Since you use ar(1) as a covariance structure, you implicitly ignore overdispersion.  What does an exploratory step of calculating means and variances at each time point show?  Are the means and variances approximately equal, both within and between timepoints?  If so, overdispersion is probably not an issue.  If the variances are consistently larger than the means, you may need to consider a negative binomial distribution.  If the variances look substantially different at the time points, then the ar(1) covariance structure may be inadequately fitting your results.  You may wish to consider moving to PROC GLIMMIX, which has a wider variety of possible variance-covariance structures.  In particular, you may need to consider type=arh(1), if your timepoints are evenly spaced, or sp(pow) if they are unevenly spaced. Try: proc glimmix data = nvissex ic=q; class ssex sname; model nvis = ssex/dist=poisson; random sname/residual subject=sname type=ar(1) /*or other structures*/; lsmeans ssex/diff cl ilink; run; Look at the information criteria, including the generalized chi-square/Df.  If this is extremely large, you have a good case for overdispersion. Good luck. Steve Denham ... View more