[R-meta] Dear Wolfgang

[Lee, Ju]

Hello,

I am currently using a mixed effect meta-regression to explore the effects of different environmental variables on fish densities in coastal habitats.
For this, I am constructing a different model for individual fish species, in which my goal is to identify important predictor variables separately for each species by using aicc-based model selection (glmulti).
For each fish dataset, I usually identify 3-4 potentially important predictor variables – which include both categorical and continuous variables – based on a priori hypothesis and the statistical test of omnibus test (of each individual predictor variable).
I am only testing the main effects and not the interaction of multiple variables being included in the final models.

The problem I am running into is the small number of studies being available for many species, with the number of study response (effect size, not independent study) ranging from 6 to 100 for different fish species.

So my question is:

Is there a commonly used threshold for the multiple meta-regression using rma.mv to be reliable and avoid false positive or negative relationship?
From https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0229345&type=printable I read that, for meta-regression, n should be greater than 8 for low variance data, but should be greater than 25 for high variance data.
I wanted to seek your advice on what would be a good threshold criteria for minimum study response (effect size) number for running the meta-regression models.
Also, could we also apply similar threshold for number of independent studies (not effect size, but the actual publication) included in each dataset as well?

Thank you.
Sincerely,
Juhyung


Juhyung Lee
Postdoctoral Fellow
Marine Science Center, Northeastern University
430 Nahant Rd, Nahant, MA 01908, USA
Phone: +1(650)285-7614


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