addpoly.rma: Add Polygons to Forest Plots Based on 'rma' Objects

Description

Function to add a polygon to a forest plot showing the summary estimate with correspondong confidence interval based on an object of class "rma".

Usage

## S3 method for class 'rma':
addpoly(x, row=-2, level=x$level, digits=2,
        annotate=TRUE, mlab, transf=FALSE, atransf=FALSE, targs,
        col="black", efac=1, cex, \dots)

Arguments

an object of class "rma".

row

value specifying the row (or more generally, the horizontal position) for plotting the polygon (default is -2).

level

numerical value between 0 and 100 specifying the confidence interval level (the default is to take the value from the object).

digits

integer specifying the number of decimal places to which the annotations should be rounded (default is 2).

annotate

logical specifying whether annotations for the summary estimate should be added to the plot (default is TRUE).

mlab

optional character string giving a label for the summary estimate polygon. If unspecified, the function sets a default label.

transf

optional argument specifying the name of a function that should be used to transform the summary estimate and confidence interval bound. Defaults to FALSE, which means that no transformation is used.

atransf

optional argument specifying the name of a function that should be used to transform the annotations. Defaults to FALSE, which means that no transformation is used.

targs

optional arguments needed by the function specified via transf or atransf.

col

color of the polygon that is drawn (default is "black").

efac

vertical expansion factor for the polygon. The default value of 1 should usually work okay.

cex

optional symbol expansion factor. If unspecified, the function tries to set this to a sensible value.

...

other arguments.

Details

The function can be used to add a polygon to an existing forest plot created with the forest function. The polygon shows the summary estimate based on a fixed- or random-effects model. Using this function, summary estimates based on different types of models can be shown in the same plot. Also, summary estimates based on a subgrouping of the studies can be added to the plot this way. See examples below. The arguments transf, atransf, efac, and cex should always be set equal to the same values used to create the forest plot.

References

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1--48. http://www.jstatsoft.org/v36/i03/.

Examples

Run this code

### load BCG vaccine data
data(dat.bcg)

### meta-analysis of the log relative risks using the Mantel-Haenszel method
res <- rma.mh(ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg,
              slab=paste(author, year, sep=", "), measure="RR")

### forest plot of the observed relative risks with summary estimate
forest(res, atransf=exp, ylim=c(-2.5,16))

### meta-analysis of the log relative risks using a random-effects model
res <- rma(ai=tpos, bi=tneg, ci=cpos, di=cneg,
           data=dat.bcg, measure="RR", method="REML")

### add summary estimate from the random-effects model to forest plot
addpoly(res, atransf=exp)

### forest plot with subgrouping of studies and summaries per subgroup
res <- rma(ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg, measure="RR",
           slab=paste(author, year, sep=", "), method="REML")
forest(res, xlim=c(-16, 6), at=log(c(.05, .25, 1, 4)), atransf=exp,
       ilab=cbind(dat.bcg$tpos, dat.bcg$tneg, dat.bcg$cpos, dat.bcg$cneg),
       ilab.xpos=c(-9.5,-8,-6,-4.5), cex=.75, ylim=c(-1, 27),
       order=order(dat.bcg$alloc), rows=c(3:4,9:15,20:23),
       mlab="RE Model for All Studies")
op <- par(cex=.75, font=4)
text(-16, c(24,16,5), c("Systematic Allocation", "Random Allocation",
                        "Alternate Allocation"), pos=4)
par(font=2)
text(c(-9.5,-8,-6,-4.5), 26, c("TB+", "TB-", "TB+", "TB-"))
text(c(-8.75,-5.25),     27, c("Vaccinated", "Control"))
text(-16,                26, "Author(s) and Year",     pos=4)
text(6,                  26, "Relative Risk [95% CI]", pos=2)
par(op)
res <- rma(ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg, measure="RR",
           subset=(alloc=="systematic"), method="REML")
addpoly(res, row=18.5, cex=.75, atransf=exp, mlab="RE Model for Subgroup")
res <- rma(ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg, measure="RR",
           subset=(alloc=="random"), method="REML")
addpoly(res, row=7.5, cex=.75, atransf=exp, mlab="RE Model for Subgroup")
res <- rma(ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg, measure="RR",
           subset=(alloc=="alternate"), method="REML")
addpoly(res, row=1.5, cex=.75, atransf=exp, mlab="RE Model for Subgroup")