  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.
