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Activity Number: 397 - Personalized Medicine with Large-Scale Data: Beyond Machine Learning
Type: Invited
Date/Time: Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract #316972
Title: Inference for High-Dimensional Censored Quantile Regression
Author(s): Yi Li*
Companies: University of Michigan

Censored quantile regression has emerged as a powerful tool for detecting heterogeneous effects of covariates on survival outcomes. However, few works are available to draw inference on the effects of high dimensional predictors for censored quantile regression. We propose a novel fused procedure to draw inference on all predictors within the framework of ``global'' censored quantile regression, where the quantile level is over an interval, instead of several discrete values. The proposed estimator combines a sequence of low dimensional model fitting that is based on multi-sample splitting and variable selection. We show that the estimator is consistent and asymptotically follows a Gaussian process indexed by the quantile level. We apply our method to analyze the heterogeneous effects of SNPs residing in the lung cancer pathways on patients' survival, using the Boston Lung Cancer Survivor Cohort, a cancer epidemiology study investigating the molecular mechanism of lung cancer.

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

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