Interval censored survival data, where the exact event time is only known to lie in an interval, is commonly encountered in practice. Furthermore, medical advancements have made it possible for a fraction of patients to be cured. In this article, we analyze interval-censored data using the Cox–Aalen model with a cure fraction, where the probability of being uncured is determined by a logistic regression model and the failure times of the uncured subjects are modelled by the Cox–Aalen model with fixed covariates. We propose a multiple imputation (MI) scheme for obtaining parameter and variance estimates for both the cure probability and survival distribution of the uncured subjects. One major advantage of the proposed MI scheme is its simplicity since it avoids computational complexity resulting from interval censoring and presence of a cure fraction. The presented approach can be implemented by using the existing software for the Cox–Aalen model with right censored data. Simulation studies indicate that the approach performs well for practical situation. We apply the proposed method to the analysis of the data from hypobaric decompression sickness (HDS) study.

间隔删失生存数据在实践中经常遇到，其中确切的事件时间仅位于一个间隔内。此外，医学的进步使一小部分患者得以治愈。在本文中，我们使用具有治愈分数的 Cox-Aalen 模型分析区间删失数据，其中未治愈的概率由逻辑回归模型确定，未治愈受试者的失败时间由 Cox-Aalen 模型建模具有固定的协变量。我们提出了一种多重插补（MI）方案，用于获得未治愈受试者的治愈概率和生存分布的参数和方差估计。所提出的 MI 方案的一个主要优点是它的简单性，因为它避免了因区间审查和治愈分数的存在而导致的计算复杂性。所提出的方法可以通过使用具有正确审查数据的 Cox-Aalen 模型的现有软件来实现。仿真研究表明该方法在实际情况下表现良好。我们将所提出的方法应用于低压减压病（HDS）研究数据的分析。