Random or repeated for PROC mixed

[stats2554]

I have a data set with 4 groups, 5 subjects per group, at 4 timepoints (1, 2, 3, 4 hours post treatment).  I am using proc mixed with the following model:

proc mixed data=Result method=reml;
class subject treatment time;
model result = treatment time treatment*time;
<repeated or random>
lsmeans treatment / adjust=dunnett;
run; quit;

I am wondering if there are any advantages to using a repeated or random statement?  Additionally, I have seen people use the same data set and include interactions for the random statement.  Is it appropriate if I were to include the following in my random statement?:

random subject subject*treatment subject*time

[StatsMan]

Sounds like a classic repeated measures. Seeing the data would help a bit more, but from what you describe a REPEATED statement would be the way to go. Use the TYPE= to specify the covariance structure you wish for the 4 repeated measures on each subject. If each subject received one and only one treatment, then you would not be able to estimate a SUBJECT*TREATMENT interaction. If you model TIME as a CLASS effect and have 1 observation per time point per subject, then you will not be able to model a SUBJECT*TIME interaction. It is possible to model TIME as a continuous effect and set up a random coefficients model for this data with

random int time / subject=subject

However, you get much more flexibility in the covariances with the REPEATED approach.