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

[Danyang Dai]

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:
[image: 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
University email: danyang.dai using uq.edu.au

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