统计学系系列讲座之275期

时间：2017 年 11月13日（星期一）13:30-14:30

地点：史带楼502室

主持人：张新生 教授 复旦大学管理学院统计学系

主题：Conditional Modeling of Longitudinal Data with Terminal Event

主讲人：Professor Bin Nan

Department of Statistics, University of California at Irvine

简介：Bin Nan 教授是美国统计学会（ASA）和国际数理统计学会（IMS）的Fellow、以及国际统计研究会（ISI）Elected Member。目前担任统计期刊Statistics in Biosciences 和 The Canadian Journal of Statistics的Associate Editor。在JASA，AOS，AOAS，Biometrika等国际期刊上发表论文超过100篇，他的研究兴趣主要集中在生存分析、高维大脑图像的数据分析、纵向数据的变点分析等研究领域。

摘要：We consider a random effects model for longitudinal data with the occurrence of an informative terminal event that is subject to right censoring. Existing methods for analyzing such data include the joint modeling approach using latent frailty and the marginal estimating equation approach using inverse probability weighting; in both cases the effect of the terminal event on the response variable is not explicit and thus not easily interpreted. In contrast, we treat the terminal event time as a covariate in a conditional model for the longitudinal data, which provides a straightforward interpretation while keeping the usual relationship of interest between the longitudinally measured response variable and covariates for times that are far from the terminal event. A two-stage semiparametric likelihood-based approach is proposed for estimating the regression parameters; first, the conditional distribution of the right-censored terminal event time given other covariates is estimated and then the likelihood function for the longitudinal event given the terminal event and other regression parameters is maximized. The method is illustrated by numerical simulations and by analyzing medical cost data for patients with end-stage renal disease. This is a joint work with Shengchun Kong and Jack Kalbfleisch.

    统计学系 

2017-11-9