This study develops a marginal screening test to detect the presence of significant predictors for a right-censored time-to-event outcome under a high-dimensional accelerated failure time (AFT) model. Establishing a rigorous screening test in this setting is challenging, because of the right censoring and the post-selection inference. In the latter case, an implicit variable selection step needs to be included to avoid inflating the Type-I error. A prior study solved this problem by constructing an adaptive resampling test under an ordinary linear regression. To accommodate right censoring, we develop a new approach based on a maximally selected Koul-Susarla-Van Ryzin estimator from a marginal AFT working model. A regularized bootstrap method is used to calibrate the test. Our test is more powerful and less conservative than both a Bonferroni correction of the marginal tests and other competing methods. The proposed method is evaluated in simulation studies and applied to two real data sets.

中文翻译：

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生存结果高维预测因子的边缘筛选


本研究开发了一种边际筛选测试，以检测高维加速失效时间 (AFT) 模型下右删失事件时间结果的显着预测因子的存在。由于正确的审查和选择后的推断，在这种情况下建立严格的筛选测试具有挑战性。在后一种情况下，需要包含隐式变量选择步骤以避免夸大 I 类错误。先前的研究通过在普通线性回归下构建自适应重采样测试解决了这个问题。为了适应正确的审查，我们开发了一种基于边际 AFT 工作模型中最大选择的 Koul-Susarla-Van Ryzin 估计器的新方法。使用正则化引导方法来校准测试。我们的测试比边际测试的 Bonferroni 校正和其他竞争方法更强大且不那么保守。所提出的方法在模拟研究中进行了评估，并应用于两个真实数据集。