Efficient Estimation of Associations Between Multiple Predictors and Survival Outcomes

• Activity Number: 329 - New Statistical Learning and Methods in Nonparametric Statistics
• Type: Invited
• Date/Time: Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Ian McKeague*
• Companies: Columbia University
• Keywords: marginal screening; post-selection inference; semiparametric efficiency

Abstract

This talk discusses a new approach to post-selection inference for screening high-dimensional predictors of survival outcomes. Post-selection inference for right-censored outcome data has been investigated in the literature, but much remains to be done to make the methods both reliable and computationally-scalable to high-dimensions. The new approach involves construction of semi-parametrically efficient estimators of the linear association between the predictors and the survival outcome, which are used to build a test statistic for detecting the presence of an association between any of the predictors and the outcome. Further, a stabilization technique reminiscent of bagging allows a normal calibration for the resulting test statistic, which enables the construction of confidence intervals for the maximal association between a predictor and the outcome and also greatly reduces computational cost. The talk is based on joint work with Tzu-Jung Huang and Alex Luedtke.