Conformal Survival Bands for Risk Screening under Right-Censoring

TL;DR

This paper introduces conformal survival bands that provide personalized uncertainty quantification for survival predictions under right-censoring, useful for risk screening.

Contribution

It presents a novel conformal inference method for survival analysis that offers predictive survival bands with finite-sample guarantees, adaptable to any survival model.

Findings

Effective in simulated data

Promising results on real datasets

Provides asymptotic guarantees

Abstract

We propose a method to quantify uncertainty around individual survival distribution estimates using right-censored data, compatible with any survival model. Unlike classical confidence intervals, the survival bands produced by this method offer predictive rather than population-level inference, making them useful for personalized risk screening. For example, in a low-risk screening scenario, they can be applied to flag patients whose survival band at 12 months lies entirely above 50\%, while ensuring that at least half of flagged individuals will survive past that time on average. Our approach builds on recent advances in conformal inference and integrates ideas from inverse probability of censoring weighting and multiple testing with false discovery rate control. We provide asymptotic guarantees and show promising performance in finite samples with both simulated and real data.