[R-meta] weight in rmv metafor

[Viechtbauer, Wolfgang (SP)]

Dear Norman,

If you only used rma(), then this is not correct. rma.mv() with an appropriately specified model (plus clubSandwich::coef_test() if the working model is only an approximation and doesn't cover all dependencies) would be more appropriate.

Best,
Wolfgang

>-----Original Message-----
>From: Norman DAURELLE [mailto:norman.daurelle using agroparistech.fr]
>Sent: Thursday, 11 June, 2020 14:13
>To: r-sig-meta-analysis
>Cc: Viechtbauer, Wolfgang (SP)
>Subject: Re: [R-meta] weight in rmv metafor
>
>Hi all,
>
>I read this discussion and one question came to my mind : I also had some
>studies that contributed multiple effect sizes in the meta-analysis that I
>recently ran thanks to Dr Viechtbauer's advice.
>For now I only used the rma function, but should I have used rma.mv because
>of these stuides that had multiple effect sizes ?
>
>Thank you !
>
>Norman
>
>________________________________________
>De: "James Pustejovsky" <jepusto using gmail.com>
>À: "Wolfgang Viechtbauer" <wolfgang.viechtbauer using maastrichtuniversity.nl>
>Cc: "r-sig-meta-analysis" <r-sig-meta-analysis using r-project.org>, "Huang Wu"
><huang.wu using wmich.edu>
>Envoyé: Mercredi 10 Juin 2020 05:08:09
>Objet: Re: [R-meta] weight in rmv metafor
>
>Hi Huang,
>
>I've written up some notes that add a bit of further intuition to the
>discussion that Wolfgang provided. The main case that I focus on is a model
>that is just a meta-analysis (i.e., no predictors) and that includes random
>effects to capture both between-study and within-study heterogeneity. I
>also say a little bit about meta-regression models with only study-level
>predictors.
>
>https://www.jepusto.com/weighting-in-multivariate-meta-analysis/
>
>Best,
>James
>
>On Sun, Jun 7, 2020 at 4:11 PM Viechtbauer, Wolfgang (SP) <
>wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
>
>> Of course the weights "impact the estimated fixed effects". But whether
>> studies with multiple effect sizes tend to receive more weight depends on
>> various factors, including the variances of the random effects and the
>> sampling error (co)variances.
>>
>> A more detailed discussion around the way weighting works in rma.mv
>> models can be found here:
>>
>> http://www.metafor-project.org/doku.php/tips:weights_in_rma.mv_models
>>
>> Note that weights(res, type="rowsum") currently only works in the 'devel'
>> version of metafor, so follow
>> https://wviechtb.github.io/metafor/#installation if you want to reproduce
>> this part as well.
>>
>> I hope this clarifies things.
>>
>> Best,
>> Wolfgang