Regularized Regression for Survival Data: Methods and Applications

Overview

Abstract

In the era of biomedical big data, survival outcome data with high-throughput predictors are routinely collected. These high dimensional data defy classical survival regression models, which are either infeasible to fit or likely to incur low predictability because of overfitting. This short course will introduce the basic concepts of survival analysis and various cutting-edge methods that handle survival outcome data with high dimensional predictors. I will cover statistical principles and concepts behind the methods, and will also discuss their applications to the real medical examples.
 

The topics include

      Survival analysis overview: basic concepts and models, e.g.

            i)  Cox Models,

            ii)  Accelerated Failure Time (AFT),

          and iii) Censored Quantile Regression (CQR) Models;

      Survival models with high dimensional predictors (p>n): Regularized methods and Dantzig selector.

Brief Biography

Yi Li is a Professor of Biostatistics and Professor of Public Health. His current research interests are data science, survival analysis, longitudinal and correlated data analysis, measurement error problems, spatial models, and clinical trial designs. His methodologic research is funded by various NIH statistical grants starting from year 2003. Yi Li is actively involved in collaborative research in modern biomedical studies with researchers from the University of Michigan and Harvard University. The applications have included chronic kidney disease surveillance, organ transplantation, cancer preventive studies and cancer genomics. He has made fundamental contributions to predictive modeling and risk assessment, publishing more than 200 papers in leading statistical journals, such as JASA, JRSSB, Biometrika, and Biometrics, and medical journals, such as JAMA, PNAS, and JCO.