Doubly penalized Buckley-James method for survival data with high-dimensional covariates

被引：61

作者：

Wang, Sijian ^{[1
]}

Nan, Bin ^{[1
]}

Zhu, Ji ^{[2
]}

Beer, David G. ^{[3
,4
]}

机构：

[1] Univ Michigan, Dept Biostat, Ann Arbor, MI 48109 USA

[2] Univ Michigan, Dept Stat, Ann Arbor, MI 48109 USA

[3] Univ Michigan, Dept Surg, Ann Arbor, MI 48109 USA

[4] Univ Michigan, Dept Radiat Oncol, Ann Arbor, MI 48109 USA

来源：

BIOMETRICS | 2008年 / 64卷 / 01期

关键词：

accelerated failure time model; Buckley-James method; censored survival data; elastic net; high-dimensional covariate; lung cancer; microarray analysis; variable selection;

D O I：

10.1111/j.1541-0420.2007.00877.x

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Recent interest in cancer research focuses on predicting patients' survival by investigating gene expression profiles based on microarray analysis. We propose a doubly penalized Buckley-James method for the serniparametric accelerated failure time model to relate high-dimensional genomic data to censored survival outcomes, which uses the elastic-net penalty that is a mixture of L-1- and L-2-norm penalties. Similar to the elastic-net method for a linear regression model with uncensored data, the proposed method performs automatic gene selection and parameter estimation, where highly correlated genes are able to be selected (or removed) together. The two-dimensional tuning parameter is determined by generalized crossvalidation. The proposed method is evaluated by simulations and applied to the Michigan squamous cell lung carcinoma study.

引用

页码：132 / 140

页数：9

共 41 条

[11] Iterative partial least squares with right-censored data analysis: A comparison to other dimension reduction techniques [J].