[R-meta] Random and mixed effects models with the Metafor rma.mv function

-----Original Message-----
From: Edwin Lebrija Trejos [mailto:elebrija at hotmail.com]
Sent: Monday, 31 January, 2022 21:52
To: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis at r-project.org
Subject: Re: Random and mixed effects models with the Metafor rma.mv function

Dear Wolfgang,

Thanks for your illustrating replies with links.
Some follow up (I am copy-pasting the relevant bits of conversation):

1) ">- Moreover, agreeing that it's important to control for dependence among

outcomes, I wonder if additionally controlling for the dependence of outcomes
within studies is also in place. This, since each published study used  in the
meta-analysis reports experimental outcomes for several species tested in the
same study. Is the following metaphor model syntax appropriate to correct for
such within study dependency? rma.mv (yi, vi, random = list (~1|Species, ~1|
Study.ID/ Outcome.ID), data=dat), where Study.ID is a variable that identifies
each published study?

Yes. Whether this is fully sufficient to account for within-study dependence
depends on whether the sampling errors are independent or not. This has been
discussed many times on this mailing list. But adding study as a random effect is
generally something I would do."

Can you refer me to some of those discussions or suggest some specific search
keywords?

2) ">My question here is: isn't it better to explore the sources of
heterogenenity in

the data taking advantage of the mixed model approach implemented by the rmw.mv
function and include in the same model both categorical and continuous

variables.

Or, is there an advantage to performing" Subgroup" analysis?

See:

https://www.metafor-project.org/doku.php/tips:comp_two_independent_estimates

Generally, my preference is to use meta-regression models instead of
subgrouping."

A model that uses the full data intuitively seems preferred to me, yet I am not
sure I can pinpoint the reasons for the preference.
The example on the link you sent shows no difference in results between the
analysis of subgroups and a meta-regression, providing that an "~inner | outer"
formula and a diagonal variance structure are specified in the random and
structure arguments of the rma.mv function, respectively... So, is a meta-
regression preferred because it allows to choose (and test) between independent
or pooled estimations of the residual heterogeneity? And, is this possibility
relevant because of the reasons detailed the the referred paper by Rubio-
Aparicio, et al. 2020? (https://doi.org/10.1080/00220973.2018.1561404)

Thanks again for your kind attention.
Edwin