meta (version 4.4-0)

metareg: 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"

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.

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.

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.

References

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

See Also

bubble, summary.meta, metagen

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)

# 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)

# 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)