[R-sig-ME] GEE vs. mixed-effects modeling for handling singletons in higher-level units

[Kevin E. Thorpe]

On 04/07/2011 09:57 PM, Jeremy Koster wrote:
> A statistically-minded colleague recently commented that, in datasets
> that have singletons in higher-level units, Generalized Estimating
> Equations might give more accurate estimates of fixed effects than
> random-effects modeling.
>
> The conversation emerged from the observation that some
> (anthropological) datasets include households with only one member.
> That is, we have encountered datasets in which 250 households include
> more than one person whereas about 10-15 are single-person
> households.
>
> Are there pros (and cons) to either GEE or random-effects modeling in
> such cases?
>
> More generally, can anyone recommend references to literature on
> singletons in the context of mixed-effects modeling?  My impression
> is that it's still possible to specify random effects when some
> higher-level units include only one lower-level data point, but my
> colleague believes that it would be problematic.
>

I found this post interesting.  This is contrary to my understanding. 
Specifically, I understood that larger cluster/group sizes are needed 
for the GEE asymptotics.  Have I misunderstood?

-- 
Kevin E. Thorpe
Biostatistician/Trialist, Knowledge Translation Program
Assistant Professor, Dalla Lana School of Public Health
University of Toronto
email: kevin.thorpe at utoronto.ca  Tel: 416.864.5776  Fax: 416.864.3016