You can reparameterize the model to easily get what you want.
Let y be response.
Let a be the categorical predictor with 3 levels.
Let x be the continuous predictor.
In this parameterization, the interaction a*x gives you a test of whether slopes are equal:
proc glimmix data=your_data;
class a;
model y = a x a*x / solution;
run;
This parameterization reports the intercept and the slope estimates for the linear regression each level of a:
proc glimmix data=your_data;
class a;
model y = a a*x / noint solution;
/* Pairwise comparison among slopes, with stepdown Bonferroni adjustment */
estimate "Slope A1 versus A2" a*x 1 -1 0,
"Slope A1 versus A3" a*x 1 0 -1,
"Slope A2 versus A3" a*x 0 1 -1
/ adjust=bon stepdown;
run;
Check the GLIMMIX documentation for details, including ADJUST= alternatives. I haven't tested this code so there could be syntax errors.
The book by Milliken and Johnson (Analysis of Messy Data, Vol III: Analysis of Covariance) covers ANCOVA extensively. I think there's also info in Littell et al. (SAS System for Mixed Models, 2nd ed).
Have fun!
Susan
In the second parameterization, the fixed-effects solutions will report tests of whether estimates are zero--in other words, whether each intercept or slope is equal to zero.
Message was edited by: Susan