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