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

[Stefanou Revesz]

Dear Wolfgang,

Many thanks. I read all three resources. Regarding:

"What if our rma.mv() fit has multiple (interactive) moderators, what
should be passed as R and vcov() to matreg()?"

I meant, as a general matter, if our dataset (e.g.,`dat.craft2003`),
say, had an additional continuous moderator (Xwb) varying within and
between studies (see below), then how could such a moderator affect
the result of rcalc() or rma.mv() or matreg()?

Apparently, the `dat` object returned by rcalc() doesn't return the
full dataset so to use moderators like Xwb from `dat` in the
subsequent rma.mv() call. Also, in your Rmarkdown doc (previous
email), you use a subgroup analysis to deal with a study-level,
categorical moderator (sport) which makes me wonder how we can deal
with Xwb or when multiple moderators exist in the data?

Thanks,
Stefanou

set.seed(0)
dat <- dat.craft2003
dat$Xwb <- rnorm(nrow(dat),rnorm(nrow(dat),9,4),2)

On Sun, Dec 5, 2021 at 11:12 AM Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
>
> Dear Stefanou,
>
> See below for my responses.
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: Stefanou Revesz [mailto:stefanourevesz using gmail.com]
> >Sent: Sunday, 05 December, 2021 3:55
> >To: R meta; Viechtbauer, Wolfgang (SP)
> >Subject: metafor::matreg() and its workflow
> >
> >Dear Wolfgang,
> >
> >In the latest version of metafor, I realized there are a couple of new
> >functions specifically made for correlations (that's awesome!).
> >
> >I understand the use of rclac() which provides the V for rma.mv(). But
> >I'm a bit unclear why we get the vcov() from the rma.mv() fit and then
> >input it to matreg().
> >
> >My questions are:
> >
> >1- What role does matreg() play and why not just using rma.mv()?
>
> matreg() is for fitting regression models based on variance-covariance and correlation matrices. Such a matrix can be obtained by conducting a meta-analysis (e.g., using rma.mv()). This all takes us into methodology that is sometimes described as MASEM (meta-analytic structural equation modeling). Maybe start with this chapter:
>
> Becker, B. J., & Aloe, A. (2019). Model-based meta-analysis and related approaches. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta-analysis (3rd ed., pp. 339-363). New York: Russell Sage Foundation.
>
> to go along with:
>
> https://wviechtb.github.io/metafor/reference/matreg.html
>
> and
>
> https://wviechtb.github.io/meta_analysis_books/cooper2019.html#16)_Model-Based_Meta-Analysis_and_Related_Approaches
>
> >3- What if our rma.mv() fit has multiple (interactive) moderators,
> >what should be passed as R and vcov() to matreg()?
>
> I can't really answer this question without further details. But I would suggest to first read up on the methdology itself.
>
> >3- Is rtoz =TRUE necessary in this workflow?
>
> I assume you mean argument 'ztor' in matreg(). Whether to use this depends on whether you have a matrix of r-to-z transformed correlation coefficients or a matrix of raw correlation coefficients. Whether one should or should not use transformed correlations in a meta-analysis that could yield such a matrix is a lengthy and endless debate, so I am not going to touch on "necessary" without a ten-foot pole.
>
> >Thank you,
> >Stefanou