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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. [ABSTRACT FROM AUTHOR]

To avoid fraudulent practices, LC-Orbitrap and FT-NIRS combined with multivariate analysis was used to distinguish between 53 Korean and foreign Boston butt samples; forty were used to establish the calibration model and 13 were used as an external validation set. Twenty metabolites were determined to be good indicators of geographic origin. Both LC-Orbitrap with CDA model based on 20 metabolites and FT-NIRS with PLS achieved 100% efficiency in identifying Korean and foreign samples; overall predictive rates for LC-Orbitrap (94.9%) and FT-NIRS (100%). Thus, combined use of LC-Orbitrap and FT-NIRS could be proposed to determine reliably discriminate geographic origins of pork samples. [ABSTRACT FROM AUTHOR]