[R-meta] Meta-analysis of prevalence data: back-transformation and polytomous data

[Röver, Christian]

Dear Jakub,

I think Schwarzer et al. (2019; https://doi.org/10.1002/jrsm.1348) have
a valid point, and that the double arcsine transform is not really
suitable for meta-analysis purposes. The approach by Barendregt et al.
(2013; https://doi.org/10.1136/jech-2013-203104) seems to me more like
a kind of workaround, and I am not sure whether it will actually work
generally, or would only "fix" the issue (or at least won't fail
immediately) in some cases.

I guess a quick and simple solution might be to go for the ("simple")
arcsine transformation instead, or otherwise check out one of the more
appropriate alternative approaches that were pointed out by Schwarzer
et al. (2019).

Cheers,

Christian



On Wed, 2022-02-23 at 12:06 +0100, Jakub Ruszkowski wrote:
> Dear Community,
> 
> I am trying to do a meta-analysis of prevalence according to the 
> recommendations arising from the current literature. I have two
> problems that 
> I cannot handle on my own.
> 
> 1. I found that there are controversies about a back-transformation
> method 
> for the Freeman-Tukey double arcsine transformation (Schwarzer et
> al., doi: 
> 10.1002/jrsm.1348). However, there is a probable resolution that
> incorporates 
> inverse variance instead of harmonic mean (Barendregt-Doi
> implementation, 
> clearly explained in Supplementary Materials in doi:
> 10.1111/jebm.12445; 
> older version introducing it: 10.1136/jech-2013-203104).
> Unfortunately, I am 
> not proficient in programming, so I am not sure how to implement
> this 
> solution on my own. Is there an R implementation of Barendregt-Doi 
> back-transformation available or is it possible to add this method to
> the 
> metafor?
> 
> 2. Are there any available examples of R code to meta-analyze 
> ordinal/multinomial prevalence data (e.g., mild, moderate, severe
> severity)? 
> I found one method implemented in MetaXL that used double arcsine 
> transformation (mentioned earlier doi: 10.1136/jech-2013-203104), and
> one 
> Bayesian method using the Dirichlet-multinomial model (doi: 
> 10.1080/03610918.2021.1887229). Unfortunately, the R code is not
> supplemented 
> with the latter article.
> 
> 
> Kind regards
>