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.
Best,
Arthur