Toward Conditional Distribution Calibration in Survival Prediction

Shi-ang Qi, Yakun Yu, Russell Greiner

Keywords: survival analysis; calibration; conformal prediction; censorship; discrimination

TL;DR: This paper underscores the critical role of marginal and conditional calibration in survival analysis and introduces a method that enhances both marginal and conditional calibration without sacrificing discrimination performance in survival models.

Abstract:

Survival prediction often involves estimating the time-to-event distribution from censored datasets. Previous approaches have focused on enhancing discrimination and marginal calibration. In this paper, we highlight the significance of *conditional calibration* for real-world applications – especially its role in individual decision-making. We propose a method based on conformal prediction that uses the model’s predicted individual survival probability at that instance’s observed time. This method effectively improves the model’s marginal and conditional calibration, without compromising discrimination. We provide asymptotic theoretical guarantees for both marginal and conditional calibration and test it extensively across 15 diverse real-world datasets, demonstrating the method’s practical effectiveness and
versatility in various settings.

Primary Area: Machine learning for healthcare

Submission Number: 6318