Time：2020/9/15 10:00-11:00

Form：Tencent Meeting

Topic：New Statistical Methods for Complex Survival Data with High-Dimensional Covariates

Abstract:

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.

Resume:

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.