meta (version 3.0-1)

funnel: Generic function to produce a funnel or radial plot.

Description

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.

Usage

funnel(x, ...)
radial(x, ...)

Arguments

x

An object of class meta, or estimated treatment effect in individual studies.

...

Additional arguments as in par.

Details

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.

References

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.

See Also

metabias, funnel.default, funnel.meta

Examples

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", method="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)