Additive risk survival model with microarray data

被引:0
作者
Shuangge Ma
Jian Huang
机构
[1] Yale University,Department of Epidemiology and Public Health
[2] University of Iowa,Department of Statistics and Actuarial Science
来源
BMC Bioinformatics | / 8卷
关键词
Partial Little Square; Lasso; Mantle Cell Lymphoma; Principal Component Regression; Survival Risk;
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[1]  
Rosenwald A(2003)The proliferation gene expression signature is a quantitative integrator of oncogenic events that predicts survival in mantle cell lymphoma Cancer Cell 3 185-197
[2]  
Wright G(2001)Variable selection via nonconcave penalized likelihood and its oracle properties Journal of the American Statistical Association 96 1348-1360
[3]  
Wiestner A(2000)Distinct types of diffuse large B-Cell lymphoma identified by gene expression profiling Nature 403 503-511
[4]  
Chan WC(2001)Gene shaving as a method for identifying distinct sets of genes with similar expression patterns Genome Biology 2 1-21
[5]  
Connors JM(2002)Partial least squares proportional hazard regression for application to DNA microarray data Bioinformatics 18 1625-1632
[6]  
Campo E(2003)Kernel Cox regression models for linking gene expression profiles to censored survival data Pacific Symposium on Biocomputing 8 65-76
[7]  
Gascoyne RD(1997)The LASSO method for variable selection in the Cox model Statistics in Medicine 16 385-395
[8]  
Grogan TM(2005)Penalized Cox Regression Analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data Bioinformatics 21 3001-3008
[9]  
Muller-Hermelink HK(2006)Additive risk models for survival data with high dimensional covariates Biometrics 62 202-210
[10]  
Smeland EB(1994)Semiparametric analysis of the additive risk model Biometrika 81 61-71