Steve, Thanks again for the help, but I'm still running into some issues. It looks like Proc glimmix doesn't include a joint option for lsmestimate statements. It does provide an FTEST option. Should I be using this? I've been scouring the web all morning for examples of how to use this with my code, and have totally drawn a blank. I've copied a bit of what have below: proc glimmix data=in;      class mouse treatment day;      model score = treatment*day;      random day / subject=Mouse group=treatment;      lsmestimate treatment*day 'treatment1 vs treatment2'           .125 .125 .125 .125 .125 .125 .125 .125           -.071 -.071 -.071 -.071 -.071 -.071 -.071 -.071 -.071 -.071 -.071 -.071 -.071 -.071           0 0 0 0 0 0 0 0 0 0 0 0 0 0           0 0 0 0 0 0 0 0 0 0 0 0 0 0           0 0 0 0 0 0 0 0 0 0 0 0 0 0,      lsmestimate treatment*day 'treatment1 vs treatment3'           .125 .125 .125 .125 .125 .125 .125 .125           0 0 0 0 0 0 0 0 0 0 0 0 0 0           -.071 -.071 -.071 -.071 -.071 -.071 -.071 -.071 -.071 -.071 -.071 -.071 -.071 -.071           0 0 0 0 0 0 0 0 0 0 0 0 0 0           0 0 0 0 0 0 0 0 0 0 0 0 0 0; run; I have a few more lsmestimate statements included in the full code, but they pretty much follow the examples I provided. Note, Group 1 only has 8 'days', while the other groups have 14, hence the differential weighting. I'm also correcting for multiple comparisons as you suggested, but using proc multtest in a separate step. So, given what I've provided, how would the FTEST option (if that's correct) fit into my LSMESTIMATE statements as written? Do I need a new LSMESTIMATE statement that will use the FTEST option? If so, how do I weight the individual cells to give me appropriate weights. I'm used to one cell being positive and the other negative, but that doesn't seem right in the case of the FTEST/JOINT option. Sorry to belabor the issues I'm having, but I simply can't find useful examples of this online, and thus have to resort back to the forum for help. Thanks. ... View more

I've finally gotten around to finishing up this project and thanks to the earlier help everyone here provided, I feel like I'm good to go. I have a few follow-up questions. Remember, I'm fairly out of my league here both in terms of my understanding of mixed models as well as my comprehension of means models. Since some of my treatment groups are missing days entirely, can I safely assume there will always be a "treatment x Day" interaction? I'm worried about the case in which there is a main effect of one or the other, but no interaction. I can't see obvious way to report a main effect using this method. Do I need to correct for multiple comparisons of all my lsestimates statements? I'm used to an ANOVA setting, where the DFs used by my posthocs are encumbered in the model itself, so no correction of posthocs is necessary. It looks like proc glimmix is using t-tests, so that would suggest I need to correct for multiple comparisons. Thoughts? How do I go about reporting these results in a manuscript? I'm used to reporting the fixed effects followed by the posthocs; in a means model should I be reporting the "Treatment*Day" F? What about in the case of #1 above, where there is no interaction, but there is a main effect? How do I report this? Thanks for the help! You guys are the best! ... View more

Ah! That makes sense!  Thanks.  I'm heading to the library right now.  It definitely seems like I need a better background in this, as I have no idea what I'm doing. In the mean time, I still can't make comparisons between the bad group and the other groups, even using the means model.  Using the "/solution" statement, I was able to see the order of the components of my ESTIMATE statement.  Using this order, I still get non-est for every comparison with this group.  I can accurately compare any other groups, but nothing works with this group.  Any idea why this may be the case? ... View more

Here's the code I'm using: proc mixed data=OND;      class Mouse Treatment Day;      model Running= Treatment*Day;      repeated Day / subject =Mouse; run; The Fixed effects results are: Effect                    NumDF     DenDF     F Value     Pr > F Treatment*Day          63          199          2.00          0.0002 I only say they're garbage because they disagreed so intensely with my prior results, which showed p=0.9827 for the interaction.  Is this an incorrect statement? Regarding the ESTIMATES, how do I know if days are being excluded from my model?  I assumed that SAS left those cells empty, but that they still existed for the group missing days.  Is this not the case?  Does it have fewer cells? ... View more

It sounds like the means model is the way to go.  I've never used the model in this manner before, so I have a couple of questions.  First off, I assume the F value from the fixed effects is no longer relevant.  It's giving me garbage anyways.  Next, Steve mentioned using LSESTIMATE, but unless I'm missing something, PROC MIXED can't do this.  So, it seems like I'll need to use ESTIMATE.  Given the fixed effects are garbage, do I need to specify the degrees of freedom for the ESTIMATE statement?  Since I'm only using the interaction factor, I'll need to include all 70 terms in my ESTIMATE statement, correct?  What would that make my DF?  Here's what I was considering for my ESTIMATE statement: ESTIMATE 'Group 1 vs Group 2' intercept 1 Treatment*Day .07 .07 .07 .07 .07 .07 .07 .07 .07 .07 .07 .07 .07 .07 -.07 -.07 -.07 -.07 -.07 -.07 -.07 -.07 -.07 -.07 -.07 -.07 -.07 -.07 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0; Does this seem reasonable?  I have 5 treatment groups (so five lines) and I got the .07 from the number of days I have data for (14).  The 0's may not be necessary in this case, but they help me keep things straight as I create more ESTIMATE statements.  I ran this, and it's still giving me "Non-est' for result.  Thoughts?  Thanks again for all the help. ... View more

Since I have one treatment group that ends the study early, it doesn't seem reasonable to assume my data points are missing at random.  The more I look into this, the more it seems like I need some sort of joint model to link the longitudinal quantitative measure with the survival data.  Does anyone have any experience with this in SAS?  I think I'm getting way out of my league here... ... View more

Ah!  I understand now.  Thanks for explaining it.  When I look at my results though, I see an effect of treatment but no effect of day or interaction of the two.  So, I'm need to estimate the effect of each of the five treatment groups.  If I just use the treatment*day term, can I still estimate the main effect of treatment using "estimate" statements? ... View more

I'm confused.  Perhaps I don't understand what you're suggesting.  I don't see how this is any different than what I've done.  Doesn't the vertical bar ( | ) imply the isolate terms as well as their interaction?  In other words, that writing "Treatment | Day" was identical to writing "Treatment Day Treatment*Day".  If my understanding is correct, could you clarify how your suggestion differs from my original coding?  I'm just trying to figure this out.  Thanks for your time and thoughts. ... View more