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

Xiaochao Xia, Binyan Jiang, Jialiang Li, Wenyang Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)547-569
Number of pages23
JournalLifetime Data Analysis
Volume22
Issue number4
DOIs
Publication statusPublished - 1 Oct 2016

Keywords

  • Accelerated failure time model
  • Confounder adjustment
  • Gene expression
  • Independent screening
  • Variable selection

ASJC Scopus subject areas

  • Applied Mathematics

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