This study develops a sure joint feature screening method for the case-cohort design with ultrahigh-dimensional covariates. Our method is based on a sparsity-restricted Cox proportional hazards model. An iterative reweighted hard thresholding algorithm is proposed to approximate the sparsity-restricted, pseudo-partial likelihood estimator for joint screening. We rigorously show that our method possesses the sure screening property, with the probability of retaining all relevant covariates tending to 1 as the sample size goes to infinity. Our simulation results demonstrate that the proposed procedure has substantially improved screening performance over some existing feature screening methods for the case-cohort design, especially when some covariates are jointly correlated, but marginally uncorrelated, with the event time outcome. A real data illustration is provided using breast cancer data with high-dimensional genomic covariates. We have implemented the proposed method using MATLAB and made it available to readers through GitHub.

中文翻译：

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案例队列设计下高维Cox比例风险模型的可靠联合筛选。

本研究为超高维协变量的病例队列设计开发了一种可靠的联合特征筛选方法。我们的方法基于稀疏性限制的 Cox 比例风险模型。提出了一种迭代重加权硬阈值算法来近似联合筛选的稀疏性限制的伪部分似然估计。我们严格证明我们的方法具有确定的筛选特性，当样本量趋于无穷大时，保留所有相关协变量趋于 1 的概率。我们的模拟结果表明，与案例队列设计的一些现有特征筛选方法相比，所提出的程序大大提高了筛选性能，特别是当某些协变量与事件时间结果共同相关但略有不相关时。使用具有高维基因组协变量的乳腺癌数据提供了真实的数据说明。我们已经使用 MATLAB 实现了所提出的方法，并通过 GitHub 向读者提供。