This example compares survival random forest and gradient boosting models for predicting patient outcomes in an AIDS clinical trial. It shows survival curves for individual patients over time, then evaluates classification performance at a fixed time point using ROC curves for each model and a blended ensemble. The code uses the `ranger` package for survival forests, `gbm` for boosting, and `pROC` for ROC analysis, with data loaded from the ACTG320 dataset. It also demonstrates data splitting and survival probability extraction at a chosen time period.