[R-meta] Multivariate meta-analysis when "some studies" are multi-outcome

[Gladys Barragan-Jason]

Thank you so much!

Le jeu. 18 mars 2021 à 11:50, Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> a écrit :

> Dear Gladys,
>
> random=list( ~1|study,~1|lab) makes little sense, since study = 1 from lab
> X would receive the same random effect as study = 1 from lab Y, but I
> assume that the 1 is just a number that distinguishes multiple studies from
> the same lab and has no inherent meaning that somehow links the 1 from lab
> X to the 1 from lab Y.
>
> Instead, random=~1|lab/study (you don't need the list()) seems more
> applicable. This adds random effects for each level of lab and for each
> study within each lab. Hence, the combination X-1 will be a different
> random effect than the combiantion Y-1.
>
> However, this assumes no heterogeneity of the true effects within a study
> (within a lab). For example, this would assume that the 0.3 and 0.6 (for
> X-1) are both estimates of the same underlying true effect, which is
> assumed to be identical. That may or may not be true. Hence, as I noted in
> my response to Simon (see also
> https://www.metafor-project.org/doku.php/analyses:konstantopoulos2011 and
> search the archives for similar discussions), one should add higher level
> random effects (such as those for study and lab) to the random effects at
> the estimate level. So, if you dataset is called 'dat', then do:
>
> dat$estid <- 1:nrow(dat)
>
> in combination with random=~1|lab/study/estid.
>
> Finally, random=list(~1|lab, ~1|lab/study) is superfluous. ~ 1 | lab adds
> random effects for each level of lab and ~1|lab/study also does this (plus
> it adds random effects for each level of study within lab). So this would
> add random effects for lab twice.
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: Gladys Barragan-Jason [mailto:gladou86 using gmail.com]
> >Sent: Thursday, 18 March, 2021 11:17
> >To: Viechtbauer, Wolfgang (SP)
> >Cc: R meta
> >Subject: Re: [R-meta] Multivariate meta-analysis when "some studies" are
> multi-
> >outcome
> >
> >Dear Wolfgang,
> >
> >After reading your post carefully, I am still a bit confused about how to
> >implement the random effects.
> >My data are coded as follows (similar number or text in one lab means
> same lab and
> >similar number or text in one study means same participants). As you can
> see you
> >can have several studies from one lab and several effect sizes for one
> study.
> >Should I then code the random effects like this:
> >random=list( ~1|study,~1|lab)
> >or like this:
> >random=list( ~1|lab/study)
> >or like that;
> >random=list( ~1|lab, ~1|lab/study)
> >
> >lab       study   effect size
> >X              1           0.3
> >X              1           0.6
> >X              2          0.2
> >Y              1           0.5
> >Y              2           0.1
> >Z              1           0.1
> >
> >Thanks a lot for your help.
> >Best wishes,
> >Gladys
> >
> >Le mer. 17 mars 2021 à 13:27, Viechtbauer, Wolfgang (SP)
> ><wolfgang.viechtbauer using maastrichtuniversity.nl> a écrit :
> >Dear Gladys,
> >
> >Whether this makes sense depends on how thse variables are coded. There
> have been
> >several posts in the past on this mailing list where this was discussed.
> One that
> >I quickly found is:
> >
> >https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2018-July/000896.html
> >
> >Best,
> >Wolfgang
> >
> >>-----Original Message-----
> >>From: Gladys Barragan-Jason [mailto:gladou86 using gmail.com]
> >>Sent: Tuesday, 16 March, 2021 11:39
> >>To: Viechtbauer, Wolfgang (SP)
> >>Cc: Simon Harmel; R meta
> >>Subject: Re: [R-meta] Multivariate meta-analysis when "some studies" are
> multi-
> >>outcome
> >>
> >>Dear Wolfgang,
> >>
> >>Following Simon's question, I am also comparing the efficiency of
> programs (pre-
> >>post comparisons).
> >>For some of them, I do have several effect sizes for one study and one
> lab. So I
> >>was using the following code to account for it.
> >>
> >>res.ExpNC<-rma.mv(yi, vi, mods= ~ categ , random=list(
> >~1|study,~1|lab),data=dat2)
> >>
> >>But I am now wondering whether I should do this instead:
> >>
> >>dat2$estid <- 1:nrow(dat2)
> >>res.ExpNC<-rma.mv(yi, vi, mods= ~ categ , random=list(
> >>~1|study/estid,~1|lab/estid),data=dat2)
> >>
> >>What do you think?
> >>
> >>Thanks a lot for your response,
> >>
> >>Gladys
> >>
> >>Le mar. 16 mars 2021 à 11:28, Viechtbauer, Wolfgang (SP)
> >><wolfgang.viechtbauer using maastrichtuniversity.nl> a écrit :
> >>Dear Simon,
> >>
> >>At the very least, you should add random effects at the level of the
> studies and
> >>at the level of the estimates, so:
> >>
> >>dat$estid <- 1:nrow(dat)
> >>
> >>and then
> >>
> >>random = ~ 1 | id / estid
> >>
> >>For longitudinal data, one could also consider using some kind of
> autocorrelation
> >>structure for the estimates within studies. There are some examples here:
> >>
> >>https://wviechtb.github.io/metafor/reference/dat.ishak2007.html
> >>https://wviechtb.github.io/metafor/reference/dat.fine1993.html
> >>
> >>clubSandwich::impute_covariance_matrix() also allows for the
> construction of a V
> >>matrix with an autocorrelation structure.
> >>
> >>If the different outcomes are meaningfully related across studies (i.e.,
> outcome
> >>'1' stands for the same thing across all studies), then one could also
> consider
> >>using an unstructured var-cov matrix with correlated random effects for
> outcomes
> >>within studies. This would be akin to:
> >>
> >>https://www.metafor-project.org/doku.php/analyses:berkey1998
> >>
> >>Best,
> >>Wolfgang
>


-- 

------------------------------------------

Gladys Barragan-Jason, PhD.  Website
<https://sites.google.com/view/gladysbarraganjason/home>

Station d'Ecologie Théorique et Expérimentale (SETE)

CNRS de Moulis

[image: image.png][image: image.png]

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