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.
Wang
Sijian
S
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Nan
Bin
B
Zhu
Ji
J
Beer
David G
DG
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