Hi Everyone,
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!
Yeah, it’s time dependent variable, it would likely have dependencies. I bet the age distribution in your groups are not equivalent.
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
Join us for SAS Innovate 2025, our biggest and most exciting global event of the year, in Orlando, FL, from May 6-9. Sign up by March 14 for just $795.
ANOVA, or Analysis Of Variance, is used to compare the averages or means of two or more populations to better understand how they differ. Watch this tutorial for more.
Find more tutorials on the SAS Users YouTube channel.