Message-ID: <350edcad-80dd-6ff1-ca93-23be0374cd6f@staff.uni-marburg.de>
Date: 2022-03-28T08:02:16Z
From: David Pedrosa
Subject: [R-meta] Question on three-level meta-analysis
Dear list,
there is one question I have not been able to get my head around and
it's about whether if estimation of variance-covariance-matrices in a
nested/multlevel hierarchical model make sense. To put things in a
context, we have ~60 studies for which we could estimate a standardised
mean difference and these studies are with minor exceptions all
independent. Yet, there are 6 categories of interventions with something
between 2 and 30 studies nested within, so that we have individuals,
studies and category_of_intervention. We also added two moderators in
the model; quality of studies and whether it's a RCT or a NRCT which
resulted in the following:
res <- rma.mv(yi, vi,
?? ???? ??? ??? random = ~ 1 | category/study_id,
?? ???? ??? ??? mods= ~ qualsyst*factor(study_type),
?? ???? ??? ??? data=dat)
If there were studies in which some participants received different
treatments (i.e. many of them were not independent), I guess the
estimation of a different vcov should make sense. But I think it's
possibly only 3-5 studies within all 60 of them. So is it conceptually
correct to estimate the vcov for the level 'category' and stick it into
the model or is that already included as I use category as random
effect? I don't think it makes a huge difference, but I'm not sure about it.
Thanks for your help,
David
--
;
<http://www.ukgm.de>;
PD Dr. David Pedrosa
Leitender Oberarzt der Klinik f?r Neurologie,
Leiter der Sektion Bewegungsst?rungen, Universit?tsklinikum Gie?en und
Marburg
Tel.: (+49) 6421-58 65299 Fax: (+49) 6421-58 67055
;
Adresse: Baldingerstr., 35043 Marburg
Web: https://www.ukgm.de/ugm_2/deu/umr_neu/index.html
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