[R-meta] MLMA - shared control group

[Reza Norouzian]

Please see my answers below.

> Hey everyone.
>
> Regarding MLMA, due to shared control group, I wonder if:
>
> 1) Is it enough to code "studies/obs" and we are done?
>
> res_mlma <- rma.mv(yi, vi, random = ~ 1 | studies/obs, data=dat)
>
>

Unfortunately, no, using random-effects alone doesn't directly account
for that source of dependency. See:
https://www.metafor-project.org/doku.php/analyses:gleser2009#multiple-treatment_studies;
for a good discussion on this.


> 2) Or after that, do we also need to compute a correlation matrix? I got
> lost in this part.

This type of dependency needs to be specified in the rma.mv() via the
V argument. See the link in the previous answer for details. Also
check out the archives to find several discussions on this.

>
> 3) When coding for "studies/obs", the best option is to NOT split the
> number of participants in obs?

Not sure, what you mean here, but `obs` usually denotes the id for
each unique row in your data, like:

studies  obs
1           1
1           2
2           3
2           4

When you fit a model via rma.mv() and specify the random part as
"studies/obs", then, a unique random effect for each study and a
unique random effect for each row within a study is added to your
model. The former accounts for the effects' variation between studies,
the latter accounts for effects' variation within studies.

>
> 3.1) Any good literature to support that decision in MLMA? It still seems
> strange to me, as it will inflate the actual real number of participants.

see my previous answer.

>
> Thanks for your time and best wishes,
> Jorge
>
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>
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