There are much bigger problem in this model than overdispersion. As mentioned, overdispersion is not a problem at all.   The estimates from this model will unfortunately be complete meaningless! The problem is that age, period and cohort is linearly dependent. If forexample you want period estimates, then you can not either identify the an intercept or a linear trend. The intercept may not be a problem as you may want to estimate the difference between periods. But the linear trend is a bigger problem.   It is rather hard to solve the problem. What you should do is to make a projection of the column vectors in your design matrix that comes from cohort effect (still assuming you want period estimates). Then projecting these collumns into the orthogonal space to the space spanned by the linear trend. You can then adjust for this "untrended" effect of cohort, and assume that the linear trend is only due to period effect (but not cohort effect) you will get period estimates that are adjusted for age and an "untrended" cohort effect. Proc IML may be a big help for you to make all the linear algebra that is neccessary.   reference: Age-period-cohort models for the Lexis diagram - Reply. / Carstensen, Bendix. In: Statistics in Medicine, Vol. 27, No. 9, 2008, p. 1561-1564 ... View more