Variable screening for censored survival data is most challenging when both survival and censoring times are correlated with an ultrahigh‐dimensional vector of covariates. Existing approaches to handling censoring often make use of inverse probability weighting by assuming independent censoring with both survival time and covariates. This is a convenient but rather restrictive assumption which may be unmet in real applications, especially when the censoring mechanism is complex and the number of covariates is large. To accommodate heterogeneous (covariate‐dependent) censoring that is often present in high‐dimensional survival data, we propose a Gehan‐type rank screening method to select features that are relevant to the survival time. The method is invariant to monotone transformations of the response and of the predictors, and works robustly for a general class of survival models. We establish the sure screening property of the proposed methodology. Simulation studies and a lymphoma data analysis demonstrate its favorable performance and practical utility.

中文翻译：

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存在异类审查的生存数据的可变筛选

当生存和审查时间都与协变量的超高维向量相关时，对审查的生存数据进行变量筛选最具挑战性。现有的处理审查方法通常通过假设具有生存时间和协变量的独立审查来利用逆概率加权。这是一个方便但相当严格的假设，在实际应用中可能无法满足，特别是在检查机制复杂且协变量数量大的情况下。为了适应高维生存数据中经常出现的异类（依赖于协变量）检查，我们提出了一种Gehan型秩筛选方法，以选择与生存时间相关的特征。该方法对于响应和预测变量的单调变换是不变的，并能在一般生存模型类别中发挥出色的作用。我们确定了所提出方法的确定筛选性质。仿真研究和淋巴瘤数据分析证明了其良好的性能和实用性。