[R-meta] Rare dependent variable with correlation among effect sizes
Arthur Albuquerque
@rthurc@|r|o @end|ng |rom gm@||@com
Mon Mar 6 22:45:08 CET 2023
Thanks a lot.
On Mar 6, 2023, 6:44 PM -0300, Viechtbauer, Wolfgang (NP) <wolfgang.viechtbauer using maastrichtuniversity.nl>, wrote:
> I don't remember our numbering off the top of my head, but yep, that is the model I just described, with fixed study effects and using +-1/2 coding for the treatment random effect. With more than a single treatment group, you will need to think about how to code the latter. But I am off to bed now.
>
> > -----Original Message-----
> > From: Arthur Albuquerque [mailto:arthurcsirio using gmail.com]
> > Sent: Monday, 06 March, 2023 22:37
> > To: R Special Interest Group for Meta-Analysis; Viechtbauer, Wolfgang (NP)
> > Subject: RE: [R-meta] Rare dependent variable with correlation among effect sizes
> >
> > Following your article "A comparison of seven random-effects models for meta-
> > analyses that estimate the summary odds ratio”,
> >
> > would that be "Model 4: a modified version of Simmonds and Higgins model”?
> >
> > glmer(cbind(event,n-event)~factor(study)+factor(treat)+(treat12-1|study),
> > data=thedata1, family=binomial(link="logit"))
> >
> > On Mar 6, 2023, 6:33 PM -0300, Viechtbauer, Wolfgang (NP)
> > <wolfgang.viechtbauer using maastrichtuniversity.nl>, wrote:
> >
> > When using a logistic model for the analysis, the data structure is changed into
> > a long / arm-based format. One then adds fixed (or random) effects for studies, a
> > fixed effect for group, and a random effect for group (to account for
> > heterogeneity in the treatment effects). This circumvents the issue of a shared
> > control group. This is in essence the same as what happens in a network meta-
> > analysis using an arm-based instead of a contrast-based model (in the latter
> > case, we need to deal with the dependency in three- or more-arm studies, but not
> > in the arm-based model).
> >
> > -----Original Message-----
> > From: Arthur Albuquerque [mailto:arthurcsirio using gmail.com]
> > Sent: Monday, 06 March, 2023 22:22
> > To: R Special Interest Group for Meta-Analysis; Viechtbauer, Wolfgang (NP)
> > Subject: RE: [R-meta] Rare dependent variable with correlation among effect sizes
> >
> > My effect size of interest is the odds ratio.
> >
> > A random effect logistic regression with random intercept by study won’t account
> > for the shared control group within each study.
> >
> > What other alternative do I have over the sandwich estimator?
> >
> > On Mar 6, 2023, 6:19 PM -0300, Viechtbauer, Wolfgang (NP)
> > <wolfgang.viechtbauer using maastrichtuniversity.nl>, wrote:
> >
> > I don't see the need to use a sandwich estimator, and with 4 studies, this is
> > unlikely to be all that useful.
> >
> > -----Original Message-----
> > From: Arthur Albuquerque [mailto:arthurcsirio using gmail.com]
> > Sent: Monday, 06 March, 2023 22:01
> > To: R Special Interest Group for Meta-Analysis; Viechtbauer, Wolfgang (NP)
> > Subject: RE: [R-meta] Rare dependent variable with correlation among effect sizes
> >
> > 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
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