-----Original Message-----
From: C?tia Ferreira De Oliveira [mailto:cmfo500 at york.ac.uk]
Sent: Sunday, 23 May, 2021 19:54
To: Viechtbauer, Wolfgang (SP)
Cc: r-sig-meta-analysis at r-project.org
Subject: Re: [R-meta] Why does rma.mv does not show the same results as robumeta?
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 at maastrichtuniversity.nl> wrote:
Dear C?tia,
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/pipermai
l/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 at r-project.org] On
Behalf Of C?tia Ferreira De Oliveira
Sent: Sunday, 23 May, 2021 3:51
To: r-sig-meta-analysis at 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
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
the fact that DD has a higher estimate in one model but in the other it is the
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