[R-meta] Diagnostic tests meta regression
@de||n@@@rten|e @end|ng |rom br|@to|@@c@uk
Thu Dec 22 15:19:01 CET 2022
I am trying to check my model assumptions and I think I don�t understand fully what to look at and how, so if possible, any advice you may have would be greatly appreciated:
I am running a mixed-effects meta-regression model. Because the outcome (a rate) is rare in some cases, there is a concern that the normality assumption is not met (Viechtbauer, Chapter 11, Handbook of Meta Analysis).
As I read about this, my understanding is that both the random-effects and the random-error terms are assumed to be normally distributed (though this is not 100% clear to me, because different sources tend to focus on one term or the other, some<https://www.meta-analysis.com/downloads/MRManual.pdf> say that the method-of-moments does not, in fact, make assumptions about the distribution of random-effects (page 150)).
If true that both terms have to be normally distributed, I am trying to understand how to visualise/test this. One straightforward approach would be to look at a normal QQplot. My questions are:
1. If I follow this approach<https://www.metafor-project.org/doku.php/plots:normal_qq_plots>, and run a mixed-effects meta-regression (res4), I would be visualising/checking the assumption that the residual heterogeneity in the true effects is normally distributed or not. Am I correct in saying that this would be a proxy for checking the assumption that the random-effects are normally distributed?
1. If so, how would I check if the error terms are normally distributed? Would I plot a normal QQplot for each study? I have several outcomes and a total of 150 outcomes..
Thank you in advance,
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