Medical advancements have increased cancer survival rates and the possibility of finding a cure. Hence, it is crucial to evaluate the impact of treatments in terms of both curing the disease and prolonging survival. We may use a Cox proportional hazards (PH) cure model to achieve this. However, a significant challenge in applying such a model is the potential presence of partially observed covariates in the data. We aim to refine the methods for imputing partially observed covariates based on multiple imputation and fully conditional specification (FCS) approaches. To be more specific, we consider a more general case, where different covariate vectors are used to model the cure probability and the survival of patients who are not cured. We also propose an approximation of the exact conditional distribution using a regression approach, which helps draw imputed values at a lower computational cost. To assess its effectiveness, we compare the proposed approach with a complete case analysis and an analysis without any missing covariates. We discuss the application of these techniques to a real-world dataset from the BO06 clinical trial on osteosarcoma.

翻译：医学进步提高了癌症患者的生存率及疾病治愈的可能性。因此，评估治疗在治愈疾病和延长生存期两方面的效果至关重要。为实现这一目标，可采用Cox比例风险（PH）治愈模型。然而，应用此类模型面临的一个主要挑战是数据中可能存在部分观测的协变量。本文旨在基于多重插补和完全条件设定（FCS）方法，改进对部分观测协变量的插补方法。具体而言，我们考虑了一种更一般的情形，即使用不同的协变量向量分别对治愈概率和未治愈患者的生存情况进行建模。我们还提出了一种基于回归方法的精确条件分布近似，这有助于以较低的计算成本生成插补值。为评估其有效性，我们将所提出的方法与完整病例分析以及无缺失协变量的分析进行了比较。最后，我们讨论了这些技术在骨肉瘤BO06临床试验真实数据集中的应用。