Comparison of Two Frameworks for Analyzing Longitudinal Data

被引：1
|
作者：
Zhou, Jie [1 ]
Zhou, Xiao-Hua [2 ,3 ]
Sun, Liuquan [4 ]
机构：
[1] Capital Normal Univ, Sch Math, Beijing 100048, Peoples R China
[2] Peking Univ, Sch Publ Hlth, Dept Biostat, Beijing 100871, Peoples R China
[3] Peking Univ, Beijing Int Ctr Math Res, Beijing 100871, Peoples R China
[4] Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing 100190, Peoples R China
来源：
STATISTICAL SCIENCE | 2021年 / 36卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Clustered data framework; counting process framework; estimation procedures; longitudinal data; INFORMATIVE OBSERVATION; SEMIPARAMETRIC REGRESSION; MISSING DATA; MODEL; TIMES;
D O I：
10.1214/20-STS813
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Under the random design of longitudinal data, observation times are irregular, and there are mainly two frameworks for analyzing such kind of longitudinal data. One is the clustered data framework and the other is the counting process framework. In this paper, we give a thorough comparison of these two frameworks in terms of data structure, model assumptions and estimation procedures. We find that modeling the observation times in the counting process framework will not gain any efficiency when the observation times are correlated with covariates but independent of the longitudinal response given covariates. Some simulation studies are conducted to compare the finite sample behaviors of the related estimators, and a real data analysis of the Alzheimer's disease study is implemented for further comparison.
引用
收藏
页码：530 / 541
页数：12
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