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

CHAPPELL Francesca F@Ch@ppe|| @end|ng |rom ed@@c@uk
Thu Feb 24 12:36:56 CET 2022

This paper argues for modelling the within-study variance directly as binomial (https://pubmed.ncbi.nlm.nih.gov/18083461/)  and provides a SAS program to do so. I think R can do it too with glmer, though I don't have a handy program. But see https://journals.lww.com/epidem/Fulltext/2020/09000/Meta_analysis_of_Proportions_Using_Generalized.16.aspx , specifically the appendices for R code. I haven't come across a multinomial version that doesn't use WinBUGS.


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From: R-sig-meta-analysis <r-sig-meta-analysis-bounces using r-project.org> On Behalf Of Röver, Christian
Sent: 24 February 2022 08:43
To: jakub.ruszkowski using gumed.edu.pl; r-sig-meta-analysis using r-project.org
Subject: Re: [R-meta] Meta-analysis of prevalence data: back-transformation and polytomous data

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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).



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
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