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.

翻译：现有的生存分析技术严重依赖强建模假设，因此容易产生模型设定误差。本文基于保形预测思想开发了一种推理方法，该方法可包裹任何生存预测算法，生成经过校准的、依赖协变量的生存时间下界预测值。在I型右删失设置下，当删失时间完全外生时，下界预测值在有限样本下具有保证的覆盖概率，且除数据独立同分布假设外无需任何额外假设。在更一般的条件独立删失假设下，这些界满足双重稳健性质：只要删失机制或条件生存函数中有一项估计良好，边际覆盖概率即可近似保证。此外，我们证明了这些下界预测值对其他删失类型同样有效且具有信息量。通过合成数据及英国生物银行真实COVID-19数据验证了本方法的有效性与高效性。