Doubly penalized buckley-james method for survival data with high-dimensional covariates.
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 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.
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Sijian
S
Wang
Bin
B
Nan
Ji
J
Zhu
David G
DG
Beer
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008175:Lung Neoplasms / Q000378:metabolism* / Q000401:mortality*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D016019:Survival Analysis; D015996:Survival Rate
D014408:Biomarkers, Tumor