Survival analysis proportional hazards violation I've been using Cox for survival analysis with the goal of identifying the effect of 5 different levels of a treatment on survival. My dataset is not small and has 130 covariates after dummy encoding.
About 15 of these - including one level of my treatment - violate the PH assumption.
I think in my situation the best extended Cox option would be adding time interactions to those that violate the PH assumption meaningfully. Another option, of course, could be an AFT, if the assumptions there are also satisfied.
My question is: If I were to try each of these approaches, how could I compare which these are preferred given my objective. That is, how would I know which is better? It's not like I'm making a prediction about survival in which case I could use CI or something more precise. Thus, comparing which is best and knowing how to know this is not obvious to me.

The most rational and linear approach would be to put Bayesian priors on the amount of non-PH for all variables, before making use of information you get from examining fit to the observed outcome variable.  This will give you the correct uncertainties for "final" effects in the model.
Less satisfactory but simpler approaches are (1) use the unitness correlation $\rho$ that goes with the test of PH to gauge the extent of the violation, instead of using P-values, which can detect trivial violations, or (2) fit an overall PH model then fit an overall non-PH model (e.g., log-normal survival time model) and see which one "wins" by some global goodness of fit assessment.  I'm less inclined to pick and choose covariate-by-covariate.
