[R-meta] Question on three-level meta-analysis

[David Pedrosa]

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


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PD Dr. David Pedrosa
Leitender Oberarzt der Klinik für Neurologie,
Leiter der Sektion Bewegungsstörungen, Universitätsklinikum Gießen und 
Marburg

Tel.: (+49) 6421-58 65299 Fax: (+49) 6421-58 67055

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Web: https://www.ukgm.de/ugm_2/deu/umr_neu/index.html


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