Doubly penalized Buckley-James method for survival data with high-dimensional covariates
Doubly penalized Buckley-James method for survival data with high-dimensional covariates
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DOI：
10.1111/j.1541-0420.2007.00877.x
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发表时间：
2008-03-01
期刊：
影响因子：
1.9
通讯作者：
Beer, David G.
中科院分区：
文献类型：
--
作者：
Wang, Sijian;Nan, Bin;Beer, David G.
最近对癌症研究的兴趣集中在通过基于微阵列分析的基因表达谱来预测患者的生存。我们为serniparametric加速失效时间模型提出了一种双重惩罚Buckley-James方法，将高维基因组数据与审查的生存结果联系起来，该方法使用弹性网络惩罚，该惩罚是L-1-和L-2-范数惩罚的混合。与使用未经审查的数据的线性回归模型的弹性网络方法类似，所提出的方法执行自动基因选择和参数估计，其中高度相关的基因能够被一起选择（或删除）。二维调整参数由广义交叉验证确定。所提出的方法通过模拟进行评估并应用于密歇根鳞状细胞肺癌研究。
Recent interest in cancer research focuses on predicting patients' survival by investigating gene expression profiles based on microarray analysis. We propose a doubly penalized Buckley-James method for the serniparametric accelerated failure time model to relate high-dimensional genomic data to censored survival outcomes, which uses the elastic-net penalty that is a mixture of L-1- and L-2-norm penalties. Similar to the elastic-net method for a linear regression model with uncensored data, the proposed method performs automatic gene selection and parameter estimation, where highly correlated genes are able to be selected (or removed) together. The two-dimensional tuning parameter is determined by generalized crossvalidation. The proposed method is evaluated by simulations and applied to the Michigan squamous cell lung carcinoma study.