I may be a little unclear on this, but I would try using a generalized linear model that captured the pairing design.  Perhaps something like: proc glimmix; class group pair; model nlevel = group / solution dist=multinomial; random int / subject=pair; run; Without knowing exactly what the data look like, this is as close as I can come up with for paired multinomial data. Steve Denham ... View more

Not sure this will give what you want, but it might lead to it: proc genmod data=health; class T; model Status = T W/dist=multinomial link=clogit type3 aggregate=T; estimate 'P(low|T1)' intercept 1 T 1 0 0 / exp e; estimate 'P(mid-low, low|T1) intercept 0 1  T 1 0 0 / exp e; estimate 'P(med, mid-low, low|T1) intercept 0 0 1  T 1 0 0 / exp e; <put more estimates in here, as needed, following these examples> ods output estimates=estimates; run; I based it on the cumulative logit.  There would have to be a post-processing step on the estimates dataset, as the mean estimates are the cumulative probabilities.  The estimated probabilities per level would have to be calculated by difference, with the probability of the 'high' level as 1 - P(med-high, med, mid-low, low|T1).  Unfortunately, this will not give standard errors for the probabilities, but if you only need point estimates, this might be an approach you could use. Steve Denham ... View more