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Last data update: 2014.03.03

R: Meta-regression

metareg

R Documentation

Meta-regression

Description

Meta-regression for objects of class meta. This is a wrapper function for the R function rma.uni in the R package metafor (Viechtbauer 2010).

Usage

metareg(x, formula, method.tau = x$method.tau, hakn = x$hakn,
        level.comb = x$level.comb, intercept = TRUE,...)

Arguments

`x`	An object of class `meta`.
`formula`	Either a character string or a formula object.
`method.tau`	A character string indicating which method is used to estimate the between-study variance tau-squared. Either `"FE"`, `"DL"`, `"REML"`, `"ML"`, `"HS"`, `"SJ"`, `"HE"`, or `"EB"`, can be abbreviated.
`hakn`	A logical indicating whether the method by Hartung and Knapp should be used to adjust test statistics and confidence intervals.
`level.comb`	The level used to calculate confidence intervals for parameter estimates in the meta-regression model.
`intercept`	A logical indicating whether an intercept should be included in the meta-regression model.
`...`	Additional arguments passed to R function `rma.uni`.

Details

This R function is a wrapper function for R function rma.uni in the R package metafor (Viechtbauer 2010), i.e. the function metareg can only be used if the R package 'metafor' is installed.

Argument '...' can be used to pass additional arguments to R function rma.uni. For example, argument control to provide a list of control values for the iterative estimation algorithm. See help page of R function rma.uni for more details.

Value

An object of class c("metareg", "rma.uni","rma"). Please look at the help page of R function rma.uni for more details on the output from this function.

In addition, a list .meta is added to the output containing the following components:

`x, formula, method.tau, hakn, level.comb, intercept`	As definied above.
`dots`	Information provided in argument '...'.
`call`	Function call.
`version`	Version of R package meta used to create object.
`version.metafor`	Version of R package metafor used to create object.

Author(s)

Guido Schwarzer sc@imbi.uni-freiburg.de

References

Viechtbauer W (2010), Conducting Meta-Analyses in R with the Metafor Package. Journal of Statistical Software, 36, 1–48.

Examples

data(Fleiss93cont)

# Add some (fictious) grouping variables:
Fleiss93cont$age <- c(55, 65, 55, 65, 55)
Fleiss93cont$region <- c("Europe", "Europe", "Asia", "Asia", "Europe")

meta1 <- metacont(n.e, mean.e, sd.e,
                  n.c, mean.c, sd.c,
                  data = Fleiss93cont, sm = "MD")


mu1 <- update(meta1, byvar = region)

mu2 <- update(meta1, byvar = region,
              tau.common = TRUE, comb.fixed = FALSE)

## Not run: 
# Warnings due to wrong ordering of arguments (order has changed with
# version 3.0-0 of R package meta)
#
metareg(~ region, meta1)
metareg(~ region, data = meta1)

# Warning as no information on covariate is available
#
metareg(meta1)

## End(Not run)

# Do meta-regression for covariate region
# (see R code to create object mu2)
#
metareg(mu2)

# Same result for
# - tau-squared
# - test of heterogeneity
# - test for subgroup differences
# (as argument 'tau.common' was used to create mu2)
#
mu2
metareg(mu2, intercept = FALSE)
metareg(meta1, region)
#
# Different result for
# - tau-squared
# - test of heterogeneity
# - test for subgroup differences
# (as argument 'tau.common' is - by default - FALSE)
#
mu1

# Generate bubble plot
#
bubble(metareg(mu2))

# Do meta-regression with two covariates
#
metareg(mu1, region + age)

# Do same meta-regressions using 'official' formula notation
#
metareg(meta1, ~ region)
metareg(mu1, ~ region + age)

# Do meta-regression using REML method and print intermediate results
# for iterative estimation algorithm; furthermore print results with
# three digits.
#
metareg(mu1, region, method.tau = "REML",
        control = list(verbose = TRUE), digits = 3)

# Use Hartung-Knapp method
#
mu3 <- update(mu2, hakn = TRUE)
mu3
metareg(mu3, intercept = FALSE)