[R-meta] multivariate fixed-effect meta-analysis

[Viechtbauer, Wolfgang (SP)]

>-----Original Message-----
>From: Luke Martinez [mailto:martinezlukerm using gmail.com]
>Sent: Tuesday, 23 November, 2021 20:38
>To: Viechtbauer, Wolfgang (SP)
>Cc: Filippo Gambarota; R meta
>Subject: Re: [R-meta] multivariate fixed-effect meta-analysis
>
>Dear Wolfgang,
>
>Strictly, the model is fixed-effects multivariate (i.e., MANOVA type)
>if Filippo has one effect size per outcome, right?

I don't know what you mean by that. If you only specify V and no random effects, one could call it a multivariate fixed-effects model, just like used for example in this chapter:

https://www.metafor-project.org/doku.php/analyses:gleser2009

Whether one has one effect size per outcome or 20 is not relevant as long as V captures the covariance between the sampling errors of the estimates.

>I mean to the extent that this is not the case, then will this model
>diverge from a fixed-effect multivariate model and become more like
>marginal models (i.e., nlme::gls() type)?

Again, I can't follow your reasoning here.

>Thanks,
>Luke
>
>On Tue, Nov 23, 2021 at 1:22 PM Viechtbauer, Wolfgang (SP)
><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
>>
>> With method="FE", 'random' is also ignored. You will see in the output that it
>says "Variance Components: none".
>>
>> If 'cov_mat' captures the sampling error covariances, then this could be argued
>to be a fixed-effects version of a multivariate model.
>>
>> Best,
>> Wolfgang
>>
>> >-----Original Message-----
>> >From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org]
>On
>> >Behalf Of Filippo Gambarota
>> >Sent: Tuesday, 23 November, 2021 20:07
>> >To: R meta
>> >Subject: [R-meta] multivariate fixed-effect meta-analysis
>> >
>> >Hi!
>> >I'm performing a multivariate meta-analysis with metafor, however I'm
>> >not sure how to obtain the fixed-effect version. Given that I have not
>> >enough data I'm not interested in estimating tau for each outcome and
>> >the correlation among outcomes but only taking into account the
>> >sampling error dependence. I'm using this function:
>> >```
>> >rma.mv(
>> >    yi = eff_size,
>> >    V = cov_mat,
>> >    mods = ~ 0 + outcome,
>> >    struct = "UN",
>> >    random = ~ outcome|paper_id,
>> >    method = "FE",
>> >    data = data)
>> >```
>> >Of course, the struct argument is no more relevant (as the warning
>> >message said) but I'm wondering if the result is what I'm looking for
>> >because from the rma.mv documentation the method = "FE" is not
>> >mentioned combined with a multivariate parametrization.
>> >Thank you!
>> >
>> >--
>> >Filippo Gambarota
>> >PhD Student - University of Padova
>> >Department of Developmental and Social Psychology
>> >Website: filippogambarota.netlify.app
>> >Research Group: Colab   Psicostat