Looking for some interpretation help with a GLIMMIX output (as most the documentation is from logistic models).
The very basic bivariable model is as follows:
proc glimmix data=have noclprint noitprint method=quad gradient ;
class study_id ;
model score=race / solution dist=mult link=clogit ;
random intercept / type=un subject=study_id ;
covtest 'Need Random Intercept?' 0 ;
where race ^in(.,88,99) ;
run ;
I have 5500 observations, with a total of 1795 individuals (study_id) observed over time. Participants have multiple scores (range: 0-4) over time. I have made no assumption about the distance between adjacent levels, but they are ordered (i.e. 0=not present, 4=very bad levels present). Race is ethnic race (i.e. caucasian, African-American etc) with 5 levels. I am modeling the probabilities of levels of score having lower ordered values in the response profile table.
Once run, I have 4 intercepts (0-3), and 5 estimates for race. Numbers below are just made up.
Effect Score Race Estimate
Intercept 0 0.5
Intercept 1 0.6
Intercept 2 0.7
Intercept 3 0.8
Race Race1 0.09
Race Race 2 0.1
Race Race3 0.11
Race Race4 0.12
Race Race5 0.13
I would like to be able to interpret this (eventually) in a multivariable model as well, then present it in a logical and meaningful way. Any direction would be appreciated.
