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

Jeongho Park, Daheen Kim, Cheoljun Kim, Hyungbin Park, Sangwook Kang, Gwangsu Kim

Keywords: survival analysis, calibration, Kolmogorov-Smirnov metric, post-processing

TL;DR: We propose a KS-based calibration method for survival models that avoids discretization and improves calibration while preserving predictive accuracy across real-world datasets and models.

Abstract: 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 $\textit{KS metric-based post-processing}$ 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.

Primary Area: General machine learning (supervised, unsupervised, online, active, etc.)

Submission Number: 21598