An analysis of covariance model might work, if what you want is to estimate the means at 15, 30, 45, 60, 90, and 120 minutes for a common value of baseline. (The baseline time=0 value would be the covariate.)   Because (presumably) of the repeated measures on a subject at 0, ..., 120 min, the model would be more complicated than your standard ANCOVA (which would have data for time=0 and time= a second value). In particular, the relationship between data at time 15 and time 0 might be stronger than the relationship between data at time 120 and time 0 because noise intrudes as time passes--in other words, the slope of the regression of the response on baseline (time=0) might decrease with later times.   This is speculative and untested, but I would consider proc glimmix data=have; class time subjectID; model response = time baseline time*baseline; random time / subject=subjectID type=<some covariance structure, maybe ar(1)> residual; lsmeans time / at mean; /* or some other value of baseline */ run; Issues to consider are -- the nature of the relationship between response and baseline at each time (e.g., linear) -- an appropriate covariance structure for the repeated measures within a subject -- normality and homogeneity of variance (assuming normal distribution) -- what a sensible "common" value for baseline might be, in context   Alternatively, you could compute a variable that represents deviance from baseline, either absolute (i.e., subtract the baseline value) or relative (e.g., divide by the baseline value). Or maybe a random coefficients model.  ... View more