|Title||Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates.|
|Publication Type||Journal Article|
|Year of Publication||2016|
|Authors||He, Kevin, Yanming Li, Ji Zhu, Hongliang Liu, Jeffrey E. Lee, Christopher I. Amos, Terry Hyslop, Jiashun Jin, Huazhen Lin, Qinyi Wei, and Yi Li|
|Date Published||2016 Jan 01|
|Keywords||Algorithms, BRCA2 Protein, Computer Simulation, Humans, Melanoma, Polymorphism, Single Nucleotide, Risk Factors, Skin Neoplasms, Survival Analysis, Time Factors|
MOTIVATION: 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.AVAILABILITY AND IMPLEMENTATION: The related source code and documents are freely available at https://sites.google.com/site/bestumich/issues.CONTACT: email@example.com.
|Original Publication||Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates.|
|PubMed Central ID||PMC4757968|
|Grant List||R01CA100264 / CA / NCI NIH HHS / United States |
P50 CA093459 / CA / NCI NIH HHS / United States
P30 CA023108 / CA / NCI NIH HHS / United States
R01CA133996 / CA / NCI NIH HHS / United States
P30CA014236 / CA / NCI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates.