Most of my work these days is running pairwise and mixed treatment meta-analyses, where the unit of analysis for any covariate adjustment is usually quite small (10-40). This means that we usually end up plugging coefficients in one at a time, which is less than ideal.

I’m wondering if anyone here is familiar with applications of ridge/lasso/horseshoe style shrinkage to coefficients in meta-regression, or if perhaps there is something about the meta-analysis paradigm that makes this not make sense? I’ve been looking for a good topic to work out a simulation for, and thought this might be a good one since it fits well with what I do for the bulk of my work. Quick search of google/pubmed didn’t pull out anything that was immediately relevant, so I thought I’d check here to see if the idea is already used in some work that this group is familiar with.

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