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Title: Low-dimensional confounder adjustment and high-dimensional penalized estimation for survival analysis
Authors: Xia, X
Jiang, B 
Li, J
Zhang, W
Issue Date: Oct-2016
Source: Lifetime data analysis, Oct. 2016, v. 22, no. 4, p. 547-569
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.
Keywords: Accelerated failure time model
Confounder adjustment
Gene expression
Independent screening
Variable selection
Publisher: Springer
Journal: Lifetime data analysis 
ISSN: 1380-7870
EISSN: 1572-9249
DOI: 10.1007/s10985-015-9350-z
Rights: © Springer Science+Business Media New York 2015
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at:
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