GEE vs. mixed-effects modeling for handling singletons in higher-level units
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