Metafor: Error with mlab in the forest function while passing CI bounds from df

I am working with vaccine effectiveness (VE) data and have the point estimates along with their confidence intervals (CI) in my df. I am trying to create a forest plot using the metafor package in R in the RevMan style, while passing the CI bounds from my dataset. However, I get the error "mlab" is not a graphical parameter when trying to get the mlab polygon to show up in the plot.

A reproducible example is below.

library(metafor)

dat <- data.frame(author = c("Amore-Coffea", "Deliciozza", "Kahve-Paradiso"),
                  year   = c(2000, 2004, 2002),
                  ve     = c(0.9, 0.85, 0.92),
                  ci_ll    = c(0.7, 0.7, 0.9),
                  ci_ul     = c(0.98, 0.9, 0.95))
                  
dat <- escalc(measure = "RR",              
              yi = log(1 - dat$ve),
              sei = (log(1 - dat$ci_ul) - log(1 - dat$ci_ll)) / (2 * 1.96),
              data = dat)
                  
res <- rma(yi, vi, data=dat, method="DL")
                  
sav <- forest(dat$ve, ci.lb=dat$ci_ll, ci.ub=dat$ci_ul, 
              slab = paste(dat$author, dat$year, sep = ", "),
              transf = function(x) x * 100,
              at = c(0, 25, 50, 75, 100),
              xlim = c(-200, 110), 
              xlab="", 
              efac=c(0,4), 
              textpos=c(-200,-25), 
              lty=c(1,1,0), 
              refline=NA,
              cex=0.78, 
              header=c("Study", "IV, Random, 95% CI"), 
              mlab="")

Is there a way to make mlab work in the forest function or should I be using addpoly? However, with addpoly, then the polygon shows up underneath the effect measure bar.

Solution

You are passing estimates to the forest() function, in which case forest.default() will be called. It does not have an mlab argument (since there won't be a pooled estimate, there is no point in having a label for it). So you should remove mlab="" from your call to forest(). Instead, you should make a bit of extra space at the bottom of the plot with ylim=c(-2,6) and then add the summary estimate with addpoly(res, row=-1, transf = function(x) (1-exp(x)) * 100).