Conformalized Survival Analysis

TL;DR

This paper introduces a conformal prediction-based method for survival analysis that provides calibrated, covariate-dependent lower bounds on survival times with finite-sample coverage guarantees, even under model misspecification.

Contribution

It develops a flexible, assumption-light inferential approach for survival analysis that guarantees coverage and robustness under various censoring conditions.

Findings

Method achieves finite-sample coverage guarantees.

Bounds remain valid under different censoring types.

Validated on synthetic and COVID-19 UK Biobank data.

Abstract

Existing survival analysis techniques heavily rely on strong modelling assumptions and are, therefore, prone to model misspecification errors. In this paper, we develop an inferential method based on ideas from conformal prediction, which can wrap around any survival prediction algorithm to produce calibrated, covariate-dependent lower predictive bounds on survival times. In the Type I right-censoring setting, when the censoring times are completely exogenous, the lower predictive bounds have guaranteed coverage in finite samples without any assumptions other than that of operating on independent and identically distributed data points. Under a more general conditionally independent censoring assumption, the bounds satisfy a doubly robust property which states the following: marginal coverage is approximately guaranteed if either the censoring mechanism or the conditional survival function is estimated well. Further, we demonstrate that the lower predictive bounds remain valid and informative for other types of censoring. The validity and efficiency of our procedure are demonstrated on synthetic data and real COVID-19 data from the UK Biobank.