Reinaldo,
When there are complex effects, I typically recommend finding delta, the difference of the measured response minus the mean of fixed effects and time. To visualize these effects, create a column Mean Effect using the formula,
Col Mean( :Y, :Treatment, :Time )
Then create the column delta, Y - Mean effect. Next call up the Variability chart and plot Y, Delta and Mean Effect by Treatment, Time, etc. This provides a visual representation of the variability. Next combine columns representing the effects, call the new column Effects. Now perform a Oneway Analysis of Variance and test for Unequal Variances.
Many significance tests, especially for small sample group sizes or very large group sizes, might not flag or over flag. If there are at least 5 to 10 per group, look at the ratio of the max stdev to the min stdev and if the ratio is more than 3 then it is showing unequal variance with 11-20 if the ratio is more than 2 and no outliers, then varainces are likely unequal.
JMP has a rich repeated measures example data table Cholesterol Stacked.jmp. I modified it per these suggestions and added two scripts prefixed by gzm: gzm - Variability and gzm - Oneway. This study is looking at the effects of two (A and B) cholesterol lowering drugs vs. a control and a placebo. See attached file. Note the Levene's test in the report.
As you might expect those getting an effective treatment will have more variation than those that are not (a person with higher cholesterol to begin with might have bigger drop, and drugs do have the same effect on all).
The cholesterol data is also used for a Manova analysis with tests for unequal covariance (sphericity tests).