数学科学系

Department of Mathematical Sciences

A New Inference Approach for Joint Models of Longitudinal Data with Informative Observation and Censoring Times

报告题目A New Inference Approach for Joint Models of Longitudinal Data with Informative Observation and Censoring Times

 

报告人:孙六全(中科院数学与系统科学研究院研究员)

 

时间201349(星期二)16:00-17:00

 

地点:理科楼数学系A304会议室

 

摘要For the analysis of longitudinal data, Liang, Lu and Ying (Biometrics, 2009) proposed a novel joint model to capture the relation between the longitudinal response process and the observation times through latent variables and developed an estimation procedure under the assumptions that the distributions of the latent variables are specified and the censoring times are noninformative, which may not be true in practice. In this article, we propose a new estimation procedure for their model where these assumptions are not required anymore. Estimating equation approaches are developed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. In addition, some procedures are presented for model selection and model checking. Simulation studies demonstrate that the proposed method performs well and an application to a bladder cancer study is provided.

 

报告人简介:孙六全是中国科学院数学与系统科学研究院研究员,中国科学院数学与系统研究院概率统计室主任,统计中心副主任,中国科学院随机复杂结构与数据科学重点实验室统计部副部长。孙六全研究员长期从事生存分析、生物与医学统计、复发事件和纵向数据的统计推断、区间删失数据和各种不完全观察数据的统计分析、数理统计及其应用等方面的研究。

 

联系人:杨瑛