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

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 at 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 ? 13:27, Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer at maastrichtuniversity.nl> a ?crit :
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 at 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 ? 11:28, Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer at maastrichtuniversity.nl> a ?crit :
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