I would like to use the Covariate Balancing Propensity Score (CBPS) method to adjust for confounding because of its optimization properties.
I am doing a Cox regression model on some observed survival data and I have age as a confounder. However, as expected the age covariate breaks the proportional hazards assumption (risk of death increases with age).
One approach I took was to break age into intervals and stratify the Cox model on those intervals (basically create a new Cox model for each interval). I checked the Cox PH assumption and the non-interaction assumption and it seemed that the stratified model fixed the problems.
I was able to use the CBPS method to calculate weights to then use in my stratified model. However, I am learning from Frank Harrell that this approach is is dangerous for many reasons. His preferred approach on continuous covariates such as age is to approximate it using a spline instead of breaking it up into intervals. If do this I am not sure I can implement the CBPS method anymore.
Should I abandon the CBPS method and calculate the propensity score using a less optimal method where I can incorporate splines? Thank you in advance for any insight!