# NOT RUN {
### copy data into 'dat'
dat <- dat.hart1999
### calculate log incidence rate ratios and corresponding sampling variances
dat <- escalc(measure="IRR", x1i=x1i, x2i=x2i, t1i=t1i, t2i=t2i, data=dat)
dat
### meta-analysis of log incidence rate ratios using a random-effects model
res <- rma(yi, vi, data=dat)
res
### average incidence rate ratio with 95% CI
predict(res, transf=exp)
### forest plot with extra annotations
par(mar=c(5,4,1,2))
forest(res, xlim=c(-11, 5), at=log(c(.05, .25, 1, 4)), atransf=exp,
slab=paste0(dat$study, " (", dat$year, ")"),
ilab=cbind(paste(dat$x1i, "/", dat$t1i, sep=" "),
paste(dat$x2i, "/", dat$t2i, sep=" ")),
ilab.xpos=c(-6.5,-4), cex=.85, header="Study (Year)")
op <- par(cex=.85, font=2)
text(c(-6.5,-4), 8.5, c("Warfarin", "Control"))
text(c(-6.5,-4), 7.5, c("Strokes / PT", "Strokes / PT"))
segments(x0=-8, y0=8, x1=-2.75, y1=8)
par(op)
### meta-analysis of incidence rate differences using a random-effects model
res <- rma(measure="IRD", x1i=x1i, x2i=x2i, t1i=t1i, t2i=t2i, data=dat)
res
# }
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