I spent a bit more time going over this but still am not sure about the design and number of levels within some of the factors. So lets start back at the first model you proposed, so that we can get through some thing that might help:   proc mixed data=dataset1; class subj cond time measure site area; model DV = cond|time|measure|P1|P2; *random intercept /subject=area; *random intercept /subject=site(area); /since site are nested within area*/ /* These two statements could be reduced to a single statement. Since they are equivalent to random area; and random site*area; you could get by with the single statement: */ random intercept site/subject=area type=cs; *repeated / subject=subj type=cs; /*For the REPEATED statement make sure that subj is completely unique, such that there are no subjects with the same identifier at the various sites. Also, since the various measures are found on all subjects, you should consider the following REPEATED statement*/ repeated time/subject=subj*measure type=cs; This REPEATED statement is a sort of kludgy way of getting away from the Kronecker product approach. It depends on measure effect on the dependent variable being relatively homogeneous (i.e., only a level effect, and that is independent of the other terms in the model).   There are ways around some of the seemingly unending times to completion, but I am not sure if they are all available in SAS Studio. You might want to consider moving to GLIMMIX where you can use the NLOPTIONS statement to tune the iterative algorithm.   SteveDenham   ... View more

Please try defining logfirm = log(Firmness) in a DATA step. To get identical results strikes me as kinda suss, so just for now try comparing variables with different names. I like your selection of a gamma distribution. Now, to get an answer to "a 1-week increase in x is associated with ___ decrease in Firmness", you need to fit a first degree linear model (y = int + slope * x). Just about any other equation will need some extra interpretation. Since the fit is not a straight line, the decrease per week depends on which weekly interval you include. Given the figure you showed earlier, the decrease going from Week 0 to Week 1 is nearly zero, while the decrease going from Week 4 to Week 5 is probably greater than 10. Once you start going to non-normal distributions, this becomes a common issue, as almost every standard link is non-linear. Consequently, a figure is almost always a better way to convey change in response per unit change in the X variable.   Also, taking the log of your response and then fitting that is the equivalent of a lognormal distribution. Be aware that the simple back transformation (exp) gives the geometric mean, not the expected value on the original scale, and exponentiating the slope coefficient does not give the varying slope seen on the original scale. Thus, rethink your objective summary statement so that it reflects the non-linearity of the response curve on the original scale.   SteveDenham ... View more