I used bootstrap (N = 1000 samples) to quantificate optimism and subsequently corrected c-index and Somers´ D statistics to select a final model with best predictive accuracy among three "candidate" models. I used then bootstrapping (N = 10 000 samples) on this final model to calculate bootstrapped confidence intervals and p-values of the variables in the final model.
The bootstrap methods, they differ among each other, right? I mean, the first used bootstrap only do validate the model in respect to predictive power and the second makes confidence intervals of variables in final model more realiable?
Thanks for comment