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Activity Number: 302 - Statistical Methods for Data Integration
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
Sponsor: International Chinese Statistical Association
Abstract #314107
Title: High-Dimensional Censored Quantile Regression Inference
Author(s): Qi Zheng*
Companies: University of Louisville

It is of interest to identify heterogeneous effects of genetic biomarkers on patients' survival and conduct proper statistical inference. We propose a novel fused procedure to draw inference on all predictors within the framework of censored quantile regression. The proposed estimator combines a sequence of low dimensional model fitting based on multi-sample splitting and variable selection. We show that, under some regularity conditions, the estimator is consistent and converges weakly to a Gaussian process indexed by the quantile level. Simulation studies indicate that our procedure properly quantifies the uncertainty of effect estimates in high dimensional settings. 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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