and Jackson (2008) 
method.tau.ci = "QP"  QProfile method (Viechtbauer, 2007) 
These methods have been recommended by Veroniki et al. (2016). By
default, the Jackson method is used for the DerSimonianLaird
estimator of τ^2 and the Qprofile method for all other
estimators of τ^2. No confidence intervals for
τ^2 and τ are calculated if method.tau.ci =
""
.
Hartung and Knapp (2001a,b) proposed an alternative method for
random effects metaanalysis based on a refined variance estimator
for the treatment estimate. Simulation studies (Hartung and Knapp,
2001a,b; IntHout et al., 2014; Langan et al., 2019) show improved
coverage probabilities compared to the classic random effects
method. However, in rare settings with very homogeneous treatment
estimates, the HartungKnapp (HK) variance estimate can be
arbitrarily small resulting in a very narrow confidence interval
(Knapp and Hartung, 2003; Wiksten et al., 2016). In such cases, an
ad hoc variance correction has been proposed by utilising
the variance estimate from the classic random effects model (Knapp
and Hartung, 2003). Argument adhoc.hakn
can be used to
choose the ad hoc method:
Argument  Ad hoc method 
adhoc.hakn = ""  not used 
adhoc.hakn = "se"  used if HK standard error is smaller than standard error 
from classic random effects model (Knapp and Hartung, 2003)  
adhoc.hakn = "ci"  used if HK confidence interval is narrower than CI from 
classic random effects model with DL estimator (IQWiG, 2020) 
A prediction interval for the treatment effect of a new study
(Higgins et al., 2009) is calculated if arguments prediction
and comb.random
are TRUE
. Note, the definition of
prediction intervals varies in the literature. This function
implements equation (12) of Higgins et al., (2009) which proposed a
t distribution with K2 degrees of freedom where
K corresponds to the number of studies in the metaanalysis.
Argument byvar
can be used to conduct subgroup analysis for
a categorical covariate. The metareg
function can be
used instead for more than one categorical covariate or continuous
covariates.
Argument null.effect
can be used to specify the (treatment)
effect under the null hypothesis in a test for an overall
effect.
By default (null.effect = 0
), the null hypothesis
corresponds to "no difference" (which is obvious for absolute
effect measures like the mean difference (sm = "MD"
) or
standardised mean difference (sm = "SMD"
)). For relative
effect measures, e.g., risk ratio (sm = "RR"
) or odds ratio
(sm = "OR"
), the null effect is defined on the log scale,
i.e., ln(RR) = 0 or ln(OR) = 0 which is equivalent to
testing RR = 1 or OR = 1.
Use of argument null.effect
is especially useful for summary
measures without a "natural" null effect, i.e., in situations
without a second (treatment) group. For example, an overall
proportion of 50% could be tested in the metaanalysis of single
proportions with argument null.effect = 0.5
.
Note, all tests for an overall effect are twosided with the
alternative hypothesis that the effect is unequal to
null.effect
.
Arguments subset
and exclude
can be used to exclude
studies from the metaanalysis. Studies are removed completely from
the metaanalysis using argument subset
, while excluded
studies are shown in printouts and forest plots using argument
exclude
(see Examples).
Metaanalysis results are the same for both arguments.
Internally, both fixed effect and random effects models are
calculated regardless of values choosen for arguments
comb.fixed
and comb.random
. Accordingly, the estimate
for the random effects model can be extracted from component
TE.random
of an object of class "meta"
even if
argument comb.random = FALSE
. However, all functions in R
package meta will adequately consider the values for
comb.fixed
and comb.random
. For example, functions
print.meta
and forest.meta
will not
show results for the random effects model if comb.random =
FALSE
.
Argument pscale
can be used to rescale single proportions or
risk differences, e.g. pscale = 1000
means that proportions
are expressed as events per 1000 observations. This is useful in
situations with (very) low event probabilities.
Argument irscale
can be used to rescale single rates or rate
differences, e.g. irscale = 1000
means that rates are
expressed as events per 1000 time units, e.g. personyears. This is
useful in situations with (very) low rates. Argument irunit
can be used to specify the time unit used in individual studies
(default: "personyears"). This information is printed in summaries
and forest plots if argument irscale
is not equal to 1.
Default settings for comb.fixed
, comb.random
,
pscale
, irscale
, irunit
and several other
arguments can be set for the whole R session using
settings.meta
.
An object of class c("metagen", "meta")
with corresponding
print
, summary
, and forest
functions. The
object is a list containing the following components:
TE, seTE, studlab, exclude, n.e, n.c 
As defined above. 
sm, method.ci, level, level.comb, 
As defined above. 
comb.fixed, comb.random, 
As defined above. 
overall, overall.hetstat, 
As defined above. 
hakn, adhoc.hakn, method.tau, method.tau.ci, 
As defined above. 
tau.preset, TE.tau, method.bias, 
As defined above. 
tau.common, title, complab, outclab, 
As defined above. 
label.e, label.c, label.left, label.right, 
As defined above. 
byvar, bylab, print.byvar, byseparator, warn 
As defined above. 
lower, upper 
Lower and upper confidence interval limits for individual studies. 
statistic, pval 
Statistic and pvalue for test of treatment effect for individual studies. 
w.fixed, w.random 
Weight of individual studies (in fixed and random effects model). 
TE.fixed, seTE.fixed 
Estimated overall treatment effect and standard error (fixed effect model). 
lower.fixed, upper.fixed 
Lower and upper confidence interval limits (fixed effect model). 
statistic.fixed, pval.fixed 
Statistic and pvalue for test of overall treatment effect (fixed effect model). 
TE.random, seTE.random 
Estimated overall treatment effect and standard error (random effects model). 
lower.random, upper.random 
Lower and upper confidence interval limits (random effects model). 
statistic.random, pval.random 
Statistic and pvalue for test of overall treatment effect (random effects model). 
prediction, level.predict 
As defined above. 
seTE.predict 
Standard error utilised for prediction interval. 
lower.predict, upper.predict 
Lower and upper limits of prediction interval. 
null.effect 
As defined above. 
k 
Number of studies combined in metaanalysis. 
Q 
Heterogeneity statistic. 
df.Q 
Degrees of freedom for heterogeneity statistic. 
pval.Q 
Pvalue of heterogeneity test. 
tau2 
Betweenstudy variance τ^2. 
se.tau2 
Standard error of τ^2. 
lower.tau2, upper.tau2 
Lower and upper limit of confidence interval for τ^2. 
tau 
Squareroot of betweenstudy variance τ. 
lower.tau, upper.tau 
Lower and upper limit of confidence interval for τ. 
H 
Heterogeneity statistic H. 
lower.H, upper.H 
Lower and upper confidence limit for heterogeneity statistic H. 
I2 
Heterogeneity statistic I^2. 
lower.I2, upper.I2 
Lower and upper confidence limit for heterogeneity statistic I^2. 
Rb 
Heterogeneity statistic R_b. 
lower.Rb, upper.Rb 
Lower and upper confidence limit for heterogeneity statistic R_b. 
approx.TE, approx.seTE 
As defined above. 
method 
Pooling method: 
df.hakn 
Degrees of freedom for test of treatment effect for
HartungKnapp method (only if 
bylevs 
Levels of grouping variable  if 
TE.fixed.w, seTE.fixed.w 
Estimated treatment effect and
standard error in subgroups (fixed effect model)  if

lower.fixed.w, upper.fixed.w 
Lower and upper confidence
interval limits in subgroups (fixed effect model)  if

statistic.fixed.w, pval.fixed.w 
Statistics and pvalues for
test of treatment effect in subgroups (fixed effect model)  if

TE.random.w, seTE.random.w 
Estimated treatment effect and
standard error in subgroups (random effects model)  if

lower.random.w, upper.random.w 
Lower and upper confidence
interval limits in subgroups (random effects model)  if

statistic.random.w, pval.random.w 
Statistics and pvalues
for test of treatment effect in subgroups (random effects model)
 if 
w.fixed.w, w.random.w 
Weight of subgroups (in fixed and
random effects model)  if 
df.hakn.w 
Degrees of freedom for test of treatment effect
for HartungKnapp method in subgroups  if 
n.harmonic.mean.w 
Harmonic mean of number of observations in
subgroups (for back transformation of FreemanTukey Double
arcsine transformation)  if 
n.e.w 
Number of observations in experimental group in
subgroups  if 
n.c.w 
Number of observations in control group in subgroups 
if 
k.w 
Number of studies combined within
subgroups  if 
k.all.w 
Number of all studies in subgroups  if 
Q.w.fixed 
Overall within subgroups heterogeneity statistic Q
(based on fixed effect model)  if 
Q.w.random 
Overall within subgroups heterogeneity statistic
Q (based on random effects model)  if 
df.Q.w 
Degrees of freedom for test of overall within
subgroups heterogeneity  if 
pval.Q.w.fixed 
Pvalue of within subgroups heterogeneity
statistic Q (based on fixed effect model)  if 
pval.Q.w.random 
Pvalue of within subgroups heterogeneity
statistic Q (based on random effects model)  if 
Q.b.fixed 
Overall between subgroups heterogeneity statistic
Q (based on fixed effect model)  if 
Q.b.random 
Overall between subgroups heterogeneity statistic
Q (based on random effects model)  if 
df.Q.b 
Degrees of freedom for test of overall between
subgroups heterogeneity  if 
pval.Q.b.fixed 
Pvalue of between subgroups heterogeneity
statistic Q (based on fixed effect model)  if 
pval.Q.b.random 
Pvalue of between
subgroups heterogeneity statistic Q (based on random effects
model)  if 
tau.w 
Squareroot of betweenstudy variance within subgroups
 if 
H.w 
Heterogeneity statistic H within subgroups  if

lower.H.w, upper.H.w 
Lower and upper confidence limit for
heterogeneity statistic H within subgroups  if 
I2.w 
Heterogeneity statistic I^2 within subgroups  if

lower.I2.w, upper.I2.w 
Lower and upper confidence limit for
heterogeneity statistic I^2 within subgroups  if 
keepdata 
As defined above. 
data 
Original data (set) used in function call (if

subset 
Information on subset of original data used in
metaanalysis (if 
call 
Function call. 
version 
Version of R package meta used to create object. 
R function rma.uni
from R package
metafor (Viechtbauer 2010) is called internally to estimate
the betweenstudy variance τ^2.
Guido Schwarzer sc@imbi.unifreiburg.de
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update.meta
, metabin
,
metacont
, print.meta
,
settings.meta
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84  data(Fleiss1993bin)
m1 < metabin(d.asp, n.asp, d.plac, n.plac, study,
data = Fleiss1993bin, sm = "RR", method = "I")
m1
# Identical results using the generic inverse variance method with
# log risk ratio and its standard error:
# Note, argument 'n.e' in metagen() is used to provide the total
# sample size which is calculated from the group sample sizes n.e
# and n.c in metaanalysis m1.
m1.gen < metagen(TE, seTE, studlab, n.e = n.e + n.c, data = m1, sm = "RR")
m1.gen
forest(m1.gen, leftcols = c("studlab", "n.e", "TE", "seTE"))
# Metaanalysis with prespecified betweenstudy variance
#
summary(metagen(m1$TE, m1$seTE, sm = "RR", tau.preset = sqrt(0.1)))
# Metaanalysis of survival data:
#
logHR < log(c(0.95, 1.5))
selogHR < c(0.25, 0.35)
metagen(logHR, selogHR, sm = "HR")
# PauleMandel method to estimate betweenstudy variance for data
# from Paule & Mandel (1982)
#
average < c(27.044, 26.022, 26.340, 26.787, 26.796)
variance < c(0.003, 0.076, 0.464, 0.003, 0.014)
#
summary(metagen(average, sqrt(variance), sm = "MD", method.tau = "PM"))
# Conduct metaanalysis using hazard ratios and 95% confidence intervals
#
# Data from Steurer et al. (2006), Analysis 1.1 Overall survival
# https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD004270.pub2/abstract
#
study < c("FCG on CLL 1996", "Leporrier 2001", "Rai 2000", "Robak 2000")
HR < c(0.55, 0.92, 0.79, 1.18)
lower.HR < c(0.28, 0.79, 0.59, 0.64)
upper.HR < c(1.09, 1.08, 1.05, 2.17)
#
# Input must be log hazard ratios, not hazard ratios
#
metagen(log(HR), lower = log(lower.HR), upper = log(upper.HR),
studlab = study, sm = "HR")
# Exclude MRC1 and MRC2 studies from metaanalysis, however,
# show them in printouts and forest plots
#
metabin(d.asp, n.asp, d.plac, n.plac, study,
data = Fleiss1993bin, sm = "RR", method = "I",
exclude = study %in% c("MRC1", "MRC2"))
#
# Exclude MRC1 and MRC2 studies completely from metaanalysis
#
metabin(d.asp, n.asp, d.plac, n.plac, study,
data = Fleiss1993bin, sm = "RR", method = "I",
subset = !(study %in% c("MRC1", "MRC2")))
# Exclude studies with total sample size above 1500
#
metabin(d.asp, n.asp, d.plac, n.plac, study,
data = Fleiss1993bin, sm = "RR", method = "I",
exclude = (n.asp + n.plac) > 1500)
# Exclude studies containing "MRC" in study name
#
metabin(d.asp, n.asp, d.plac, n.plac, study,
data = Fleiss1993bin, sm = "RR", method = "I",
exclude = grep("MRC", study))
# Use both arguments 'subset' and 'exclude'
#
metabin(d.asp, n.asp, d.plac, n.plac, study,
data = Fleiss1993bin, sm = "RR", method = "I",
subset = (n.asp + n.plac) > 1500,
exclude = grep("MRC", study))

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