Conformalized survival analysis

• 成果类型: Article
• 署名作者: Candes, Emmanuel; Lei, Lihua; Ren, Zhimei
• 署名单位: Stanford University; Stanford University; Stanford University; University of Chicago
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1093/jrsssb/qkac004
• 发表日期: 2023
• 页码: 24-45
• 关键词: follow-up; prediction; inference; MODEL; RECRUITMENT; Lasso

摘要

In this paper, we develop an inferential method based on 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. The validity and efficiency of our procedure are demonstrated on synthetic data and real COVID-19 data from the UK Biobank.