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Statistica Sinica 29 (2019), 2105-2139

MARGINAL SCREENING FOR HIGH-DIMENSIONAL
PREDICTORS OF SURVIVAL OUTCOMES
Tzu-Jung Huang, Ian W. McKeague and Min Qian
Columbia University

Abstract: 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.

Key words and phrases: Accelerated failure time model, bootstrap, family-wise error rate, inverse probability weighting, multiple testing, post-selection inference.

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