[R-meta] weight in rmv metafor

[Norman DAURELLE]

Thank you. 
I am not sure I understand exactly what you mean by " i f the working model is only an approximation and doesn't cover all dependencies ". 
Could you please explain it ? 

For now I used the rma() function to synthesize the available literature existing on the blackleg - oil seed rape disease-yield relationship, using slopes as effect-sizes. 
the models that gave me the slopes I used in the meta-analysis are all Y = a + bX, simple linear regressions with Y being the yield and X being the diseqse severity. 
So my slopes, b, are all negative, and I have obtained a "summary" effect size through the rma() function. 

But I indeed have two studies that for now contribute to most of the effect-sizes that are included in my meta-analysis. 

So why exactly is it necessary to use the rma.mv() function ? 
What exactly does the "multivariate" qualificative refer to ? 

Thank you, 
Norman. 


De: "Wolfgang Viechtbauer" <wolfgang.viechtbauer using maastrichtuniversity.nl> 
�: "Norman DAURELLE" <norman.daurelle using agroparistech.fr>, "r-sig-meta-analysis" <r-sig-meta-analysis using r-project.org> 
Envoy�: Jeudi 11 Juin 2020 22:34:55 
Objet: RE: [R-meta] weight in rmv metafor 

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> 
>�: "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> 
>Envoy�: 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 

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