Buckley-James Boosting for Survival Analysis with High-Dimensional Biomarker Data

Wang Zhu and Wang C.Y.
Additional contact information
Wang Zhu: Yale University
Wang C.Y.: Fred Hutchinson Cancer Research Center

Statistical Applications in Genetics and Molecular Biology, 2010, vol. 9, issue 1, 33

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.

Keywords: boosting; accelerated failure time model; Buckley-James estimator; censored survival data; LASSO; variable selection (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (5)

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DOI: 10.2202/1544-6115.1550

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