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I have a couple of general questions so bare with me.

I have three groups, patients, relatives and healthy controls (HC). I determined an effect between patients and HC, although that effect wasn't significant between HC and relatives. However, there's an obvious trend: the data for the relatives sit in between the two the way you expect them too. What kind of test I should run to see if there's a statistically significant trend, from patients to relatives to control? This test should come out negative if patients > relatives=control but positive if patients > relatives > control. I am guessing a one way ANOVA won't do cause it will test if any two means are different. Is regression with categorical variables the answer?

The second question is somehow related. I have several measurements from each subject of different but related things. I want to test if there are group differences between 2 groups in regards to those measurements. I am guessing a usual t-test won't do the job cause there's some dependence between the data collected (the measurements that come from each subject). Should I then do repeated measures ANOVA? The issue is that those measurements from every subject are also describing different things.

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Regarding your first question: Regression with categorical variables is the same as ANOVA. Ordinal independent variables are tricky - most methods treat them as either continuous or categorical, but neither is exactly right. PROC TRANSREG in SAS offers optimal monotone scoring, which is a good method. I am not sure if this is available in other packages, but it probably is.

Regarding your second question: You need to account for dependence in the data. However, RM ANOVA makes some unrealistic assumptions. I recommend a multilevel model.

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  • $\begingroup$ Thank you Peter. Any ideas how should I go about a multilevel model? $\endgroup$
    – G Ch
    Commented Sep 30, 2015 at 18:11
  • $\begingroup$ They are complicated models. You could either take a course or else hire a consultant. $\endgroup$
    – Peter Flom
    Commented Sep 30, 2015 at 20:00

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