Marginal screening for high-dimensional predictors of survival outcomes.
Marginal screening for high-dimensional predictors of survival outcomes.
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DOI：
10.5705/ss.202017.0298
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发表时间：
2019-10
影响因子：
1.4
通讯作者：
T. Huang;I. McKeague;Min Qian
中科院分区：
文献类型：
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作者：
T. Huang;I. McKeague;Min Qian
本研究开发了一种边缘筛选检验，以检测高维加速失效时间（AFT）模型下右删失事件发生时间结局的显著预测因子。在这种情况下建立一个严格的筛选测试是具有挑战性的，因为正确的删失和选择后的推断。在后一种情况下，需要包含隐式变量选择步骤，以避免扩大I类错误。先前的研究通过在普通线性回归下构造自适应回归检验来解决这个问题。为了适应正确的删失，我们开发了一种新的方法的基础上，最大限度地选择Koul-Susarla-货车Ryzin估计从边际AFT工作模型。一个正则化的bootstrap方法被用来校准测试。我们的测试是更强大的和更少的保守性比Bonferroni校正的边缘测试和其他竞争的方法。所提出的方法进行了评估，在模拟研究和应用到两个真实的数据集。
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.