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

Arthur Albuquerque @rthurc@|r|o @end|ng |rom gm@||@com
Mon Mar 6 22:00:33 CET 2023


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 using 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 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.
> >
> > Best,
> >
> > Arthur

	[[alternative HTML version deleted]]



More information about the R-sig-meta-analysis mailing list