[R-meta] Rare dependent variable with correlation among effect sizes

Arthur Albuquerque @rthurc@|r|o @end|ng |rom gm@||@com
Mon Mar 6 21:17:20 CET 2023

Hi all,

Tl;dr: I want to meta-analyze studies with a rare dependent variable with correlation among effect sizes.

I have four randomized controlled trials. Within each RCT, there is one “control” group and multiple (>3) “experimental” groups. Thus, there is a shared control group which induces correlation among the effect sizes within each RCT.

I am aware that constructing a variance-covariance matrix with vcov() then fitting the model with rma.mv() is an appropriate solution (per topic 5 in “Details” in ?vcov). Such approach requires one to first estimate effect sizes with escalc().

However, I am dealing with RCTs with a rare dependent variable. In these cases, using an exact likelihood (in this case, Binomial) is preferable. I believe rma.mv() does not support such likelihood.

How can I fit such model with rma.glmm() considering correlation among effect sizes? Ideally, I’d like to fit a random effect model.



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