funnel(x, y, xlim=NULL, ylim=NULL, xlab=NULL, ylab=NULL,
comb.fixed=FALSE, axes=TRUE,
pch=1, text=NULL, cex=1, col=NULL,
log="", yaxis="se", sm=NULL,
level=NULL, ...)radial(x, y, xlim=NULL, ylim=NULL,
xlab="Inverse of standard error",
ylab="Standardised treatment effect (z-score)",
comb.fixed=TRUE, axes=TRUE,
pch=1, text=NULL, cex=1, col=NULL,
level=NULL, ...)
meta
, or estimated treatment
effect in individual studies.x
not of class meta
)."x"
if the x axis
is to be logarithmic, "y"
if the y axis is to be logarithmic
and "xy"
or "yx"
if both axes are to be logarithmic
(applies only to function "se"
, "invvar"
, "invse"
, or "size"
(applies only to function funnel
)."RD"
, "RR"
, "OR"
, "AS"
, "MD"
,
"SMD"
(applies only to function funnel
).yaxis="size"
.par
may also be
passed as arguments.comb.fixed
is TRUE, the pooled
estimate of the fixed effect model is plotted. If level
is not
NULL, the corresponding confidence limits are drawn. In the funnel plot, if yaxis
is "se"
, the standard error
of the treatment estimates is plotted on the y axis which is likely to
be the best choice (Sterne & Egger, 2001). Other possible choices for
yaxis
are "invvar"
(inverse of the variance),
"invse"
(inverse of the standard error), and "size"
(study size).
Galbraith RF (1988b), A note on graphical presentation of estimated odds ratios from several clinical trials. Statistics in Medicine, 7, 889--894. Light RJ & Pillemer DB (1984), Summing Up. The Science of Reviewing Research. Cambridge: Harvard University Press.
Sterne JAC & Egger M (2001), Funnel plots for detecting bias in meta-analysis: Guidelines on choice of axis. Journal of Clinical Epidemiology, 54, 1046--1055.
metabias
, metabin
, metagen
data(Olkin95)
meta1 <- metabin(event.e, n.e, event.c, n.c,
data=Olkin95, subset=c(41,47,51,59),
sm="RR", meth="I")
oldpar <- par(mfrow=c(2, 2))
funnel(meta1)
##
## Same result as code above:
##
funnel(meta1$TE, meta1$seTE, sm="RR")
funnel(meta1, comb.fixed=TRUE, level=0.95)
radial(meta1, level=0.95)
par(oldpar)
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