Yes to both questions. Here are some links with example code:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2811235/ -> Look at sample code A5.
https://www4.stat.ncsu.edu/~dzhang2/epid766/766note.pdf -> Start on page 172. There is code for GLIMMIX that looks like this (minor changes are mine):
title "Random intercept model for infection disease data";
proc glimmix data=indon method=quad;
class id sex;
model infect = xero sinv age time sex height / dist=bin link=logit s;
random int / subject=id type=vc;
run;
If you wished to model time as a repeated measure effect, this code would work for the data in question (not presented in class notes). This maintains the conditional nature of the inference. A marginal model can be obtained by changing the method to rspl, and adding a residual option to the second RANDOM statement.
title "Random intercept model for infection disease data";
proc glimmix data=indon method=quad;
class id sex;
model infect = xero sinv age time sex height / dist=bin link=logit s;
random int / subject=id type=vc; <This statement might be removed for some of the covariance structures tested below (UN, CS, CSH) or left in for others (AR(1), ARH(1), ANTE(1))
random time/subject=id type= <Here you would have to pick from various types that model the correlation pattern of the data, see the GLIMMIX documentation>;
run;
These are for a binomial response. If you have an ordinal response, change the options in the MODEL statement to: dist=multinomial link=cumlogit s;
I suggest trying something like these with your data, and then if there are difficulties, come back with the code you are using and the log showing any pertinent ERRORS, WARNINGS and NOTES.
SteveDenham
