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

[Michael Dewey]

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