-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
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