metamerge {meta} | R Documentation |
This function can be used to merge pooled results of two meta-analyses into a single meta-analysis object. This is, for example, useful to produce a forest plot of a random-effects meta-analysis with different estimates of the between-study variance τ^2.
metamerge( meta1, meta2, pooled1, pooled2, text.pooled1, text.pooled2, text.w.pooled1, text.w.pooled2, label1, label2, backtransf )
meta1 |
First meta-analysis object (see Details). |
meta2 |
Second meta-analysis object (see Details). |
pooled1 |
A character string indicating whether results of
common effect or random effects model should be considered for
first meta-analysis. Either |
pooled2 |
A character string indicating whether results of
common effect or random effects model should be considered for
second meta-analysis. Either |
text.pooled1 |
A character string used in printouts and forest plot to label the results from the first meta-analysis. |
text.pooled2 |
A character string used in printouts and forest plot to label the results from the second meta-analysis. |
text.w.pooled1 |
A character string used to label weights of the first meta-analysis. |
text.w.pooled2 |
A character string used to label weights of the second meta-analysis. |
label1 |
A character string used to label estimate of between-study variance and heterogeneity statistics of the first meta-analysis. |
label2 |
A character string used to label estimate of between-study variance and heterogeneity statistics of the second meta-analysis. |
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 common effect and random effects
meta-analysis. This function enables the user to keep the results
of one of these models and to add results from a second
meta-analysis or a sensitivity analysis.
Applications of this function include printing and plotting results of the common effect or random effects meta-analysis and the
trim-and-fill method (trimfill
),
limit meta-analyis (limitmeta
from R
package metasens),
Copas selection model (copas
from R
package metasens),
robust variance meta-analysis model
(robu
from R package robumeta).
The first argument must be an object created by a meta-analysis
function, e.g., metagen
or metabin
. It
is also possible to provide an object created with
limitmeta
or
copas
. In this case, arguments meta2
and pooled2
will be ignored.
The second meta-analysis could be an object created by a
meta-analysis function or with trimfill
,
limitmeta
, copas
,
or robu
.
The created meta-analysis object only contains the study results
from the first meta-analysis which are shown in printouts and
forest plots. This only makes a difference for meta-analysis
methods where individual study results differ, e.g.,
Mantel-Haenszel and Peto method for binary outcomes (see
metabin
).
R function metabind
can be used to print and plot the
results of more than two meta-analyses, however, without showing
individual study results.
An object of class "meta"
and "metamerge"
with
corresponding generic functions (see meta-object
).
The following list elements have a different meaning:
TE, seTE, studlab |
Treatment estimate, standard error, and study labels (first meta-analyis). |
lower, upper |
Lower and upper confidence interval limits for individual studies (first meta-analysis). |
statistic, pval |
Statistic and p-value for test of treatment effect for individual studies (first meta-analysis. |
w.common |
Weights of first common effect meta-analysis. |
w.random |
Weights of first random effects meta-analysis. |
k |
Number of studies combined in first meta-analysis. |
Furthermore, meta-analysis results of common effect or random
effects model are taken from first meta-analysis if only random
effects or common effects models are selected from both
meta-analyses (arguments pooled1
and pooled2
).
Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de
data(Fleiss1993cont) # m1 <- metacont(n.psyc, mean.psyc, sd.psyc, n.cont, mean.cont, sd.cont, data = Fleiss1993cont, sm = "MD", text.random = "Random effects model (REML)", text.w.random = "DL") # # Use DerSimonian-Laird estimator of tau2 # m2 <- update(m1, method.tau = "DL", common = FALSE, text.random = "Random effects model (DL)", text.w.random = "DL") # # Merge results of the two meta-analyses # m12 <- metamerge(m1, m2) m12 forest(m12, rightcols = c("effect", "ci", "w.common")) # Show in addition the results for the Paule-Mandel estimate of # between-study variance # m3 <- update(m1, method.tau = "PM", text.random = "Random effects moded (PM)", text.w.random = "PM") # m123 <- metamerge(m12, m3, pooled2 = "random") m123 data(Fleiss1993bin) # # Mantel-Haenszel method # m4 <- metabin(d.asp, n.asp, d.plac, n.plac, data = Fleiss1993bin, studlab = paste(study, year), sm = "OR", random = FALSE, text.common = "MH method", text.w.common = "MH") # # Peto method # m5 <- update(m4, method = "Peto", text.common = "Peto method", text.w.common = "Peto") # # Merge results (show individual results for MH method) # m45 <- metamerge(m4, m5) summary(m45) forest(m45, digits = 4) # # Merge results (show individual results for Peto method) # m54 <- metamerge(m5, m4) summary(m54) forest(m54)