Hi folks,
I'm really hoping someone can help me with this one, because I've been wracking my brain for about 3 days now. I'm trying to compare two variables (Tx1, Tx2; continuous data) within trials to see if they are different. There are two other variables that play into each trial, Subject and Donor. The Subject and Donor were not randomly chosen, and as such, I need to correct for that because it violates the assumption of nonindependence. At first I thought I should use SAS MIXED procedure with two random effects (subject and donor). Not only am I not sure that I'm setting up the data and model correctly for that type of analysis, but I'm also worried because the residuals are not normally distributed (which is a requirement of the model.) I also gave SAS GLIMMIX procedure a try, but I didn't have any success there. The problem with this data is that it doesn't fit any distribution. I've tried transforming it to no avail. It is very skewed.
In short, when I do a nonparametric matched pairs test (Wilcoxon rank-sum test), I get significant results. That's great, except that I'm worried about going forward with those results without correcting for the violations of independence. So, does anyone know how I can do a nonparametric matched pairs test comparing Tx1 to Tx2 while taking into account the nonindependence of the subject and donor?
Thanks in advance for any suggestions, and here is a little visualization of the layout I have the data in...
Trial Subject Donor Tx1 Tx2
1 Sub1 Don1 # #
2 Sub1 Don2 # #
3 Sub2 Don1 # #
4 Sub3 Don2 # #
5 Sub3 Don3 # #
6 Sub3 Don4 # #
etc...