Description Usage Arguments Details Value Author(s) See Also Examples
This function can be used to merge pooled results of two metaanalyses into a single metaanalysis object. This is, for example, useful to produce a forest plot of a randomeffects metaanalysis with and without using the HartungKnapp method.
1 2 3 4 5 6 7 8 9 10 11 12 13  metamerge(
meta1,
meta2,
pooled1,
pooled2,
text.pooled1,
text.pooled2,
text.w.pooled1,
text.w.pooled2,
detail.tau1,
detail.tau2,
backtransf
)

meta1 
First metaanalysis object (of class 
meta2 
Second metaanalysis object (see Details). 
pooled1 
A character string indicating whether results of
fixed effect or random effects model should be considered for
first metaanalysis. Either 
pooled2 
A character string indicating whether results of
fixed effect or random effects model should be considered for
second metaanalysis. Either 
text.pooled1 
A character string used in printouts and forest plot to label the estimate from the first metaanalysis. 
text.pooled2 
A character string used in printouts and forest plot to label the estimate from the second metaanalysis. 
text.w.pooled1 
A character string used to label weights of the first metaanalysis. 
text.w.pooled2 
A character string used to label weights of the second metaanalysis. 
detail.tau1 
A character string used to label estimate of betweenstudy variance of the first metaanalysis. 
detail.tau2 
A character string used to label estimate of betweenstudy variance of the second metaanalysis. 
backtransf 
A logical indicating whether results should be
back transformed in printouts and plots. If

In R package meta, objects of class "meta"
contain
results of both a fixed effect and random effects
metaanalysis. This function enables the user to keep the results
of one of these models and to add results from a second
metaanalysis or a sensitivity analysis.
Applications of this function include printing and plotting results of the fixed effect or random effects metaanalysis and the
HartungKnapp method (see argument hakn
in
metagen
),
trimandfill method (trimfill
),
limit metaanalyis (limitmeta
from R
package metasens),
Copas selection model (copas
from R
package metasens),
robust variance metaanalysis model
(robu
from R package robumeta).
The first argument must be an object created by a metaanalysis
function, e.g., metagen
or metabin
. The
second metaanalysis could also be an object created with
trimfill
, limitmeta
,
copas
, or robu
.
The created metaanalysis object only contains the study results
from the first metaanalysis which are shown in printouts and
forest plots. This only makes a difference for metaanalysis
methods where individual study results differ, e.g.,
MantelHaenszel and Peto method for binary outcomes (see
metabin
).
R function metabind
can be used to print and plot the
results of more than two metaanalyses, however, without showing
individual study results.
An object of class "meta"
and "metamerge"
with
corresponding print
, summary
, and forest
functions. The following list elements have a different meaning:
TE, seTE, studlab 
Treatment estimate, standard error, and study labels (first metaanalyis). 
lower, upper 
Lower and upper confidence interval limits for individual studies (first metaanalysis). 
statistic, pval 
Statistic and pvalue for test of treatment effect for individual studies (first metaanalysis. 
w.fixed 
Weight of individual studies (first metaanalysis). 
w.random 
Weight of individual studies (second metaanalysis). 
TE.fixed, seTE.fixed 
Estimated overall treatment effect and standard error (first metaanalysis). 
lower.fixed, upper.fixed 
Lower and upper confidence interval limits (first metaanalysis). 
statistic.fixed, pval.fixed 
Statistic and pvalue for test of overall treatment effect (first metaanalysis). 
TE.random, seTE.random 
Estimated overall treatment effect and standard error (second metaanalysis). 
lower.random, upper.random 
Lower and upper confidence interval limits (second metaanalysis). 
statistic.random, pval.random 
Statistic and pvalue for test of overall treatment effect (second metaanalysis). 
lower.predict, upper.predict 
Lower and upper limits of prediction interval (related to first metaanalysis). 
k 
Number of studies combined in first metaanalysis. 
Q 
Heterogeneity statistic (first metaanalysis). 
df.Q 
Degrees of freedom for heterogeneity statistic (first metaanalysis). 
pval.Q 
Pvalue of heterogeneity test (first metaanalysis). 
tau2 
Betweenstudy variance(s) τ^2 (first and second metaanalysis). 
lower.tau2, upper.tau2 
Lower and upper limit of confidence interval(s) for τ^2 (first and second metaanalysis). 
tau 
Squareroot of betweenstudy variance(s) τ (first and second metaanalysis). 
lower.tau, upper.tau 
Lower and upper limit of confidence interval(s) for τ (first and second metaanalysis). 
text.fixed 
Label for the first metaanalysis. 
text.random 
Label for the second metaanalysis. 
See metagen
for information on other list
elements.
Guido Schwarzer sc@imbi.unifreiburg.de
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  data(Fleiss1993cont)
#
m1 < metacont(n.psyc, mean.psyc, sd.psyc, n.cont, mean.cont, sd.cont,
data = Fleiss1993cont, sm = "MD",
comb.fixed = FALSE,
text.random = "Classic random effects",
text.w.random = "RE")
#
# Use HartungKnapp method
#
m2 < update(m1, hakn = TRUE,
text.random = "HartungKnapp method",
text.w.random = "HK")
#
# Merge results of the two metaanalyses
#
m12 < metamerge(m1, m2)
m12
forest(m12, rightcols = c("effect", "ci", "w.fixed"))
# Show results for DerSimonianLaird and REML estimate of
# betweenstudy variance
#
m3 < update(m1,
text.random = "Random effects moded (DL)",
text.w.random = "DL")
m4 < update(m1, method.tau = "REML",
text.random = "Random effects moded (REML)",
text.w.random = "REML")
#
m34 < metamerge(m3, m4)
m34
data(Fleiss1993bin)
#
# MantelHaenszel method
#
m5 < metabin(d.asp, n.asp, d.plac, n.plac, data = Fleiss1993bin,
studlab = paste(study, year),
sm = "OR", comb.random = FALSE,
text.fixed = "MH method", text.w.fixed = "MH")
#
# Peto method
#
m6 < update(m5, method = "Peto", text.fixed = "Peto method",
text.w.fixed = "Peto")
#
# Merge results (show individual results for MH method)
#
m56 < metamerge(m5, m6)
m56
forest(m56, digits = 4)
#
# Merge results (show individual results for Peto method)
#
m65 < metamerge(m6, m5)
m65

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