Permutation Methods for Comparing the Accuracy of Nested Prediction Models in Survival Analysis.

When making patient-specific prediction, it is important to compare prediction models to evaluate the gain in prediction accuracy for including additional covariates. We propose two statistical testing methods, the complete data permutation (CDP) and the permutation cross-validation (PCV) for comparing prediction models. We simulate clinical trial settings extensively and show that both methods are robust and achieve almost correct test sizes; the methods have comparable power in moderate to large sample situations, while the CDP is more efficient in computation. The methods are also applied to ovarian cancer clinical trial data.