I'm trying to do a valid question in the forum and won't let me
Link to post:
(Help with addressing colliniarity in a cox proportional model?)
by RafaelZS
https://community.jmp.com/t5/Discussions/Help-with-addressing-colliniarity-in-a-cox-proportional-model/m-p/871715#M103574
I need help to better understand and address the following issue. I'm building a model to predict the development of cardiovascular disease in a cohort. I’ve included two variables that are strongly correlated with each other (R² = 0.83). When I include both in the model, one of them flips direction—it becomes protective instead of a risk factor. But when I include each variable separately, both show a positive association with disease, as expected. I don't think this is due to a biological explanation—it seems more like a multicollinearity issue. I know I could use either VIF or PCA to address this. But since my outcome is binary, I’m unsure how to correctly calculate VIF. I tried running a least squares regression between the two variables and got a VIF of 1, but I don’t think that’s the right approach here. Has anyone dealt with a similar situation? How would you recommend addressing this?
This message has 0 replies
