[R-meta] Metafor: meta regression using rma function for proportion with categorical and continuous variable using PFT transformation

[Danyang Dai]

Hi Michael and the community

Thanks for your suggestion. I tried log transformation and I could go with
log transformation as well. I chose Freeman-Tukey transformation as the
prevalences we have ranged from 2% up to 80%. If I were to use log
transformation, how should I backgransform the coefficients for
interpretation? Thank you for your help!

Kind regards
Daidai

On Sun, Sep 29, 2024 at 9:10 PM Michael Dewey <lists using dewey.myzen.co.uk>
wrote:

> Dear Daidai
>
> Are you committed to using the Freeman-Tukey transformation? It is
> easier to back-transform using log or log-odds.
>
> Michael
>
> On 29/09/2024 05:05, Danyang Dai via R-sig-meta-analysis wrote:
> > Dear community members
> >
> > I am preparing meta regression using escalc and rma function from the
> > Metafor package. I would like to control for study mean age (continuous
> > variable), percentage of CKD patients (continuous variable between 0 and
> > 1) and the region where the study was conducted (categorical variable).
> >
> > The effect size is a proportion (xi/ni). For the first step, I used the
> > PFT to transform the data using: icu_ies <- escalc(data =
> > data_icu_meta_join_2, xi = events, ni = icu_all, measure = "PFT").
> >
> > To conduct the meta regression, I then run: icu_region_ckd_age <- rma(yi
> > = yi, vi = vi, data = icu_ies, mods = ~region +ckd_pre+age_all_mean_1 ).
> > See the output:
> > Screenshot 2024-09-29 at 13.49.30.png
> > I am having trouble*interpreting the estimated coeffections* from the
> > output above. I could tell that the omnibus test suggests that we cannot
> > reject the null hypothesis which indicates that the joint parameters
> > were not significant. If we ignore the significance of the parameters,
> > how should we interpret the estimates? For example, if we take region =
> > North America, controlling for the CKD percentage and mean age of the
> > study population, North America has shown a higher prevalence (0.2135)
> > compared to the baseline region. As we have done the PFT transformation
> > upfront, I am not sure if that is the correct interpretation. I tried
> > use prediction function to calculate the backtranformed values:
> > predict(icu_region_ckd_age, transf=,
> > targs=list(ni=icu_ies$icu_all),transf=transf.pft), but this would return
> > the individual backtranformed value for each study. I would like to
> > calculate the backtranformed coeffections for the purpose of
> > interpretation. Thank you all for your suggestions and help!
> >
> > Kind regards
> > Daidai
> > Github: https://github.com/DanyangDai <https://github.com/DanyangDai>
> > University email: danyang.dai using uq.edu.au <mailto:danyang.dai using uq.edu.au>
> >
> >
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> Michael
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