学术报告

Covariate-Adjusted Regression for Distorted Longitudinal Data with Informative Observation Times

阅读次数:1033

题目:Covariate-Adjusted Regression for Distorted Longitudinal Data with Informative Observation Times
报告人:赵兴球 教授 (香港理工大学应用数学系)
地点:致远楼101室
时间:2019年5月30日上午9:00
【摘要】In many longitudinal studies, repeated response and predictors are not directly observed, but can be treated as distorted by unknown functions of a common confounding covariate. Moreover, longitudinal data involve an observation process which may be informative with a longitudinal response process in practice. To deal with such complex data, we propose a class of flexible semiparametric covariate-adjusted joint models. The new models not only allow for the longitudinal response to be correlated with observation times through latent variables and completely unspecified link functions, but they also characterize distorted longitudinal response and predictors by unknown multiplicative factors depending on time and a confounding covariate. For estimation of regression parameters in the proposed models, we develop a novel covariate-adjusted estimating equation approach which does not rely on forms of link functions and distributions of frailties. The asymptotic properties of resulting parameter estimators are established and examined by simulation studies. A longitudinal data example containing calcium adsorption and intake measurements is provided for illustration.

欢迎广大师生前来参加

联系我们

    电话:86-21-65981384

    地址:上海市四平路1239号 致远楼

快速通道

Copyright © 2018  澳门新葡萄京888官网-注册娱乐所有网站 版权所有.

XML 地图 | Sitemap 地图