[R-meta] metafor::matreg() and its workflow

-----Original Message-----
From: Stefanou Revesz [mailto:stefanourevesz at gmail.com]
Sent: Monday, 13 December, 2021 22:15
To: Viechtbauer, Wolfgang (SP)
Cc: R meta
Subject: Re: metafor::matreg() and its workflow

Super helpful! For some reason, the devel version doesn't get
installed on my machine (must be an R issue; mine's a version 4.0).

At some point, one might say which regression is more useful, the one
on the means from the fixed effects or the one on the true effects
from the random effects!

rma.mv(yi ~ 0 + outcome*group, V, random = ~ 0 + outcome*group |
study, struct = "GEN")

Thank you so very much!
Stefanou

On Mon, Dec 13, 2021 at 2:59 PM Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:

Here is an example using G as input to matreg():

https://www.metafor-

project.org/doku.php/analyses:vanhouwelingen2002#regression_of_true_log_odds

(you will need the devel version of metafor for the cvvc="varcov" part).

Best,
Wolfgang

-----Original Message-----
From: Stefanou Revesz [mailto:stefanourevesz at gmail.com]
Sent: Monday, 13 December, 2021 21:20
To: Viechtbauer, Wolfgang (SP)
Cc: R meta
Subject: Re: metafor::matreg() and its workflow

Thank you so much! One clarification question. matreg() is not
effect-size specific, correct? I mean you may have meta-analyzed any
type effect size (SMD, ROM, OR, ...) and then subject the vcov() or G
or H matrices of those meta-analyses to matreg(), correct?

Thanks again,
Stefanou

On Mon, Dec 13, 2021 at 12:53 PM Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:

There is an upcoming talk by Suzanne Jak on MASEM at this seminar series:

https://www.srmasig.org/seminar/

Might be of interest.

Best,
Wolfgang

-----Original Message-----
From: Stefanou Revesz [mailto:stefanourevesz at gmail.com]
Sent: Thursday, 09 December, 2021 23:23
To: Viechtbauer, Wolfgang (SP)
Cc: R meta
Subject: Re: metafor::matreg() and its workflow

Dear Wolfgang,

I see, so conditioning (using predict() ) is the way to go even if
there is a large set of conditions.

Related to the above, if instead of vcov(), one intends to use G and H
matrices (latent regression), would that also require conditioning on
the levels of fixed effects?

The other challenge that I expect to encounter (I'm preparing to do a
meta-analysis exploring anxiety and achievement) is that correlations
reported in each study may not reflect the same pair of variables
across the studies. Thus, this prevents me from having a "var1.var2"
like variable in my model which also means I can't proceed to mateg().
I believe, in that case, I can do only an exploratory study of
correlations (with rma.mv() ) rather than a model based one (with
matreg() ).

Thank you,
Stefanou