Feature screening has been seen as the first step in analyzing the ultrahigh-dimensional data with the censored survival time. In this article, we develop a surrogate-variable-based model-free feature screening approach for the censored data under the general censoring mechanism, where the censoring variable may depend on the survival variable and the covariates. This approach is developed by finding some observable variables whose active covariates contain the active covariates of the survival variable as a subset, respectively. Then, any existing model-free feature screening method with the sure screening property for full data can be applied to estimating the sets of the active covariates of the observable variables and hence the set of the active covariates of the survival variable. The sure screening property of the proposed approach is established, and its finite sample performances are demonstrated through some simulations. Further, we illustrate the proposed approach by analyzing two real datasets.

特征筛选被视为分析具有删失生存时间的超高维数据的第一步。在本文中，我们在一般审查机制下为审查数据开发了一种基于代理变量的无模型特征筛选方法，其中审查变量可能取决于生存变量和协变量。这种方法是通过找到一些可观察变量来开发的，这些变量的活动协变量分别包含生存变量的活动协变量作为子集。然后，任何现有的对全数据具有确定筛选特性的无模型特征筛选方法都可以应用于估计可观察变量的活动协变量集，从而估计生存变量的活动协变量集。建立了所提出方法的可靠筛选特性，并通过一些模拟证明了其有限样本性能。此外，我们通过分析两个真实数据集来说明所提出的方法。