Dear Stata Users,
I am using gllamm to fit a logistic model to look at risk factors for a
disease of piglets where my outcome is whether an individual is diseased or
not (0/1) and piglets are naturally clustered within litters.
I know that cases of the disease are not distributed randomly among litters
and it appears that some litters are more heavily affected than others. This
could be due to factors affecting individual litters or may be due to
maternal or paternal effects (sows had up to 5 litters during the period I'm
dealing with and boars sired up to 60). Hence I would like to fit and
compare three models:
1) random effects for litter
2) random effects for sow
3) random effects for boar
All explanatory variables (my risk factors) are the same in the models, its
only the random effects I want to vary. I am hoping that by comparing the
models I will be able to get an idea if piglets from certain sows/boars more
likely to develop the disease. The data set is very large (~13,000 pigs in
1436 litters from 704sows and 123 boars) so can't reliably include sow or
boar as a seperate risk factor in model.
My question is how do I compare the models and find which one best explains
the clustering. lrtest doesnt work because the df are different.
Many thanks in advance
Lucy
==============================
Dr. Lucy Snow
Veterinary Epidemiologist
Centre for Epidemiology & Risk Analysis
Veterinary Laboratories Agency
New Haw, Addlestone
KT15 3NB
UK
Email: [email protected]
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