Inference for High-Dimensional Censored Quantile Regression

• 成果类型: Article
• 署名作者: Fei, Zhe; Zheng, Qi; Hong, Hyokyoung G.; Li, Yi
• 署名单位: University of California System; University of California Los Angeles; University of Louisville; Michigan State University; University of Michigan System; University of Michigan
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.1957900
• 发表日期: 2023
• 页码: 898-912
• 关键词: Post-selection Inference; lung-cancer; confidence-intervals; repair genes; survival; Heterogeneity; mutations; susceptibility; expression; regions

摘要

With the availability of high-dimensional genetic biomarkers, it is of interest to identify heterogeneous effects of these predictors on patients' survival, along with proper statistical inference. Censored quantile regression has emerged as a powerful tool for detecting heterogeneous effects of covariates on survival outcomes. To our knowledge, there is little work available to draw inferences on the effects of high-dimensional predictors for censored quantile regression (CQR). This article proposes a novel procedure to draw inference on all predictors within the framework of global CQR, which investigates covariate-response associations over an interval of quantile levels, instead of a few discrete values. The proposed estimator combines a sequence of low-dimensional model estimates that are based on multi-sample splittings and variable selection. We show that, under some regularity conditions, the estimator is consistent and asymptotically follows a Gaussian process indexed by the quantile level. Simulation studies indicate that our procedure can properly quantify the uncertainty of the estimates in high-dimensional settings. We apply our method to analyze the heterogeneous effects of SNPs residing in lung cancer pathways on patients' survival, using the Boston Lung Cancer Survival Cohort, a cancer epidemiology study on the molecular mechanism of lung cancer.