Abstract

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.

摘要

随着高维遗传生物标志物的出现，确定这些预测因素对患者生存的异质性影响以及适当的统计推断是很有意义的。截尾分位数回归已成为检测协变量对生存结果的异质性影响的强大工具。据我们所知，几乎没有什么工作可以推断高维预测变量对审查分位数回归（CQR）的影响。本文提出了一种新颖的程序，可以在全局 CQR 框架内对所有预测变量进行推断，该程序研究分位数水平区间内的协变量-响应关联，而不是几个离散值。所提出的估计器结合了一系列基于多样本分割和变量选择的低维模型估计。我们证明，在某些规律性条件下，估计量是一致的，并且渐近遵循由分位数水平索引的高斯过程。模拟研究表明，我们的程序可以正确量化高维设置中估计的不确定性。我们利用波士顿肺癌生存队列（一项关于肺癌分子机制的癌症流行病学研究）应用我们的方法来分析肺癌通路中的 SNP 对患者生存的异质性影响。模拟研究表明，我们的程序可以正确量化高维设置中估计的不确定性。我们利用波士顿肺癌生存队列（一项关于肺癌分子机制的癌症流行病学研究）应用我们的方法来分析肺癌通路中的 SNP 对患者生存的异质性影响。模拟研究表明，我们的程序可以正确量化高维设置中估计的不确定性。我们利用波士顿肺癌生存队列（一项关于肺癌分子机制的癌症流行病学研究）应用我们的方法来分析肺癌通路中的 SNP 对患者生存的异质性影响。