Meta-regression
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).
## S3 method for class 'meta' metareg( x, formula, method.tau = x$method.tau, hakn = x$method.random.ci == "HK", level.ma = x$level.ma, intercept = TRUE, ... ) metareg(x, ...) ## Default S3 method: metareg(x, ...)
x: An object of class meta.formula: Either a character string or a formula object.method.tau: A character string indicating which method is used to estimate the between-study variance tau-squared. Either "FE", "DL", "REML", "ML", "HS", "SJ", "HE", or "EB", can be abbreviated.hakn: A logical indicating whether the method by Hartung and Knapp should be used to adjust test statistics and confidence intervals.level.ma: The level used to calculate confidence intervals for parameter estimates in the meta-regression model.intercept: A logical indicating whether an intercept should be included in the meta-regression model....: Additional arguments passed to R function rma.uni.An object of class c("metareg", "rma.uni", "rma"). Please look at the help page of R function rma.uni
for more details on the output from this function.
In addition, a list .meta is added to the output containing the following components: - x, formula, method.tau, hakn, level.ma, intercept: As defined above.
dots: Information provided in argument '...'.
call: Function call.
version: Version of R package meta used to create object.
version.metafor: Version of R package metafor used to create object.
This R function is a wrapper function for R function rma.uni in the R package metafor
(Viechtbauer 2010).
Note, results are not back-transformed in printouts of meta-analyses using summary measures with transformations, e.g., log risk ratios are printed instead of the risk ratio if argument sm = "RR" and logit transformed proportions are printed if argument sm = "PLOGIT".
Argument '...' can be used to pass additional arguments to R function rma.uni. For example, argument control to provide a list of control values for the iterative estimation algorithm. See help page of R function rma.uni for more details.
data(Fleiss1993cont) # Add some (fictitious) grouping variables: Fleiss1993cont$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") ## Not run: # Error due to wrong ordering of arguments (order has changed in # R package meta, version 3.0-0) # try(metareg(~ region, m1)) try(metareg(~ region, data = m1)) # Warning as no information on covariate is available # metareg(m1) ## End(Not run) # Do meta-regression for covariate region # mu2 <- update(m1, subgroup = region, tau.common = TRUE, common = FALSE) 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(m1, region) # Different result for # - tau-squared # - test of heterogeneity # - test for subgroup differences # (as argument 'tau.common' is - by default - FALSE) # mu1 <- update(m1, subgroup = region) mu1 # Generate bubble plot # bubble(metareg(mu2)) # Do meta-regression with two covariates # metareg(mu1, region + age) # Do same meta-regressions using formula notation # metareg(m1, ~ 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, method.random.ci = "HK") mu3 metareg(mu3, intercept = FALSE)
Viechtbauer W (2010): Conducting Meta-Analyses in R with the Metafor Package. Journal of Statistical Software, 36 , 1--48
bubble, summary.meta, metagen
Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de