KSP: Kolmogorov-Smirnov metric-based Post-Hoc Calibration for Survival Analysis

We propose a new calibration method for survival models based on the Kolmogorov–Smirnov (KS) metric.

Existing approaches—including conformal prediction, D-calibration, and Kaplan–Meier (KM)-based methods—often rely on heuristic binning or additional nonparametric estimators, which undermine their adaptability to continuous-time settings and complex model outputs.

To address these limitations, we introduce a streamlined KS metric-based post-processing framework (KSP) that calibrates survival predictions without relying on discretization or KM estimation. This design enhances flexibility and broad applicability.

We conduct extensive experiments on diverse real-world datasets using a variety of survival models. Empirical results demonstrate that our method consistently improves calibration performance over existing methods while maintaining high predictive accuracy. We also provide a theoretical analysis of the KS metric and discuss extensions to in-processing settings.