Skip to content
Prev 28 / 5632 Next

[R-meta] Freeman-Tukey double arcsine transformation and harmonic mean

Hi Naike,

The first linked got mangled up. It is: http://www.metafor-project.org/doku.php/analyses:miller1978

The exact back/inverse transformation of the Freeman-Tukey (double arcsine) transformation requires that we specify the sample size for the transformed value. So:

library(metafor)
dat <- escalc(measure="PFT", xi=4, ni=10)
yi     vi
1 0.6936 0.0238

transf.ipft(dat$yi, ni=10)

yields a proportion of 0.4 as expected.

Now if you synthesize a whole bunch of transformed values and you want to back-transform that value to a proportion, you still need to specify some value for the sample size if you want to use the exact back-transformation. Miller (1978), who derived the back-transformation, suggested to use the harmonic mean of the sample sizes. That is what transf.ipft.hm() does. Using the harmonic mean of the sample sizes is a rather heuristic method that may or may not work so well. I would be interested in any published papers that show this to be a problem.

I don't know what MetaXL does for the back-transformation, but maybe it just pretends that the values are arcsine-square-root transformed proportions and then uses the back-transformation for that -- which does not require one to specify the sample size. The difference is typically negligible:

transf.iarcsin(dat$yi)

yields 0.4086998. But then, one might as well just do the meta-analysis directly with the AS transformed proportions:

dat <- escalc(measure="PAS", xi=4, ni=10)
dat
yi     vi
1 0.6847 0.0250

transf.iarcsin(dat$yi)

gives back 0.4 exactly.

Or one could go directly to a logistic mixed-effects model for the analysis. You can do that with rma.glmm().

Best,
Wolfgang