title: Buckley-James boosting for survival analysis with high-dimensional biomarker data.
abstract: There has been increasing interest in predicting patients' survival after therapy by investigating gene expression microarray data. In the regression and classification models with high-dimensional genomic data, boosting has been successfully applied to build accurate predictive models and conduct variable selection simultaneously. We propose the Buckley-James boosting for the semiparametric accelerated failure time models with right censored survival data, which can be used to predict survival of future patients using the high-dimensional genomic data. In the spirit of adaptive LASSO, twin boosting is also incorporated to fit more sparse models. The proposed methods have a unified approach to fit linear models, non-linear effects models with possible interactions. The methods can perform variable selection and parameter estimation simultaneously. The proposed methods are evaluated by simulations and applied to a recent microarray gene expression data set for patients with diffuse large B-cell lymphoma under the current gold standard therapy.
authors.affiliation: Yale University, USA. zhu.wang@yale.edu
authors.forename: Zhu
authors.initials: Z
authors.lastname: Wang
authors.forename: C Y
authors.initials: CY
authors.lastname: Wang
mesh_terms: D014408:Biomarkers, Tumor; D020869:Gene Expression Profiling; D006801:Humans; D016403:Lymphoma, Large B-Cell, Diffuse / Q000175:diagnosis* / Q000235:genetics / Q000628:therapy; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D011237:Predictive Value of Tests; D016019:Survival Analysis
chemical_list: D014408:Biomarkers, Tumor