Dears,   I am analyzing the results of subjects through the time. At each time we take two measurements from the subject that are correlated between them ( coded the two measurements in one variable X). The dependent variable is the change of results from first time point (chg). The predictors are the results from first time point (first), X, time and interaction between X and time. I want to take into account the correlation of the two measurements from X in the model. For that reason, I have the following 4 models. Note that models 1 and 2 have the same results. The same happens for models 3 and 4. My questions are, what is the difference between those 4 models (i.e: how could I explain the models to a non statistical audience)? why do I get same results in models 1 vs 2 and 3 vs 4? /*model 1*/ proc sort data= data; by subjid X time;run; Proc mixed data=data; Class subjid X time; Model chg=first X time X*time/ ddfm=kenwardroger solution; Repeated X(time)/subject=subjid type=CS ; LSmeans time*X; Run; /*model 2*/ proc sort data= data; by subjid X time;run; Proc mixed data=data; Class subjid X time; Model chg=first X time X*time/ ddfm=kenwardroger solution; Repeated time(X)/subject=subjid type=CS ; LSmeans time*X; Run; /*model 3*/ proc sort data= data; by X subjid time;run; Proc mixed data=data; Class subjid X time; Model chg=first X time X*time/ ddfm=kenwardroger solution; Repeated time/subject=subjid(X) type=CS ; LSmeans time*X; Run; /*model 4*/ proc sort data= data; by subjid X time;run; Proc mixed data=data; Class subjid X time; Model chg=first X time X*time/ ddfm=kenwardroger solution; Repeated time/subject=X(subjid) type=CS ; LSmeans time*X; Run; ... View more