Feature Selection with Survival Outcome Data - Dr. Hong
Biostatistics Branch Seminar Series
April 1, 2020 | 10:30 AM – 11:30 AM
Hyokyoung (Grace) Hong, Ph.D.
Department of Statistics and Probability
Michigan State University
Detecting biomarkers that are relevant to patients' survival outcome is crucial for precision medicine. Dimension reduction is key in the process. Although regularization methods have been used for dimension reduction, they cannot handle a large number of candidate biomarkers generated by modern bio-techniques. Variable screening, which has been widely used for handling exceedingly large numbers of variables, is however much underdeveloped for censored outcome data. This talk introduces a series of new feature screening procedures that I have recently developed for survival data with ultrahigh dimensional covariates. These methods include conditional screening, integrated powered density (IPOD) screening, Lq-norm learning, and forward regression with partial likelihood. I will discuss the intuition behind and demonstrate their utilities through real data analyses.
Meeting number: 739 791 362
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1-650-479-3207 Call-in toll number (US/Canada)
Access code: 739 791 362
**The mission of the Biostatistics Branch (BB) is to be an outstanding biostatistics unit that can contribute to the understanding of cancer etiology and to improve public health by the development and application of quantitative methods. The BB Investigators develop statistical methods and data resources to strengthen observational studies, intervention trials, and laboratory investigations of cancer.**