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

Viechtbauer, Wolfgang (NP) wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Mon Mar 6 21:45:11 CET 2023

Hi Arthur,

Just a small correction: vcov() should be vcalc().

But to your actual question: rma.glmm() doesn't handle that. Some options:

1) use rma.mv() with a measure like "AS" and use vcalc() to construct the V matrix.

2) go straight to lme4::glmer(). Except for the non-central hypergeometric model, rma.glmm() is in essence just a wrapper for lme4::glmer() (or GLMMadaptive / glmmTMB as alternatives).


>-----Original Message-----
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On
>Behalf Of Arthur Albuquerque via R-sig-meta-analysis
>Sent: Monday, 06 March, 2023 21:17
>To: R meta
>Cc: Arthur Albuquerque
>Subject: [R-meta] Rare dependent variable with correlation among effect sizes
>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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