# How to compare multiple groups of unblanced repeated measures non normal data?

I'm trying to compare three groups data. But the data set is about a new drug trial. The data set has these characteristics:

1. Follow-up set. That is, after administration of the drug, a series of parameters were collected in the following date, day0, day1,day2,...,day28.
2. Unbalanced set. Because some patients died and some recovered, not all patients were followed to 28 days; for instance, some were followed for only 20 days.
3. Non normally distributed parameter. Especially, day25-day28, sometimes sd >> mean.

So, I try to use the linear mixed-effect model with group, time and group*time for the comparison. ANOVA is not suit for the unbalanced set.

However, I wonder if it's correct model for non normal data set? Or is there another method suited for this kind of set?

I use SPSS 19. Some of my friends use STATA or JMP.

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Can you clarify your concern regarding the non-normality? Are you worried that the raw response data aren't normal, or that the residuals aren't? (NB only the residuals matter.) Is it that the response is categorical (eg, 0 or 1), or a count? What do you mean by the parameter being non-normally distributed? – gung Oct 11 '12 at 16:36
Independent variable is IL-6 while factors are group (control, drug1, drug2) and time (0,1,2,3,...,28). Raw data of IL-6, at day0-day7, are normal while at day28, mean+/-sd is 47.2 ± 53.1. Obviously, skewness distribution. Like this, Linear Mixed Model is OK for it? Thanks a lot! – user14866 Oct 11 '12 at 18:23
That's helpful, @user14866. Do I understand correctly that your concern is that the raw response data are positively skewed towards the end of the study? – gung Oct 11 '12 at 18:54
Sorry, accurately, I should say that raw response data become non-normal by normality test. You may see it via mean<sd. I care if linear mixed effect model is suit for this kind of data or not. – user14866 Oct 11 '12 at 19:36
I think you should probably just ignore the normality test (see here: is-normality-testing-essentially-useless). Beyond that, is the situation here that your response variable can only be >0, &, since SD>mean, the raw response data are positively skewed towards the end of the study? Is that what you are worried about? Is that what motivates this question? – gung Oct 11 '12 at 20:04