You should think about what the R model is trying to do. It is likely that author of the model chose penalized B splines simply to capture the potential that the survival rate has a potentially complicated relationship with the AGE variable. The specific form of the spline probably doesn't matter. In fact, if it is a good model, it should be robust to changes in the spline family. SAS supports B-splines, PB-splines, thin-plate splines, cubic splines, adaptive regression splines, and more, but in my experience, the form of the splines often has little impact on the predictive model.
For an example of two models that illustrate my points, see "Nonparametric regression for binary response data in SAS.' The example shows two completely different (nonparametric) models that give roughly the same predictive model. If they didn't give similar predictions, then one or both models are probably not good.
I encourage you to use the EFFECT statement in PROC PHREG to construct a model that uses either the BSPLINE or TPF spline basis. Compare the models' predictions to the R model. In most applications, the goal is to find a good model, not to worry about technical differences between different spline models.
