[R-meta] MLMA - shared control group

[Jorge Teixeira]

 Hi Reza - thanks for the reply.

1) If I get things right, can we copy+paste the matrix code and it always
works in similar cases?

dat <- escalc(measure="MD", m1=em, sd1=esd, n1=en, m2=cm, sd2=csd, n2=cn,
data=dat)

## correlation matrix

calc.v <- function(x) {
  v <- matrix(1/x$n2i[1] + outer(x$yi, x$yi, "*")/(2*x$Ni[1]),
nrow=nrow(x), ncol=nrow(x))
  diag(v) <- x$vi
  v
}

V <- bldiag(lapply(split(dat, dat$study), calc.v))
V

# fit multilevel model
res_mlma <- rma.mv(yi, V, random = ~ 1 | study/obs, data=dat)

2) For meta-regression, we also have to use *V*, not *vi*, correct?

Thanks,
Jorge

Reza Norouzian <rnorouzian using gmail.com> escreveu no dia sábado, 28/08/2021
à(s) 16:31:

> 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
> >
> >         [[alternative HTML version deleted]]
> >
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>

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