[R-meta] MLMA - shared control group

[Viechtbauer, Wolfgang (SP)]

>-----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