报告题目：Semiparametric Probit Regression Model with General Interval-Censored Failure Time Data

报 告 人： 李树威 副教授 广州大学

报告时间：2022年9月22日 星期四10:30-11:30

报告地点：腾讯会议  297491236

校内联系人：赵世舜 zhaoss@jlu.edu.cn

报告摘要：Interval-censored data frequently arise in various biomedical areas involving periodical follow-ups where the failure or event time of interest cannot be observed exactly but is only known to fall into a time interval. This article considers a semiparametric probit regression model, a valuable alternative to other commonly used semiparametric models in survival analysis, to investigate potential risk factors for the interval-censored failure time of interest. We develop an expectation-maximization (EM) algorithm to conduct the nonparametric maximum likelihood estimation (NPMLE) using the working independence strategy for general or mixed-case interval-censored data. The resulting estimators of regression parameters are shown to be consistent, asymptotically normal, and semi-parametrically efficient. In addition, we propose a novel penalized EM algorithm for simultaneously achieving variable selection and parameter estimation in the case of high-dimensional covariates. The proposed variable selection method can be readily implemented with some existing software and considerably reduces the estimation error of the proposed NPMLE approach. Simulation studies demonstrate the satisfactory performance of the proposed methods. An application to a set of interval-censored data on prostate cancer further confirms the utility of the methodology.

报告人简介：李树威，广州大学统计系副教授、研究生导师。研究领域为生物统计、生存分析、纵向数据等。担任多个学会的常务理事和理事，主持国家自然科学基金青年基金等项目，发表多篇SCI论文。