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

Viechtbauer, Wolfgang (SP) wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Mon Mar 28 12:08:16 CEST 2022

Dear David,

I don't quite understand your question. What variance-covariance-matrices are you referring to and how would you stick them into the model?


>-----Original Message-----
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On
>Behalf Of David Pedrosa
>Sent: Monday, 28 March, 2022 10:02
>To: r-sig-meta-analysis using r-project.org
>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,
>PD Dr. David Pedrosa
>Leitender Oberarzt der Klinik für Neurologie,
>Leiter der Sektion Bewegungsstörungen, Universitätsklinikum Gießen und
>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

More information about the R-sig-meta-analysis mailing list