I am working on a large study of bovine coronavirus (BCoV) in dairies. I have sampled around 125 dairies around Europe, and I have taken samples from calves for virus isolation (5-20 calves per dairy = 1300 samples). I want to determine risk factors for a calf being positive for BCoV and have thus developed logistic models in genmod and glimmix.In this example, the predictive variable is if the cows are vaccinated against BCoV. I have previously worked with logistic GEE models in proc genmod, and now want to understand glimmix a bit better.
I have run univariate of all variables (100+ variables), and thereafter I ran a univariate with all models in genmod with repeated measures on farm.
title2 'NEONATAL calves- % virus+'; title3 'Genmod: GEE univariate analysis, marginal'; proc genmod data=neonatal_calf desc ; class farm country BCoV_dam_vacc(ref='no') / param=ref; model virus_fn = BCoV_dam_vacc /alpha = 0.05 link=logit dist=bin; repeated subject = farm; run;
When I run this conditional model in glimmix I get similar very non-significant results:
Title3 'NEONATAL calves- % virus+'; title4 'Glimmix - population average, marginal'; proc glimmix data=neonatal_calf ic=q or noitprint ; nloptions maxiter = 100; class farm country BCoV_dam_vacc(ref='no') ; model virus_fn(descending) = BCoV_dam_vacc /distribution = binary link = logit alpha=0.05; random intercept / subject=farm; covtest /wald; run;
However, when I run this marginal model I get the outcomes to be much more significant.
random _residual_ / subject=farm SOLUTION CL type=vc;
I am a bit confused about the most appropriate model, and how to code the random effect of farm.
When i look at the distribution of BCoV positive calves (events) in the farms, there are 58% of the farms where I do not find any positive calves.
Then i tried another model approach with looking at the proportion of calves positive in a farm:
proc glimmix data=neonatal_calf; class farm country BCoV_dam_vacc(ref='no'); model NCBCoV/NCtotal =BCoV_dam_vacc / solution; random farm; run;
Then the model specifies:
Number of Observations Read 1338 Number of Observations Used 1138 Number of Events 4327 Number of Trials 16974
Why do I get some many events and trials from the 1338 calves in the study?
I am grateful for any advice and tips to move this analysis forward.
Kind regards, Cat.
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