[R-meta] Why does rma.mv does not show the same results as robumeta?

[Cátia Ferreira De Oliveira]

Thank you for your quick response!
Is there any good source of information on which option would be the most
adequate for meta-analysis with dependencies, i.e. whether one should just
use a) rma.mv; b) rma.mv + robust() or clubSandwich() or c) robumeta?

Thank you!

Best wishes,

Catia

On Sun, 23 May 2021 at 17:34, Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:

> Dear Cátia,
>
> robumeta uses robust variance estimation. If you want to do the same based
> on an 'rma.mv' object, you need to use robust() or, even better, the
> clubSandwich package. See here for examples:
>
> https://wviechtb.github.io/metafor/reference/robust.html
>
> However, the results still won't be exactly the same. There is at least
> one post in the archives that discusses the somewhat subtle differences. If
> you go here:
>
>
> https://www.google.com/search?hl=EN&source=hp&q=site:https://stat.ethz.ch/pipermail/r-sig-meta-analysis
>
> you can add some appropriate search strings to find those posts (I believe
> it was James Pustejovksy that explained this quite thoroughly, so you might
> want to include 'James' in your search terms).
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: R-sig-meta-analysis [mailto:
> r-sig-meta-analysis-bounces using r-project.org] On
> >Behalf Of Cátia Ferreira De Oliveira
> >Sent: Sunday, 23 May, 2021 3:51
> >To: r-sig-meta-analysis using r-project.org
> >Subject: [R-meta] Why does rma.mv does not show the same results as
> robumeta?
> >
> >Hello,
> >
> >I have conducted a meta-analysis that I am currently analysing looking at
> the
> >relationship between memory and language/literacy and multiple studies
> contributed
> >more than one effect size. I have preregistered doing the analyses in
> robumeta.
> >But I am interested in checking how the results converge across packages
> as I am
> >tempted to use metafor for my next meta-analysis given how easy it is to
> plot,
> >check for publication bias, etc with this package. When running both
> models, they
> >produced different results and I am a bit unsure as to why they are
> different. I
> >know if I look at the estimates it is not that different, but what
> surprises me is
> >the fact that DD has a higher estimate in one model but in the other it
> is the DLD
> >group. Maybe I have done something wrong. Does anyone have any thoughts?
> >
> ># multilevel model looking at the relationship between memory and
> >language/literacy;
> ># multiple studies have contributed multiple effect sizes
> >
> >head(Data)
> >
> >rma.model <- rma.mv(yi, vi,  mods =  ~ factor(Group)-1,  random= ~ 1 |
> >Study/effectsizeID, data=Data)
> >res
> >
> >Multivariate Meta-Analysis Model (k = 414; method: REML)
> >
> >  logLik  Deviance       AIC       BIC      AICc
> >-13.0662   26.1323   36.1323   56.2253   36.2805
> >
> >Variance Components:
> >
> >            estim    sqrt  nlvls  fixed              factor
> >sigma^2.1  0.0109  0.1044     37     no               Study
> >sigma^2.2  0.0082  0.0903    414     no  Study/effectsizeID
> >
> >Test for Residual Heterogeneity:
> >QE(df = 411) = 588.9613, p-val < .0001
> >
> >Test of Moderators (coefficients 1:3):
> >QM(df = 3) = 11.1370, p-val = 0.0110
> >
> >Model Results:
> >
> >robu.model <- robu(formula = yi ~ factor(Group)-1, data = Data,
> >                       studynum = Study, var.eff.size = vi,
> >                       rho = .8, small = TRUE)
> >print(robu.model)
> >
> >RVE: Correlated Effects Model with Small-Sample Corrections
> >
> >Model: yi ~ factor(Group) - 1
> >
> >Number of studies = 37
> >Number of outcomes = 414 (min = 1 , mean = 11.2 , median = 6 , max = 52 )
> >Rho = 0.8
> >I.sq = 52.35398
> >Tau.sq = 0.02918897
> >
> >Thank you!
> >
> >Best wishes,
> >
> >Catia
>


-- 
Cátia Margarida Ferreira de Oliveira
Psychology PhD Student
Department of Psychology, Room B214
University of York, YO10 5DD

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