Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration
This addresses a key problem in survival analysis for researchers and practitioners by providing a robust solution to balance calibration and discrimination, though it is incremental as it builds on existing conformal methods.
The paper tackles the challenge of improving calibration in survival models without harming discrimination, achieving this through a conformal regression post-processing method validated across 11 real-world datasets.
Discrimination and calibration represent two important properties of survival analysis, with the former assessing the model's ability to accurately rank subjects and the latter evaluating the alignment of predicted outcomes with actual events. With their distinct nature, it is hard for survival models to simultaneously optimize both of them especially as many previous results found improving calibration tends to diminish discrimination performance. This paper introduces a novel approach utilizing conformal regression that can improve a model's calibration without degrading discrimination. We provide theoretical guarantees for the above claim, and rigorously validate the efficiency of our approach across 11 real-world datasets, showcasing its practical applicability and robustness in diverse scenarios.