Case‐cohort design has been demonstrated to be an economical and efficient approach in large cohort studies when the measurement of some covariates on all individuals is expensive. Various methods have been proposed for case‐cohort data when the dimension of covariates is smaller than sample size. However, limited work has been done for high‐dimensional case‐cohort data which are frequently collected in large epidemiological studies. In this paper, we propose a variable screening method for ultrahigh‐dimensional case‐cohort data under the framework of proportional model, which allows the covariate dimension increases with sample size at exponential rate. Our procedure enjoys the sure screening property and the ranking consistency under some mild regularity conditions. We further extend this method to an iterative version to handle the scenarios where some covariates are jointly important but are marginally unrelated or weakly correlated to the response. The finite sample performance of the proposed procedure is evaluated via both simulation studies and an application to a real data from the breast cancer study.

中文翻译：

---

具有失败时间结果的病例队列研究的特征筛选

当对所有个体的某些协变量的测量成本很高时，病例队列设计已被证明是大型队列研究中一种经济有效的方法。当协变量的维度小于样本量时，已经提出了各种方法来处理病例队列数据。然而，对大型流行病学研究中经常收集的高维病例队列数据所做的工作有限。在本文中，我们提出了一种比例模型框架下的超高维病例队列数据的变量筛选方法，该方法允许协变量维度以指数速率随样本量增加。我们的程序在一些温和的规律性条件下具有一定的筛选性和排序一致性。我们进一步将此方法扩展到迭代版本，以处理某些协变量共同重要但与响应无关或弱相关的情况。通过模拟研究和对来自乳腺癌研究的真实数据的应用来评估所提议程序的有限样本性能。