2 weeks ago
Among certain cancer patients, I am running a cox model to examine Effect of Treatment Era (year of cancer diagnosis) on Hazard of developing cardiovascular disease (CVD) accounting for Competing Risk of Death. Actually, it is the Fine-Gray model.
The problem is that Treatment Era (year of cancer diagnosis) violated the PH assumption. Since it is our main interest variable, we can not stratify by it. I have checked the interaction between treatment Era (year of cancer diagnosis) and chemotherapy/ radiation, none of them is significant. The cumulative incidence of CVD between 1990-2007 is higher than 2008-2014 for sure.
So my question is for the Fine-Gray model, does PH assumption matter? Can I ignore it? If we can not ignore, anything else can I do?
I feel the year of cancer diagnosis is kind of time variable...
Any suggestion will be appreciated! Thanks a lot!
2 weeks ago
Yeah, it’s time dependent variable, it would likely have dependencies. I bet the age distribution in your groups are not equivalent.
2 weeks ago - last edited 2 weeks ago
a more powerful analysis could be a parametric, joint frailty model. Rogers and Pocock compare alternatives and declare the joint frailty to be a superior option, although they consider the case of recurrent events (which you don't have): https://www.ncbi.nlm.nih.gov/pubmed/24453096