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

[Viechtbauer, Wolfgang (SP)]

>-----Original Message-----
>From: Edwin Lebrija Trejos [mailto:elebrija using hotmail.com]
>Sent: Monday, 31 January, 2022 21:52
>To: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis using 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?

Please search the archives; see: https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis

>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)

Yes, that paper discusses the same issue.

>Thanks again for your kind attention.
>Edwin