Message-ID: <159b7464-429e-47d1-b05c-3fc2a43eb6b7@Spark>
Date: 2023-03-06T21:00:33Z
From: Arthur Albuquerque
Subject: [R-meta] Rare dependent variable with correlation among effect sizes
In-Reply-To: <5c9cf7fc3474429291c609c5733448ed@UM-MAIL3213.unimaas.nl>
Hi Wolfang, thanks for the quick reply.
About 2), would you fit the model in lme4 then use a sandwich estimator? As you said, a regular random-effect model in lme4 would be analog to rma.glmm().
On Mar 6, 2023, 5:45 PM -0300, Viechtbauer, Wolfgang (NP) <wolfgang.viechtbauer at maastrichtuniversity.nl>, wrote:
> 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).
>
> Best,
> Wolfgang
>
> > -----Original Message-----
> > From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at 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.
> >
> > Best,
> >
> > Arthur
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