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

Zhu Wang, C. Y. Wang

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

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.

Original languageEnglish (US)
Article number24
JournalStatistical Applications in Genetics and Molecular Biology
Volume9
Issue number1
DOIs
StatePublished - 2010
Externally publishedYes

Keywords

  • Buckley-James estimator
  • LASSO
  • accelerated failure time model
  • boosting
  • censored survival data
  • variable selection

ASJC Scopus subject areas

  • Statistics and Probability
  • Molecular Biology
  • Genetics
  • Computational Mathematics

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