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.
This work was supported by the National Natural Science Foundation of China (Grant Nos: 11671275, 11471223, 11301355, 11690015, 11771431), Beijing Municipal Education Commission (KM202010028017), the Academy for Multidisciplinary Studies of Capital Normal University and NIH/NIA grant U01AG016976, NSFC grant.
The authors thank the Editor, an Associate Editor and three referees for their insightful comments and suggestions that greatly improved the article.
"Comparison of Two Frameworks for Analyzing Longitudinal Data." Statist. Sci. 36 (4) 530 - 541, November 2021. https://doi.org/10.1214/20-STS813