title: Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates.
abstract: BACKGROUND
  Technological advances that allow routine identification of high-dimensional risk factors have led to high demand for statistical techniques that enable full utilization of these rich sources of information for genetics studies. Variable selection for censored outcome data as well as control of false discoveries (i.e. inclusion of irrelevant variables) in the presence of high-dimensional predictors present serious challenges. This article develops a computationally feasible method based on boosting and stability selection. Specifically, we modified the component-wise gradient boosting to improve the computational feasibility and introduced random permutation in stability selection for controlling false discoveries.
  
  
  RESULTS
  We have proposed a high-dimensional variable selection method by incorporating stability selection to control false discovery. Comparisons between the proposed method and the commonly used univariate and Lasso approaches for variable selection reveal that the proposed method yields fewer false discoveries. The proposed method is applied to study the associations of 2339 common single-nucleotide polymorphisms (SNPs) with overall survival among cutaneous melanoma (CM) patients. The results have confirmed that BRCA2 pathway SNPs are likely to be associated with overall survival, as reported by previous literature. Moreover, we have identified several new Fanconi anemia (FA) pathway SNPs that are likely to modulate survival of CM patients.
  
  
  METHODS
  The related source code and documents are freely available at https://sites.google.com/site/bestumich/issues.
  
  
  BACKGROUND
  yili@umich.edu.
authors.affiliation: Department of Biostatistics and.
authors.forename: Kevin
authors.initials: K
authors.lastname: He
authors.affiliation: Department of Biostatistics and.
authors.forename: Yanming
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109, USA.
authors.forename: Ji
authors.initials: J
authors.lastname: Zhu
authors.affiliation: Department of Medicine, Duke University School of Medicine and Duke Cancer Institute, Duke University Medical Center, Durham, NC 27710, USA.
authors.forename: Hongliang
authors.initials: H
authors.lastname: Liu
authors.affiliation: Department of Surgical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.
authors.forename: Jeffrey E
authors.initials: JE
authors.lastname: Lee
authors.affiliation: Department of Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Hanover, NH 03750, USA.
authors.forename: Christopher I
authors.initials: CI
authors.lastname: Amos
authors.affiliation: Department of Biostatistics and Bioinformatics, Duke University and Duke Clinical Research Institute, Durham, NC 27710, USA.
authors.forename: Terry
authors.initials: T
authors.lastname: Hyslop
authors.affiliation: Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA and.
authors.forename: Jiashun
authors.initials: J
authors.lastname: Jin
authors.affiliation: Center of Statistical Research, School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, China.
authors.forename: Huazhen
authors.initials: H
authors.lastname: Lin
authors.affiliation: Department of Medicine, Duke University School of Medicine and Duke Cancer Institute, Duke University Medical Center, Durham, NC 27710, USA.
authors.forename: Qinyi
authors.initials: Q
authors.lastname: Wei
authors.affiliation: Department of Biostatistics and.
authors.forename: Yi
authors.initials: Y
authors.lastname: Li
mesh_terms: D000465:Algorithms; D024682:BRCA2 Protein / Q000235:genetics; D003198:Computer Simulation; D006801:Humans; D008545:Melanoma / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D012307:Risk Factors; D012878:Skin Neoplasms; D016019:Survival Analysis; D013997:Time Factors; D000096142:Cutaneous Malignant Melanoma
chemical_list: D024682:BRCA2 Protein