[R-meta] weight in rmv metafor

Dear all, Dr Viechtbauer, Dr Del Ponte,thank you for your answers ! I will look into what you advised me to read, and also go and read more of the archive.If I have more questions I will come back and ask them.Once again, thank you for developing the package metafor in R Dr Viechtbauer, and for creating this mailing list.Have a nice week-end,Norman
----- Mail d'origine -----
De: Emerson Del Ponte <delponte at ufv.br>
?: Norman DAURELLE <norman.daurelle at agroparistech.fr>
Cc: Wolfgang Viechtbauer <wolfgang.viechtbauer at maastrichtuniversity.nl>, r-sig-meta-analysis <r-sig-meta-analysis at r-project.org>
Envoy?: Thu, 11 Jun 2020 15:43:05 +0200 (CEST)
Objet: Re: [R-meta] weight in rmv metafor

Dear Norman
You may want to check reproducible examples of my previous work on this exact application context as a starting point. 
https://emdelponte.github.io/paper-white-mold-meta-analysis/
https://emdelponte.github.io/paper-FHB-yield-loss/code_meta_analysis.html
Emerson

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 at maastrichtuniversity.nl> 

?: "Norman DAURELLE" <norman.daurelle at agroparistech.fr>, "r-sig-meta-analysis" <r-sig-meta-analysis at 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 at 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 at gmail.com> 

?: "Wolfgang Viechtbauer" <wolfgang.viechtbauer at maastrichtuniversity.nl> 

Cc: "r-sig-meta-analysis" <r-sig-meta-analysis at r-project.org>, "Huang Wu" 

<huang.wu at 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 at 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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Emerson M. Del Ponte
Universidade Federal de Vi?osa, Brazil
Chair of the Graduate Studies in Plant PathologyEIC for Tropical Plant Pathology Co-Founder of Open Plant PathologyMy websites: Twitter | GitHub | Google Scholar | ResearchGateTel +55 31 36124830



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