meta: metabind – R documentation

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metabind

Combine and summarize meta-analysis objects

Description

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.

Usage

metabind(..., name, pooled, backtransf, outclab)

Arguments

`...`	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 indicating whether results of a fixed effect or random effects model should be considered. Either `"fixed"` or `"random"`, can be abbreviated.
`backtransf`	A logical indicating whether results should be back transformed in printouts and plots. If `backtransf=TRUE` (default), results for `sm="OR"` are printed as odds ratios rather than log odds ratios, for example.
`outclab`	Outcome label for all meta-analyis objects.

Details

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. This is possible using R function metamerge which, however, can only be used to combine results of two meta-analyses.

Value

An object of class c("metabind", "meta") with corresponding print, summary, and forest functions. See metagen for more information on list elements.

Author(s)

Guido Schwarzer sc@imbi.uni-freiburg.de

Examples

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 = "MD")

# Conduct two subgroup analyses
#
mu1 <- update(m1, byvar = age, bylab = "Age group")
mu2 <- update(m1, byvar = region, bylab = "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.reml <- update(m1, method.tau = "REML")
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("DerSimonian-Laird estimator",
          "Paule-Mandel estimator",
          "Restricted maximum-likelihood estimator",
          "Maximum-likelihood estimator",
          "Hunter-Schmidt estimator",
          "Sidik-Jonkman estimator",
          "Hedges estimator",
          "Empirical Bayes estimator")
#
m1.taus <- metabind(m1, m1.pm, m1.reml, 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)