I've got a dataset that follows patients who underwent different treatment options for aneurysms. They can have more than one aneurysm and each may be treated differently.
So I have variables like:
treatment3, where 1, 2 and 3 are different treatments.
size3 where the numbers identify which aneurysm, this follows:
location2, etc. So
size1 are connected, ie. it's aneurysm "number one" that has a specific location and size.
Then we also have adverse effect per aneurysm so
I'm interested to see if aneurysm size, location and treatment option are correlated with outcome (adverse effect).
I've thought about model selection and perhaps using a mixed model would be the best here? How would you approach such a data structure?
EDIT: I believe I have the data formatted as well as I can. I have created variables explaining size and location for each aneurysm, but I'm not sure where to go from here. Let's assume you want to know, from this data, whether location of aneurysms is correlated with size. How would you about doing that? Normally I would regress size vs location but these are 5 sizes and 5 locations, one for each aneurysm.