Data with high-dimensional covariates are now commonly encountered. Compared to other types of responses, research on high-dimensional data with censored survival responses is still relatively limited, and most of the existing studies have been focused on estimation and variable selection. In this study, we consider data with a censored survival response, a set of low-dimensional covariates of main interest, and a set of high-dimensional covariates that may also affect survival. The accelerated failure time model is adopted to describe survival. The goal is to conduct inference for the effects of low-dimensional covariates, while properly accounting for the high-dimensional covariates. A penalization-based procedure is developed, and its validity is established under mild and widely adopted conditions. Simulation suggests satisfactory performance of the proposed procedure, and the analysis of two cancer genetic datasets demonstrates its practical applicability.

中文翻译：

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高维加速失效时间模型中低维协变量的推理


现在经常遇到具有高维协变量的数据。与其他类型的反应相比，针对高维数据的截尾生存反应的研究还相对有限，现有研究大多集中在估计和变量选择上。在本研究中，我们考虑具有删失生存反应的数据、一组主要感兴趣的低维协变量以及一组也可能影响生存的高维协变量。采用加速失效时间模型来描述生存。目标是对低维协变量的影响进行推断，同时正确考虑高维协变量。开发了一种基于惩罚的程序，并在温和且广泛采用的条件下确立了其有效性。模拟表明所提出的程序具有令人满意的性能，并且对两个癌症遗传数据集的分析证明了其实际适用性。