Best model reduction approach for Fine-Gray prediction model

Hello everyone, Thank you all for the feedback! Appreciated! For unsupervised learning methods like principal component analysis, does it involve reducing the number of candidate variables before any model fitting? Also, I may have misclassified Ambler as pure stepwise earlier. My teammate said that the (Harrell-)Ambler method is not standard stepwise as it is categorized more as a selection procedure that emphasizes validation and shrinkage. Here are her comparison notes below: Please let me know what you think. Thanks again, Raf