[R-meta] MLMA - shared control group

[Jorge Teixeira]

Thank you. :)

Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl>
escreveu no dia terça, 31/08/2021 à(s) 09:57:

> >-----Original Message-----
> >From: Jorge Teixeira [mailto:jorgemmtteixeira using gmail.com]
> >Sent: Tuesday, 31 August, 2021 10:05
> >To: Viechtbauer, Wolfgang (SP)
> >Cc: Reza Norouzian; R meta
> >Subject: Re: [R-meta] MLMA - shared control group
> >
> >Thanks Wolfgang and Reza - I have made some progress, at least.
> >
> >Yes, I am thinking about 3-level MA.
> >
> >Just 2 last points:
> >
> >1) Is V** supposed to be equivalent to a certain default correlation
> value in
> >impute_covariance_matrix(). (IE. r=0.5)?
> >
> >(** --> V
> ><- bldiag(lapply(split(dat, dat$study), calc.v))
> >)
> >
> >The 2 methods seem to give different results, across multiple r values.
>
> It's not clear what exactly you are comparing, but I guess you are
> comparing impute_covariance_matrix() with the code you found on the metafor
> website, namely:
>
> https://www.metafor-project.org/doku.php/analyses:gleser2009
>
> Those are different approaches, so they are not expected to give the same
> results.
>
> >2) r values are pretty much based on "expert" opinion and faith? We don't
> have
> >tools to assess which value would be the best choice?
>
> The correlations should be based on the actual data, like in this example:
>
>
> https://www.metafor-project.org/doku.php/analyses:gleser2009#multiple-endpoint_studies
>
> If you don't know the correlations, then one can make a 'guestimate'.
> Maybe a few studies do report the correlations, so one can base this
> guestimate on that.
>
> But no, there isn't really a way of assessing which guestimate is 'best'
> (well, one can imagine some rather complex methods that might go in this
> direction, but this is beyond the scope of this discussion).
>
> Best,
> Wolfgang
>

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