Hi. I have the following situation: The data:      there is onset of disease information (visit -2  -varies for each id)      Enrollment date (visit 0 -- varies for each id)      Follow-up visits (visit 1,2,3,4....so on, varies per ID)      marker of disease (my Y)      End of Study So far we have done analyses using visit 0 (enrollment) to last visit. We were able to find the slope (or rate of disease progression) using OLS: proc reg data = my_data;      by id;      model marker_of_disease = time; ods output ParameterEstimates = estimates; run; Some data manipulation and eventually we get a slope (rate) for each subject. (Then we use that slope to do other things). OK, now, it has been of interest to check if there is a significant difference between the slope (rate of disease progression) from onset of disease to enrollment (visit -2 to 0 ) versus the slope from enrollment to...the end. | |          * |            \   y_hat = intercept + b0*time |              \ |               0 |                 \ |                   \     y1_hat = intercept + b1*time     |                     \ __________________________________________________Time      -2      0    1    2    3    4     5    One way I solved this: proc reg data = my_data; where visit in (-2,0);      model y = time; output estimates1; run; Then proc reg data = my_data; where visit>= 0      model y = time; output estimates2; run; do some cleaning and manipulation and then do a t-test for differences in the slopes. (I am not sure if this is right) However, I may be able to implement proc mixed data = my_data;      class id;      model y = time group time*group/s;      random int time/subject =id; run; Where group is an indicator of time -2 to 0, vs time >= 0. My issue is how do I handle the 0 (enrollment visit) since it contributes to both groups, so to speak? Do I create a duplicate record...which seems fishy... Is it the t-test fine/similar for such analysis? Any suggestions would be greatly appreciated. Thanks! Cheers! ... View more