[R-meta] Does clubSandwich::coef_test() handle crossed random-effects?

On Oct 3, 2021, at 4:20 AM, Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:

?Hi Fred,

The dfs you see are computed based on a Satterthwaite approximation. Depending on the data structure, the dfs could very well be much lower than the number of studies/estimates.

This aside, a limitation of the cluster robust approach is that it can only capture dependencies that occur within the groups defined by the clustering variable. Or put in a different way, for the approach to work, there need to be a sufficient (for some definition of "sufficient") number of independent groups within the dataset. When introducing crossed random effects or other random effects that can create dependencies across the entire dataset (like spatial correlation or correlation due to phylogeny in meta-analyses in ecology and evolution involing multiple species), then there might only be a single (or very few) subsets in the data that are independent and then the cluster robust approach cannot really be used anymore.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On
Behalf Of Farzad Keyhan
Sent: Sunday, 03 October, 2021 7:35
To: R meta
Subject: [R-meta] Does clubSandwich::coef_test() handle crossed random-effects?

Hello Wolfgang and James,

I have 51 studies, but when I run an unconditional model (below),
coef_test() outputs a df of 4.76 (makes no sense).

I suspected that coef_test() is not picking up my highest-level
cluster (study). So, I made that explicit in the call. And then, I got
an error saying "random effects are not nested".

First, does clubSandwich handle models with crossed random-effects? If
not, is there an alternative code for rma.mv() models to accomplish
similar things (I wanted to do egger's test as well)--and of course
the small sample correction may not be available there.

Thanks, Fred

# Note 'scale' is crossed with 'study'
m1 <- rma.mv(yi, V, random = list(~1|study/es, ~1|scale), data=data)

coef_test(m1, "CR2")
  Coef. Estimate    SE t-stat d.f. p-val (Satt) Sig.
1 intrcpt    0.506 0.193   2.63 4.76       0.0489    *

## I made cluster explicit but get an error:
coef_test(m1, "CR2", cluster = data$study)
*** Error: Random effects are not nested within clustering