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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.

SUBMITTER: Wang Z 

PROVIDER: S-EPMC2904263 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Buckley-James boosting for survival analysis with high-dimensional biomarker data.

Wang Zhu Z   Wang C Y CY  

Statistical applications in genetics and molecular biology 20100608


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  ...[more]

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