In my project, I have many variables but a very small sample (non-parametric). I'm trying to prove a link between two variables while correcting for covariates. In short, we are looking at white matter tracts and their links to visuospatial function and quality of life (QoL). We are analyzing 3 tracts, which can either be normal, displaced or ruptured (ordinal) and we have 8 scores for visuospatial abilities. Let's define variables:

- Y1 = tract 1 integrity (3 categories)
- Y2 = tract 2 integrity (3 categories)
- Y3 = tract 3 integrity (3 categories)
- X1 to X8 = visuospatial test scores (quantitative discret)

- X9 = QoL score (quantitative discrete)
- X10 ... = age, gender, etc. (these covariates are not important for right now, I'll had them in my study latter)

There is a second part to the project!! Each patient undergo a surgery and we want to compare if the change in the integrity of the tract (Y) correlates with the change in visuospatial capabilities (X) pre and post surgery (so if a white matter tract is repaired, does visuospatial function return and conversely, if we disrupt a tract in surgery, does the visuospatial function decrease?). So it's like a repeated mesures (2 times), but I still have the same issue with Y2 and Y3 vs Y1 (if Y1 is repaired, but Y2 is not and Y3 is ruptured during surgery for example)... I was thinking about a generalized estimating equation (mixed model), but still not sure how to treat my variables.

Do you know which test to use? My independent variable is the integrity of the tract, but how do I treat the other tracts when looking at only one?

Thanks for your help!