Tao Sun：New Statistical Methods for Complex Survival Data with High-Dimensional Covariates2020.09.11
Topic：New Statistical Methods for Complex Survival Data with High-Dimensional Covariates
Complex survival outcomes such as multivariate and/or interval-censored endpoints become more commonly used in clinical trials, for example, in bilateral diseases or diseases with multiple comorbidities. The revolutionary development of genetics technology allows the generation of large-scale genetic data in modern clinical trials. In this talk, I will present two new statistical methods for modeling and predicting complex survival outcomes with high-dimensional covariates, motivated by two large clinical trials for studying a bilateral eye disease, the Age-related Macular Degeneration (AMD).
In the first part of my talk, I will briefly discuss a flexible copula-based semiparametric regression model for bivariate interval-censored data. The model parameters are estimated by the sieve approach and the asymptotic properties of the sieve estimators are rigorously proved. With simulation studies, I will demonstrate that the proposed method achieves satisfactory estimation and inference performances. Then I will present the novel discoveries of genetic risk variants associated with progression to late-AMD by applying the proposed method to a large-scale clinical trial study: Age-Related Macular Degeneration Study (AREDS) in a genome-wide association study (GWAS). The second part of the talk is inspired by the extraordinary achievements of deep learning in establishing powerful prediction models in the biomedical field. I will introduce a multiple-hidden-layer deep neural network (DNN) survival model to effectively extract features and make accurate and interpretable predictions. Through simulation studies and GWAS data from two large-scale clinical trial studies of AMD, I will demonstrate that the DNN model improves predictive accuracy as compared to existing methods, and provides valuable insights into early prevention and tailored intervention for AMD.
Tao Sun is an Assistant Professor in the Department of Biostatistics and Epidemiology in the School of Statistics at the Renmin University of China. He received a Ph.D. in Biostatistics from the University of Pittsburgh. He won the 2019 ENAR and ICSA Student Paper Awards. His main research interests focus on complex survival data with high-dimensional covariates, including semiparametric inference, deep learning prediction for censored outcomes, and model diagnosis for copula-based survival models. He has four publications in Biostatistics, Statistics in Medicine, Lifetime Data Analysis, and R Journal. His applications include analyzing high-dimensional bioinformatics data (genetics, RNA, single-cell) and large-scale survey data. He has two first-authored clinical publications and 11 co-authored published papers, including Science and Nature Immunology.