论文标题
基于Copula模型，在不随机审查指标下丢失的生存估计值
Survival Estimation for Missing not at Random Censoring Indicators based on Copula Models
论文作者
论文摘要
在有协变量的右审核数据的情况下，条件性的Kaplan-Meier估计量（也称为Beran估计器）始终估计感兴趣的随机随访的条件存活函数。但是，必要的条件是对每个人是否受到审查的明确知识，这在实践中可能是不完整的。因此，当检查指标是通用随机变量时，我们建议对Beran估计器进行研究，并讨论Beran估计器效率的必要条件。由此，我们为条件生存函数提供了一个新的估计器，基于有条件的Copula模型，没有随机（MNAR）审查指标缺失。除了理论结果外，我们还说明了估计器如何通过仿真研究为小样本工作，并通过分析合成和真实数据来显示其实际适用性。
In the presence of right-censored data with covariates, the conditional Kaplan-Meier estimator (also known as the Beran estimator) consistently estimates the conditional survival function of the random follow-up for the event of interest. However, a necessary condition is the unambiguous knowledge of whether each individual is censored or not, which may be incomplete in practice. We therefore propose a study of the Beran estimator when the censoring indicators are generic random variables and discuss necessary conditions for the efficiency of the Beran estimator. From this, we provide a new estimator for the conditional survival function with missing not at random (MNAR) censoring indicators based on a conditional copula model for the missingness mechanism. In addition to the theoretical results, we illustrate how the estimators work for small samples through a simulation study and show their practical applicability by analyzing synthetic and real data.
