[R-meta] MLMA - shared control group

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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