ArticleStatistical applications in genetics and molecular biology2010
Buckley-James boosting for survival analysis with high-dimensional biomarker data.
Article in Statistical applications in genetics and molecular biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- AFFECT: an R package for accelerated functional failure time model with error-contaminated survival times and applications to gene expression data.BMC bioinformatics · 2024Article
- Regularized Buckley-James method for right-censored outcomes with block-missing multimodal covariates.Stat (International Statistical Institute) · 2022Article
- eBreCaP: extreme learning-based model for breast cancer survival prediction.IET systems biology · 2020Article
- Lasso regularization for left-censored Gaussian outcome and high-dimensional predictors.BMC medical research methodology · 2018Article
- Sparse boosting for high-dimensional survival data with varying coefficients.Statistics in medicine · 2018Article
- A Selective Review on Random Survival Forests for High Dimensional Data.Quantitative bio-science · 2017Article
- Cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L1/2 regularization.BMC medical genomics · 2016Article
- Random rotation survival forest for high dimensional censored data.SpringerPlus · 2016Article
- Empirical Likelihood for Censored Linear Regression and Variable Selection.Scandinavian journal of statistics, theory and applications · 2015Article
- A Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.Stat · 2014Article
- Identification of breast cancer prognosis markers using integrative sparse boosting.Methods of information in medicine · 2012Article
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Authors and funding
2 authors.
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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.
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