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

[David Pedrosa]

Sorry Wolfgang for not being clear. I was wondering if it makes sense to 
estimate variance-covariance-matrices for the level "category" as I was 
not sure whether this level is independent or not (altough most studies 
look at different subjects, the interventions within distinct 
"categories" may be very different and therefore distinct variance has 
to be assumed). My idea was to estimate vcov for the level "category" 
and include it in the model as input for V, although I am inclined to 
think that including catregory as random factor may account for this 
variability already. Is that correct?

Best,

David


Am 28.03.2022 um 12:08 schrieb Viechtbauer, Wolfgang (SP):
> 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?
>
> Best,
> Wolfgang
>
>> -----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,
>>
>> David
>>
>> --
>>
>> ;
>> <http://www.ukgm.de>;
>>
>> 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
>>
>> ;
>> Adresse: Baldingerstr., 35043 Marburg
>>
>> Web:https://www.ukgm.de/ugm_2/deu/umr_neu/index.html
-- 

;
<http://www.ukgm.de>;

	
	
	
	
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

;
Adresse: Baldingerstr., 35043 Marburg

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


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