funnel {meta} | R Documentation |
Draw a funnel or radial plot to assess funnel plot asymmetry in the active graphics window.
A contour-enhanced funnel plot can be produced for assessing causes of funnel plot asymmetry.
funnel(x, y, ...)
x |
An object of class meta , or estimated treatment
effect in individual studies. |
y |
Standard error of estimated treatment effect (mandatory if
x not of class meta ). |
... |
Graphical parameters as in par may also be
passed as arguments. |
For simple funnel plots, funnel.default
will be used. For an
object of class meta
the function funnel.meta
will be
used instead.
A funnel plot or radial plot, also called Galbraith plot, is drawn in
the active graphics window. If 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).
For yaxis!="size"
, contour-enhanced funnel plots can be
produced (Peters et al., 2008) by specifying the contour levels
(argument contour.levels
). By default (argument
col.contour
missing), suitable gray levels will be used to
distinguish the contours. Different colours can be chosen by argument
col.contour
.
Guido Schwarzer sc@imbi.uni-freiburg.de, Petra Graham pgraham@efs.mq.edu.au
Galbraith RF (1988a), Graphical display of estimates having differing standard errors. Technometrics, 30, 271–281.
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.
Peters JL, Sutton AJ, Jones DR, Abrams KR, Rushton L (2008), Contour-enhanced meta-analysis funnel plots help distinguish publication bias from other causes of asymmetry. Journal of Clinical Epidemiology, 61, 991–996.
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
, funnel.default
, funnel.meta
data(Olkin95) meta1 <- metabin(event.e, n.e, event.c, n.c, data=Olkin95, subset=c(41,47,51,59), studlab=paste(author, year), sm="RR", meth="I") oldpar <- par(mfrow=c(2, 2)) ## ## Funnel plots ## funnel(meta1) ## ## Same result as code above: ## funnel(meta1$TE, meta1$seTE, sm="RR") ## ## Funnel plot with confidence intervals, ## fixed effect estimate and contours ## cc <- funnel(meta1, comb.fixed=TRUE, level=0.95, contour=c(0.9, 0.95, 0.99))$col.contour legend(0.05, 0.05, c("0.1 > p > 0.05", "0.05 > p > 0.01", "< 0.01"), fill=cc) ## ## Contour-enhanced funnel plot with user-chosen colours ## funnel(meta1, comb.fixed=TRUE, level=0.95, contour=c(0.9, 0.95, 0.99), col.contour=c("darkgreen", "green", "lightgreen"), lwd=2, cex=2, pch=16, studlab=TRUE, cex.studlab=1.25) legend(0.05, 0.05, c("0.1 > p > 0.05", "0.05 > p > 0.01", "< 0.01"), fill=c("darkgreen", "green", "lightgreen")) par(oldpar)