What is CHANNEL, and what role does it play in your study? The term is in your model but not in the description of your study (which otherwise is quite helpful).   What is it that you want to know about the effect of CYCLETIME? 75 levels is a lot to sort out in an ANOVA model. (Your current code specifies a mixed-model ANOVA.)   ... View more

Here it is the factor you identified as TIME in your original post. I don't know the "blue or not blue" rule for the code inset. ... View more

Then I think something like this code might be appropriate, where CTIME = TIME   proc glimmix data=long; class trial id ctime; model y = trial | time / solution ddfm=kr2; random intercept / subject=id; random intercept time / subject=trial*id type=vc; random ctime / subject=trial*id type=sp(pow)(time) residual; output out=long_out pred=pred pred(noblup)=predpa; run; /* Observed and predicted (BLUP) for each trial for each id */ proc sgpanel data=long_out; panelby id; scatter x=time y=y / group=trial; series x=time y=pred / group=trial; run; /* Observed and predicted (BLUE) for each trial */ proc sgpanel data=long_out; panelby trial; series x=time y=y / group=id lineattrs=(pattern=2 color=red); series x=time y=predpa / lineattrs=(pattern=1 color=black); run;   Consider the statement   random intercept time / subject=trial*id type=vc; You are defining a 2 x 2 covariance matrix here. With TYPE=VC, it estimates (1) the variance among intercepts, and (2) the variance among slopes. With TYPE=UN, it estimates (1) the variance among intercepts, (2) the variance among slopes and (3) the covariance among intercepts and slopes. Because it is just a 2 x 2 matrix, TYPE=SP(POW)(c-list) makes no sense here. Be sure that you have an adequate understanding of random coefficient models. https://www.sas.com/store/books/categories/usage-and-reference/sas-for-mixed-models-second-edition/prodBK_59882_en.html by Littell et al. has a good chapter on the topic. ... View more

Why did you use random time intervals? How many time intervals do you have for each trial for each subject? Do you have the same number of time observations for each trial for each subject? Does the response Y have a linear relationship with TIME?     ... View more

Your treatment design is sometimes called a "factorial with control". Use your favorite search engine to learn more.   One approach to analysis of this sort of treatment design is to fit a one-way model (in your case, the one factor would have 13 levels) and then use contrasts to extract the pertinent tests of main effects and interactions. Writing the contrasts is much easier if you take advantage of the LSMESTIMATE statement in GLIMMIX--but it's still a challenge and easy to get wrong.   A slick trick that avoids writing contrasts is found here http://www.stat.ncsu.edu/people/dickey/courses/st512/lab10/factctrl.html for which I thank David Dickey at NCSU.   In the attached code, I've modified Dickey's code to add a blocking factor, switch to GLIMMIX, and compare 3 different statistical models (including both Dickey's approach and the one-way/contrast approach). The example data assume a normal distribution (rather than binomial), and the factorial treatment structure is different than yours (the example is a 2x2x3, and you have a 4x3). See how far this takes you!   Regarding negative estimates: You guess correctly. The values in the "Estimate" and "Standard Error" columns are on the link scale. Another lovely advantage of GLIMMIX is that it will report statistics on the inverse link scale (here, proportions) in the "Mean" and "Standard Error Mean" columns. ... View more

You did not answer whether you have 16 (4 treatments x 4 hours) pieces of tissue from the same animal, or whether it is appropriate to assume that each of the 16 pieces is statistically independent of the others (for example, each is in its own container), or whether all pieces are run at the same time.  It's not possible to determine an appropriate model structure without this information.   Your code is helpful, and I believe it illustrates that you may not yet have the correct vision of the model. Currently, you are analyzing the effects of GRUPO and HORAS separately; I would think a two-way factorial (GRUPO and HORAS jointly) model would be more appropriate, but that determination actually depends upon your research questions.   The data set that you provided does not include the "Totais" variable for each cell type; those variables are required if you want to use the binomial distribution for counts (of alive, dead, and hence total) clustered by tissue sample; for clustered counts, you'll need to use the events/trials syntax, where VIVOS is events and TOTAIS is trials; here is the documentation link http://documentation.sas.com/?docsetId=statug&docsetVersion=14.2&docsetTarget=statug_glimmix_syntax15.htm&locale=en.       ... View more

Hmm. If TRIAL is the factor for pre/post (in other words, if TRIAL has two levels, one for pre and one for post), then what is TIME?   ... View more

Does this mean that you have 16 (4 treatments x 4 hours) pieces of tissue from the same animal? Is it appropriate to assume that each of the 16 pieces is statistically independent of the others (for example, each is in its own container)? Are all pieces being run at the same time?   Do you plan to do a separate analysis for each cell type?   Have you tried to write GLIMMIX code? If so, show us how far you've gotten. (If you haven't started to learn how to do this sort of model, you won't understand what anyone in the Community might suggest.) ... View more

What is the TRIAL factor, and what role does it play in your study? ... View more

I'd say, it depends.   Do you have a research question about how blood pressure changes over time? Do you expect that mean blood pressure will change over time? Are different treatments applied to patients, and if so, might the time profile be affected by treatment?    Or are the multiple measurements merely subsamples? ... View more