[R-meta] Bivariate generalized linear mixed model with {metafor}

[Arthur Albuquerque]

I see. Thank you very much, Wolfgang.

Btw, sorry for my persistence! This subject *was* a little bit confusing until you elucidated everything.

Best,

Arthur M. Albuquerque

Medical student
Universidade Federal do Rio de Janeiro, Brazil

On Mar 9, 2022, 7:59 PM -0300, Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl>, wrote:
> Which correlation are you interested in? And are you even interested in the correlation? If not, it doesn't really matter then.
>
> Best,
> Wolfgang
>
> > -----Original Message-----
> > From: Arthur Albuquerque [mailto:arthurcsirio using gmail.com]
> > Sent: Wednesday, 09 March, 2022 23:51
> > To: r-sig-meta-analysis using r-project.org; Viechtbauer, Wolfgang (SP)
> > Subject: RE: [R-meta] Bivariate generalized linear mixed model with {metafor}
> >
> > Wow, crazy numerical coincidence then.
> >
> > I’ve been wondering about applying Reference [1] model (= your Model 6) in future
> > projects. Can you see any practical reason to apply the (group | study) syntax
> > instead of (control+treat-1|study)?
> >
> > Best,
> >
> > Arthur M. Albuquerque
> >
> > Medical student
> > Universidade Federal do Rio de Janeiro, Brazil
> >
> > On Mar 9, 2022, 7:47 PM -0300, Viechtbauer, Wolfgang (SP)
> > <wolfgang.viechtbauer using maastrichtuniversity.nl>, wrote:
> >
> > Different parameterizations of the same model.
> >
> > Also, the correlations seems like they just flipped signs, but they are really
> > different things and I suspect it's just coincidence that they happen to be so
> > close in absolute value.
> >
> > With (group | study), you have a random intercept (for the control group logit
> > risk) and a random slope for the group/treatment effect (for the log odds ratio).
> >
> > With (control+treat-1|study), you have random effects for the control and
> > treatment group logit risks. This is the same as (0 + group | study).
> >
> > So really different things are being correlated here. But in the end, it's the
> > same model, parameterized in different ways.
> >
> > Best,
> > Wolfgang

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