Low-dimensional confounder adjustment and high-dimensional penalized estimation for survival analysis

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• 作者：Xiaochao Xia ; Binyan Jiang ; Jialiang Li ; Wenyang Zhang
• 关键词：Accelerated failure time model ; Confounder adjustment ; Gene expression ; Independent screening ; Variable selection
• 刊名：Lifetime Data Analysis
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：22
• 期：4
• 页码：547-569
• 全文大小：562 KB
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics
  Statistics
  Statistics for Life Sciences, Medicine and Health Sciences
  Quality Control, Reliability, Safety and Risk
  Statistics for Business, Economics, Mathematical Finance and Insurance
  Operation Research and Decision Theory
  
• 出版者：Springer Netherlands
• ISSN：1572-9249
• 卷排序：22

文摘

High-throughput profiling is now common in biomedical research. In this paper we consider the layout of an etiology study composed of a failure time response, and gene expression measurements. In current practice, a widely adopted approach is to select genes according to a preliminary marginal screening and a follow-up penalized regression for model building. Confounders, including for example clinical risk factors and environmental exposures, usually exist and need to be properly accounted for. We propose covariate-adjusted screening and variable selection procedures under the accelerated failure time model. While penalizing the high-dimensional coefficients to achieve parsimonious model forms, our procedure also properly adjust the low-dimensional confounder effects to achieve more accurate estimation of regression coefficients. We establish the asymptotic properties of our proposed methods and carry out simulation studies to assess the finite sample performance. Our methods are illustrated with a real gene expression data analysis where proper adjustment of confounders produces more meaningful results.