报告人：Yangxin Huang(黄养新教授，美国University of South Florida公共卫生学院)

报告题目：Bayesian Approach on Mixture of Joint Models for Survival and Longitudinal Data with Multiple Features

报告摘要：It often happens in longitudinal studies that repeated measurements of markers are observed with various data features of a heterogeneous population comprising of several subclasses, left-censoring due to a limit of detection (LOD) and covariates measured with error. Moreover, repeatedly measured markers in time may be associated with a time-to-event of interest. Inferential procedures may become very complicated when one analyzes data with these features together. This talk explores a finite mixture of hierarchical joint models of event times and longitudinal measures with an attempt to alleviate departures from homogeneous characteristics, tailor observations below LOD as missing values, mediate accuracy from measurement error in covariate and overcome shortages of confidence in specifying a parametric time-to-event model with a nonparametric distribution. The Bayesian joint modeling is employed to not only estimate all parameters in mixture of joint models, but also evaluate probabilities of class membership. A real data example is analyzed to demonstrate the methodology by jointly modeling the viral dynamics and the time to decrease in CD4/CD8 ratio in the presence of CD4 cell counts with measurement error and the analytic results are reported by comparing potential models for various scenarios.

报告人简介：黄养新，统计学博士。2000年英国利物浦John Moores 大学获统计博士学位。美国南佛罗里达大学终身教授、博士生导师，合作研究中心副主任,生物统计学博士委员会主任。美国统计协会(ASA)，英国皇家统计学会(RSS)，美国数学与统计学院(MSI), 泛华统计协会(ICSA)，美国公共健康协会(APHA)的永久会员。黄养新博士在贝叶斯方法及其马尔可夫链蒙特卡罗算法，纵向数据和生存数据的联合模型，偏正态纵向数据的非线性混合效应模型，非线性混合效应分位数回归模型，偏正态纵向数据和生存数据的非参数混合效应模型，缺失的数据分析，测量误差模型，微分方程动力系统，AIDS临床试验研究，传染病建模与预测，调查数据分析等主要研究领域取得了重要创新成果与应用。正在主持和合作主持10多项美国国家科学基金和美国国立卫生研究院研究项目。出版一部英文版专著。近20年在国际主要统计和应用学术期刊发表论文140余篇。

报告时间： 2016年11月25日（星期五）下午3：30 - 4：30.

报告地点：科技楼南楼702