Robust estimation for panel count data with informative observation times and censoring times

被引：0
作者：
Hangjin Jiang
Wen Su
Xingqiu Zhao
机构：
[1] ZheJiang University,Center for Data Science
[2] The Chinese University of Hong Kong,Department of Statistics
[3] Haitong International Securities Group,Department of Applied Mathematics
[4] The Hong Kong Polytechnic University,undefined
来源：
Lifetime Data Analysis | 2020年 / 26卷
关键词：
Semiparametric regression; Panel count data; Informative observation times; Informative censoring times; Robust estimation;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
We consider the semiparametric regression of panel count data occurring in longitudinal follow-up studies that concern occurrence rate of certain recurrent events. The analysis of panel count data involves two processes, i.e, a recurrent event process of interest and an observation process controlling observation times. However, the model assumptions of existing methods, such as independent censoring time and Poisson assumption, are restrictive and questionable. In this paper, we propose new joint models for panel count data by considering both informative observation times and censoring times. The asymptotic normality of the proposed estimators are established. Numerical results from simulation studies and a real data example show the advantage of the proposed method.
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页码：65 / 84
页数：19
相关论文
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