metabind | R Documentation |
This function can be used to combine meta-analysis objects and is, for example, useful to summarize results of various meta-analysis methods or to generate a forest plot with results of several subgroup analyses.
metabind(..., name = NULL, pooled = NULL, backtransf = NULL, outclab = NULL)
... |
Any number of meta-analysis objects or a single list with meta-analyses. |
name |
An optional character vector providing descriptive names for the meta-analysis objects. |
pooled |
A character string or vector indicating whether
results of a common effect or random effects model should be
considered. Either |
backtransf |
A logical indicating whether results should be
back transformed in printouts and plots. If
|
outclab |
Outcome label for all meta-analyis objects. |
This function can be used to combine any number of meta-analysis objects which is useful, for example, to summarize results of various meta-analysis methods or to generate a forest plot with results of several subgroup analyses (see Examples).
Individual study results are not retained with metabind
as
the function allows to combine meta-analyses from different data
sets (e.g., with randomized or observational studies). This is
possible using R function metamerge
which can be used
to combine results of two meta-analyses of the same dataset.
An object of class c("metabind", "meta")
with corresponding
generic functions (see meta-object
).
Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de
metagen
, forest.metabind
,
metamerge
data(Fleiss1993cont)
# Add some (fictitious) grouping variables:
#
Fleiss1993cont$age <- c(55, 65, 55, 65, 55)
Fleiss1993cont$region <- c("Europe", "Europe", "Asia", "Asia", "Europe")
m1 <- metacont(n.psyc, mean.psyc, sd.psyc, n.cont, mean.cont, sd.cont,
data = Fleiss1993cont, sm = "SMD")
# Conduct two subgroup analyses
#
mu1 <- update(m1, subgroup = age, subgroup.name = "Age group")
mu2 <- update(m1, subgroup = region, subgroup.name = "Region")
# Combine subgroup meta-analyses and show forest plot with subgroup
# results
#
mb1 <- metabind(mu1, mu2)
mb1
forest(mb1)
# Use various estimation methods for between-study heterogeneity
# variance
#
m1.pm <- update(m1, method.tau = "PM")
m1.dl <- update(m1, method.tau = "DL")
m1.ml <- update(m1, method.tau = "ML")
m1.hs <- update(m1, method.tau = "HS")
m1.sj <- update(m1, method.tau = "SJ")
m1.he <- update(m1, method.tau = "HE")
m1.eb <- update(m1, method.tau = "EB")
# Combine meta-analyses and show results
#
taus <- c("Restricted maximum-likelihood estimator",
"Paule-Mandel estimator",
"DerSimonian-Laird estimator",
"Maximum-likelihood estimator",
"Hunter-Schmidt estimator",
"Sidik-Jonkman estimator",
"Hedges estimator",
"Empirical Bayes estimator")
#
m1.taus <- metabind(m1, m1.pm, m1.dl, m1.ml, m1.hs, m1.sj, m1.he, m1.eb,
name = taus, pooled = "random")
m1.taus
forest(m1.taus, print.I2 = FALSE, print.pval.Q = FALSE)
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