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Activity Number: 697
Type: Contributed
Date/Time: Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #314807
Title: Marginal Screening for High-Dimensional Predictors Under Right Censoring
Author(s): Tzu-Jung Huang* and Ian McKeague and Min Qian
Companies: Columbia University and Columbia University and Columbia University
Keywords: Adaptive resampling test ; Accelerated failure time model ; Bootstrap ; Empirical process ; Family-wise error rate ; Inverse probability weighting
Abstract:

This talk discusses a screening procedure based on marginal linear regression to detect the presence of a predictor significantly correlated to a right-censored time-to-event outcome. Standard inferential methods of screening fail when the limiting distribution is discontinuous at zero as a function of the regression coefficient of the predictor maximally correlated with the outcome. McKeague and Qian (2015) constructed an adaptive resampling test (ART) to circumvent this non-standard limiting behavior based on ordinary linear regression. To address right censoring, we propose to extend ART in terms of an accelerated failure time model with the time-to-event outcome replaced by the estimated synthetic response suggested in Koul, Susarla and Van Ryzin (1981). This provides a less conservative approach to control family-wise error rates than the Bonferroni method. The performance of this procedure is evaluated via simulation studies, and applied to high-throughput gene expression data for patients with diffuse large-B-cell lymphoma.


Authors who are presenting talks have a * after their name.

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