讲座题目：Computationally Efficient Estimation for the Generalized Odds Rate Mixture Cure Model with Interval Censored Data

主 讲 人：美国南卡罗来纳大学张佳佳副教授

讲座时间：6月23日（周五）13:30-14:20

地        点：6号学院楼402会议室

主办单位：伟德BETVlCTOR1946源于英国

讲座摘要：For semiparametric survival models with interval censored data and a cure fraction, it is often difficult to derive nonparametric maximum likelihood estimation due to the challenge in maximizing the complex likelihood function. In this paper, we propose a computationally efficient EM algorithm, facilitated by a gamma-poisson data augmentation, for maximum likelihood estimation in a class of generalized odds rate mixture cure (GORMC) models with interval censored data. The gamma-poisson data augmentation greatly simplifies the EM estimation and enhances the convergence speed of the EM algorithm. The empirical properties of the proposed method are examined through extensive simulation studies and compared with numerical maximum likelihood estimates. An R package ``GORCure" is developed to implement the proposed method and its use is illustrated by an application to the Aerobic Center Longitudinal Study dataset.

主讲人简介：张佳佳，2007年毕业于加拿大纪念大学(Memorial University)，获博士学位（生物统计），现任美国南卡罗来纳大学流行病与生物统计系终身副教授。主要从事生存分析、半参数估计方法等方面的理论与应用研究。研究方向包括生存模型、空间生存模型、混合治愈模型、脆弱模型、样本容量计算等。张佳佳博士在国际核心统计学学术期刊上发表论文40余篇，如Biometrka, Biometrics, Journal of Applied Statistics, Biometrical Journal, Lifetime Data Analysis, Statistical Methods in Medical Research，Communication in Statistics, Computational Statistics and Data Analysis，Statistics in Medicine， Statistics and Probability Letters等。主持美国卫生研究院(National Institutes of Health)项目5项。多次在国际学术会议作邀请报告及担任国际会议分会主席。担任国际学术期刊Journal of Biometrics & Biostatistics, Neurosurgery编委，多种国际核心学术期刊的审稿人，美国统计学会、中国国际统计学会、加拿大统计协会会员。

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