[R-meta] MLMA - shared control group

Viechtbauer, Wolfgang (SP) wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Tue Aug 31 10:57:06 CEST 2021

>-----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:


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:


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).


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