no problem. i am just having a hard time visualizing your data structure. hopefully some of the other users on here can chime in. can you show some made up dummy data (something that shows your data structure)? ... View more

i believe the two statements above say that you have two randoms effects: a random intercept for each class and a random intercept for each student (who are nested in their respective classes) ... View more

would you be able to show us your data (or at least a subset of it)? ... View more

I tend to agree with DLing here. Usually the issue lies with as DLing said - poorly gathered and/or irrelevant data. You need to ask yourself if the data collected is pertenent to the hypothesis you are testing. If so then maybe the poor fitting model is a sign that the variables you collected are not influencial to your dependent variable which could be a finding in of itself. There have been many times where I have seen clinical protocols that try to ask questions to with which the data collected cannot even answer. ... View more

build a linear model y= intercept+x+trt +x*trt and then test the coefficient for x*trt to see if the slopes are zero. to test if both treatments have the same intercept then test the trt coefficient to see if it zero. ... View more

-chester using a random intercept for each subject is in fact analogous to using a compound symetry / exchangeable covariance structure. in essence specifying a random intercept says that the correlation between any pair of repeated measurement is the same regardless the distance between admission time. if you dont think this is true you could also specify a covariance structure for the error term: proc mixed data=xxx; class id admission; model treatment_length = admission; repeated admission / sub=id type=ar(1); /* or some other covariance structure */ random intercept / sub=id; run; essentially what this does is give you a hybrid covariance sturcture that says that the correlation between measurements are not constant but also do not decrease as fast as an ar(1) structure would have them do to answer your other question, sas does allow the same predictor to be used in the class, model, and repeated/random statement. the only caveat is if for example you had recorded the time points for each longitudinal measure and wanted to use the actual time values as a continuous predictor in your model, then you would have to create a new variable time2=time and put time2 in the class statement to be used with the repeated/random statement. maybe a better option (if you had the data) would be to use time that admission occured compared to the baseline time of the first admission. that way you would be able to exploit the time between admission. i would assume that some patients return to the hospital faster than others??? just a thought george ... View more

ah in that case do repeated time / type=ar(1) subject=biopsysite(pid) the subject=biopsysite(pid) specifies that each biopsysite is nested with each pid. you may also want to try random intercept / type=un subject=pid that way you are fitting a random intercept for each person as well as an ar(1) covariance structure for the time points this ultimately gives you a hybrid covariance structure. i can't think of the complete details off the top of my head (i'll have to reference back to my old school notes if you want more info) cheers ... View more

- ritam, just to clear things up the names of the variables actually dont matter. by using the "--" notation you are including everything from the var_1-var_n ... View more

i am thinking proc mixed data=test; class time trt pid biopsysite; model y=age time trt time*trt/s; random biopsysite /  type=cs subject=pid  g; repeated time / type=ar(1) subject=pid r; lsmeans trt*time / cl pdiff alpha=0.2; run; ... View more