[R-meta] R-sig-meta-analysis Digest, Vol 67, Issue 2

[Yuhang Hu]

Dear Wolfgang,

Many thanks for your response. To make sure, as a general principle, do you
recommend always computing those probabilities based off of the
r2z-transformed effect size estimates?

My intuition regarding transforming the variance components back to their
original scale came from the fact that the emmeans package does a similar
thing (for standard errors) via the delta method (
https://cran.r-project.org/web/packages/emmeans/vignettes/transformations.html#after
).

And my desire to accurately back transform the variance components was
solely because I thought it could improve interpretability given the ease
of working with correlations than r2z transformed correlations.

Thanks again,
Yuhang

On Wed, Dec 14, 2022 at 4:01 AM <r-sig-meta-analysis-request using r-project.org>
wrote:

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>    1. Using variance components with effect size transformation
>       (Yuhang Hu)
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> Message: 1
> Date: Tue, 13 Dec 2022 20:28:55 -0700
> From: Yuhang Hu <yh342 using nau.edu>
> To: R meta <r-sig-meta-analysis using r-project.org>
> Subject: [R-meta] Using variance components with effect size
>         transformation
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> Dear Meta Community,
>
> I have a 3-level meta-regression model with a categorical moderator (3
> levels) plus some covariates fit as:
>
> m6 = rma.mv(r2z_transformed ~ 0 + cat_mod + covariates, random = ~ 1 |
> study/effect)
>
> I wonder to calculate probabilities from the distribution of effects as
> explained in this post (
> https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2022-August/004136.html
> ),
> whether I should use the transformed variance components (A) or
> back-transformed variance components (B)?
>
> A: confint.rma.mv(m6)   # transformed i.e., 'r2z'
> B: confint.rma.mv(m6, transf = transf.ztor) # back-transformed i.e., 'z2r'
>
> Currently, A and B give the same result, and I wonder why?
>
> Thanks,
> Yuhang
>
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