High-dimensional variable selection for Cox's proportional hazards model

Abstract

Variable selection in high dimensional space has challenged many contemporary statistical problems from many frontiers of scientific disciplines. Recent technology advance has made it possible to collect a huge amount of covariate information such as microarray, proteomic and SNP data via bioimaging technology while observing survival information on patients in clinical studies. Thus, the same challenge applies to the survival analysis in order to understand the association between genomics information and clinical information about the survival time. In this work, we extend the sure screening procedure Fan and Lv (2008) to Cox's proportional hazards model with an iterative version available. Numerical simulation studies have shown encouraging performance of the proposed method in comparison with other techniques such as LASSO. This demonstrates the utility and versatility of the iterative sure independent screening scheme.

17 pages, 5 figures

Cited by in corpus (5)

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• Variable Screening for High Dimensional Time Series
• Scalable Sparse Cox's Regression for Large-Scale Survival Data via Broken Adaptive Ridge
• Nonparametric Independence Screening via Favored Smoothing Bandwidth
• C-mix: a high dimensional mixture model for censored durations, with applications to genetic data