We propose an iterative variable selection method for the accelerated failure time model using high-dimensional survival data. Our method pioneers the use of the recently proposed structured screen-and-select framework for survival analysis. We use the marginal utility as the measure of association to inform the structured screening process. For the selection steps, we use Bayesian model selection based on non-local priors. We compare the proposed method with a few well-known methods. Assessment in terms of true positive rate and false discovery rate shows the usefulness of our method. We have implemented the method within the R package GWASinlps.

翻译：本文提出了一种针对高维生存数据的加速失效时间模型的迭代变量选择方法。该方法首次将近期提出的结构化筛选与选择框架应用于生存分析。我们采用边际效用作为关联度量指标，以指导结构化筛选过程。在选择步骤中，我们使用了基于非局部先验的贝叶斯模型选择方法。我们将所提方法与若干知名方法进行了比较。在真阳性率和错误发现率方面的评估表明了我们方法的有效性。该方法已在R包GWASinlps中实现。