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I am meta-analysing data from ~90 studies. This presents some challenges in how to display the data in an accessible format for publication. I would like to display only the overall effect size estimates of the different meta-analyses and exclude the study-specific estimates. I am able to do this in Stata using the metan package and adding the summaryonly command. Is it possible to suppress the study-level effect sizes in the forest plot outputs using the metafor package (or any other meta-analysis R package)?

I've been using the addpoly command to add the effect size estimates for sub-samples as described in the package documentation, e.g.:

res.a <- rma(n1i = Intervention_n, n2i = Control_n, m1i = intervention_d, m2i = control_d, sd1i = intervention_d_sd, 
         sd2i = control_d_sd, measure="MD", intercept=TRUE, data = Dataset.a, vtype="LS", method="DL", level=95, 
         digits=4, subset = (exclude==0 & child=="No"), slab=paste(Dataset.a$Label, Dataset.a$Year, sep=", "))
addpoly(res.a, row=7.5, cex=.75, font=3, mlab="Random effects model for subgroup")
zx8754
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ChrisP
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  • I would love to do this as well, but for the metaprop command from the meta package. The problem with the current approach is that I would lose all of the other summary information on the forest plot, e.g., the number of studies contributing, number of events, overall n, etc. Would be great to have a fix for that too. – Alexander Sep 30 '16 at 15:45

1 Answers1

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If I understand you correctly, you are conducting several analyses with these ~90 studies (e.g., based on different subsets) and your goal is to show only the summary estimates (as based on these analyses) in a forest plot. Then the easiest approach would be to just collect the estimates and corresponding variances of the various analyses in a vector and then pass that to the forest() function. Let me give a simple example:

### load metafor package
library(metafor)

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

### calculate log relative risks and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)

### fit random-effects models to some subsets
res.r <- rma(yi, vi, data=dat, subset=alloc=="random")
res.s <- rma(yi, vi, data=dat, subset=alloc=="systematic")
res.a <- rma(yi, vi, data=dat, subset=alloc=="alternate")

### collect model estimates and corresponding variances
estimates <- c(coef(res.r), coef(res.s), coef(res.a))
variances <- c(vcov(res.r), vcov(res.s), vcov(res.a))

### create vector with labels
labels <- c("Random Allocation", "Systematic Allocation", "Alternate Allocation")

### forest plot
forest(estimates, variances, slab=labels)

If you don't like that the point sizes differ (by default, they are drawn inversely proportional to the variances), you could use:

forest(estimates, variances, slab=labels, psize=1)

A couple other improvements:

forest(estimates, variances, slab=labels, psize=1, atransf=exp, xlab="Relative Risk (log scale)", at=log(c(.2, .5, 1, 2)))

ADDENDUM

In case you prefer polygon shapes for the estimates, you could do the following. First draw the plot as above, but use efac=0 to hide the vertical lines on the CIs. Then just draw over the summary polygons with addpoly():

forest(estimates, variances, slab=labels, psize=1, atransf=exp, xlab="Relative Risk (log scale)", at=log(c(.2, .5, 1, 2)), efac=0)
addpoly(estimates, variances, atransf=exp, rows=3:1, col="white", annotate=FALSE)

You can also use efac=1.5 in addpoly() to stretch the polygons vertically. Adjust the factor to your taste.

Wolfgang
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  • Perfect - I'll link to this from my question on Stack Overflow too (sorry, I don't have enough reputation to mark this as useful) – ChrisP Jun 20 '14 at 08:44