Schoenfeld plots after restricted cubic spline I'm using a Cox model with 100k subjects and 1624 events to model the effects of a treatment with respect to 49 covariates, 127df.
Due to proportional hazard violation of some continuous variables, I've added restricted cubic splines using rcs(). Based on the p-values provided by cox.zph (which I've decided at 0.01 due to my sample size), the variables that previously violated the PH assumption no longer do.
However, it seems worth investigating this further instead of simply relying on the p-values in cox.zph. However, my understanding tells me that visualization of Schoenfeld plots after rcs() transformation has little value in doing this.
My questions thus are:

*

*How, then, would one investigate further if the PH assumption is truly satisfied after applying restricted cubic splines?


*Can this be visualized in a convenient way similar to how scaled Schoenfeld plots are used?

The R survival package's cox.zph function has a "terms" option to combine all the terms relating to one predictor into one sub-linear predictor.  Then you get an overall assessment of proportional hazards from the collapsed terms.  It's difficult to plot what's going on but you can compute terms with predict(..., type='terms') and refit the model with the collapsed variables for purposes of getting smoothed scaled Schoenfeld residual plots.  A related example may be found here.  cox.zph also gives you multiple degree of freedom "chunk" tests of proportional hazards for all the spline terms without creating collapsed variables, as shown in an example there.
