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Doubly Penalized Buckley–James Method for Survival Data with High-Dimensional Covariates



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DOI 10 1111 j 1541 0420 2007 00877 x Biometrics 64 132 140 March 2008 Doubly Penalized Buckley James Method for Survival Data with High Dimensional Covariates Sijian Wang 1 Bin Nan 1 Ji Zhu 2 and David G Beer3 1 Department of Biostatistics University of Michigan Ann Arbor Michigan 48109 U S A 2 Department of Statistics University of Michigan Ann Arbor Michigan 48109 U S A 3 Departments of Surgery and Radiation Oncology University of Michigan Ann Arbor Michigan 48109 U S A email bnan umich edu Summary Recent interest in cancer research focuses on predicting patients survival by investigating gene expression pro les based on microarray analysis We propose a doubly penalized Buckley James method for the semiparametric 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 L1 and L2 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 Key words Accelerated failure time model Buckley James method Censored survival data Elastic net High dimensional covariate Lung cancer Microarray analysis Variable selection 1 Introduction Microarray technologies including cDNA and oligonucleotide arrays simultaneously obtain thousands of gene expression measurements for each sample Although a large number of genes are believed to be mostly inactive there are many genes whose activities are associated with various physiological effects An interesting and important task in analyzing human genomic data is to relate gene activities to phenotypic or clinical information The work of this article is motivated



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