metaprop(event, n, studlab,
data=NULL, subset=NULL,
sm=.settings$smprop,
incr=.settings$incr, allincr=.settings$allincr,
addincr=.settings$addincr,
level=.settings$level, level.comb=.settings$level.comb,
comb.fixed=.settings$comb.fixed, comb.random=.settings$comb.random,
hakn=.settings$hakn,
method.tau=.settings$method.tau, tau.preset=NULL, TE.tau=NULL,
tau.common=.settings$tau.common,
prediction=.settings$prediction, level.predict=.settings$level.predict,
method.bias=.settings$method.bias,
title=.settings$title, complab=.settings$complab, outclab="",
byvar, bylab, print.byvar=.settings$print.byvar,
keepdata=.settings$keepdata,
warn=.settings$warn)"PFT", "PAS", "PRAW", "PLN", or
"PLOGIT") is to be used for pooling of studies, see Details.incr is added to each
cell frequency of all studies if at least one study has a zero cell
count. If FALSE (default), incr is added only to each cell frequency of
studies with a zero cell count.incr is added to each cell
frequency of all studies irrespective of zero cell counts."DL", "PM", "REML", "ML", "HS",
"SJ", "HE", o"rank", "linreg", or "mm", can
be abbreviated.event.e).incr
to studies with zero cell frequencies should result in a warning.c("metaprop", "meta") with corresponding
print, summary, plot function. The object is a
list containing the following components:"proportion""Inverse"hakn=TRUE).keepdata=TRUE).keepdata=TRUE).method.tau="DL").
The following transformations of proportions are implemented to
calculate an overall proportion:
sm="PFT": Freeman-Tukey Double arcsine transformationsm="PAS": Arcsine transformationsm="PRAW": Raw, i.e. untransformed, proportionssm="PLN": Log transformationsm="PLOGIT": Logit transformation In older versions of the R package meta (< 1.5.0), only the
Freeman-Tukey Double arcsine transformation and the arcsine
transformation were implemented and an argument freeman.tukey
could be used to distinguish between these two methods. Argument
freeman.tukey has been removed from R package meta with
version 2.4-0.
If the summary measure is equal to "PRAW", "PLN", or "PLOGIT", a
continuity correction is applied if any studies has a zero cell
count. By default, 0.5 is added to all cell frequencies of studies
with a zero cell count (argument incr).
Note, exact binomial confidence intervals will be calculated for
individual study results, e.g. in R function
summary.meta.
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
argument 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.
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 metaprop object by only specifying
arguments which should be changed.
For the random effects, the method by Hartung and Knapp (2003) is
used to adjust test statistics and confidence intervals if argument
hakn=TRUE.
The iterative Paule-Mandel method (1982) to estimate the
between-study variance is used if argument
method.tau="PM". Internally, R function paulemandel is
called which is based on R function mpaule.default from R package
metRology from S.L.R. Ellison
If R package metafor (Viechtbauer 2010) is installed, the following
methods to estimate the between-study variance $\tau^2$
(argument method.tau) are also available:
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 these methods to estimate between-study variance.Edward JM et al. (2006), Adherence to antiretroviral therapy in sub-saharan Africa and North America - a meta-analysis. Journal of the American Medical Association, 296, 679--690.
Freeman MF & Tukey JW (1950), Transformations related to the angular and the square root. Annals of Mathematical Statistics, 21, 607--611. 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 .
Miller JJ (1978), The inverse of the Freeman-Tukey double arcsine transformation. The American Statistician, 32, 138.
Paule RC & Mandel J (1982), Consensus values and weighting factors. Journal of Research of the National Bureau of Standards, 87, 377--385.
Pettigrew HM, Gart JJ, Thomas DG (1986), The bias and higher cumulants of the logarithm of a binomial variate. Biometrika, 73, 425--435.
Viechtbauer W (2010), Conducting Meta-Analyses in R with the Metafor Package. Journal of Statistical Software, 36, 1--48.
update.meta, metacont, metagen, print.metametaprop(4:1, c(10, 20, 30, 40))
metaprop(4:1, c(10, 20, 30, 40), sm="PAS")
metaprop(4:1, c(10, 20, 30, 40), sm="PRAW")
metaprop(4:1, c(10, 20, 30, 40), sm="PLN")
metaprop(4:1, c(10, 20, 30, 40), sm="PFT")
forest(metaprop(4:1, c(10, 20, 30, 40)))
m1 <- metaprop(c(0, 0, 10, 10), rep(100, 4))
m2 <- metaprop(c(0, 0, 10, 10), rep(100, 4), incr=0.1)
summary(m1)
summary(m2)
forest(m1)
forest(m2)
## Example from Miller (1978):
death <- c(3, 6, 10, 1)
animals <- c(11, 17, 21, 6)
##
m3 <- metaprop(death, animals, sm="PFT")
forest(m3)Run the code above in your browser using DataLab