Message-ID: <25ba8f64-bc45-47b9-9a7b-d2b9a9bbca2e@Spark>
Date: 2023-03-06T20:17:20Z
From: Arthur Albuquerque
Subject: [R-meta] Rare dependent variable with correlation among effect sizes
In-Reply-To: <2402f80d-96e6-4625-975c-cc39502228a7@Spark>
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
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