metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, studlab,
data=NULL, subset=NULL, sm="MD",
level = 0.95, level.comb = level,
comb.fixed=TRUE, comb.random=TRUE,
hakn=FALSE,
method.tau="DL", tau.preset=NULL, TE.tau=NULL,
tau.common=FALSE,
prediction=FALSE, level.predict=level,
method.bias="linreg",
title="", complab="", outclab="",
label.e="Experimental", label.c="Control",
label.left="", label.right="",
byvar, bylab, print.byvar=TRUE,
keepdata=TRUE, warn=TRUE)"DL", "REML", "ML", "HS", "SJ",
"HE", or "EB""rank", "linreg", or "mm", can
be abbreviated."MD" or "SMD") is to be used for pooling of
studies.n.e).c("metacont", "meta") with corresponding
print, summary, plot function. The object is a
list containing the following components:"Inverse".hakn=TRUE).keepdata=TRUE).keepdata=TRUE).method.tau). The mean difference is
used as measure of treatment effect if sm="MD" -- which
correspond to sm="WMD" in older versions (<0.9) of="" the="" meta="" package.="" for="" summary="" measure="" Internally, both fixed effect and random effects models are calculated
regardless of values choosen for arguments comb.fixed and
comb.random. Accordingly, the estimate for the random effects
model can be extracted from component TE.random of an object
of class "meta" even if comb.random=FALSE. However, all
functions in R package meta will adequately consider the values
for comb.fixed and comb.random. E.g. function
print.meta will not print results for the random effects
model if comb.random=FALSE.
The function metagen is called internally to calculate
individual and overall treatment estimates and standard errors.
If R package metafor (Viechtbauer 2010) is installed, the following statistical methods are also available.
For the random effects model (argument comb.random=TRUE), the
method by Hartung and Knapp (Hartung, Knapp 2001; Knapp, Hartung
2003) is used to adjust test statistics and confidence intervals if
argument hakn=TRUE (internally R function rma.uni of R
package metafor is called).
Several methods are available to estimate the between-study variance
$\tau^2$ (argument method.tau):
method.tau="DL") (default)method.tau="REML")method.tau="ML")method.tau="HS")method.tau="SJ")method.tau="HE")method.tau="EB").rma.uni of R package metafor is called internally. See help
page of R function rma.uni for more details on the various
methods to estimate between-study variance $\tau^2$. A prediction interval for treatment effect of a new study is
calculated (Higgins et al., 2009) if arguments prediction and
comb.random are TRUE.
R function update.meta can be used to redo the
meta-analysis of an existing metacont object by only specifying
arguments which should be changed.
Hartung J & Knapp G (2001), On tests of the overall treatment effect in meta-analysis with normally distributed responses. Statistics in Medicine, 20, 1771--82. doi: 10.1002/sim.791 . Higgins JPT, Thompson SG, Spiegelhalter DJ (2009), A re-evaluation of random-effects meta-analysis. Journal of the Royal Statistical Society: Series A, 172, 137-159.
Knapp G & Hartung J (2003), Improved Tests for a Random Effects Meta-regression with a Single Covariate. Statistics in Medicine, 22, 2693-710, doi: 10.1002/sim.1482 .
Viechtbauer W (2010), Conducting Meta-Analyses in R with the Metafor Package. Journal of Statistical Software, 36, 1--48.
update.meta, metabin, metagendata(Fleiss93cont)
meta1 <- metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, data=Fleiss93cont, sm="SMD")
meta1
forest(meta1)
meta2 <- metacont(Fleiss93cont$n.e, Fleiss93cont$mean.e,
Fleiss93cont$sd.e,
Fleiss93cont$n.c, Fleiss93cont$mean.c,
Fleiss93cont$sd.c,
sm="SMD")
meta2Run the code above in your browser using DataLab