两种纵向数据分析框架的比较

IF 3.9 1区数学 Q1 STATISTICS & PROBABILITY

Statistical Science Pub Date : 2021-01-01 DOI:10.1214/20-sts813

Jie Zhou, Xiao Zhou, Liuquan Sun

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引用次数: 0

摘要

在纵向数据随机设计下，观测时间是不规则的，分析这类纵向数据主要有两种框架。一种是聚类数据框架，另一种是计数过程框架。在本文中，我们在数据结构、模型假设和估计过程方面对这两种框架进行了全面的比较。我们发现，当观测次数与协变量相关而与给定协变量的纵向响应无关时，在计数过程框架中对观测次数建模将不会获得任何效率。进行了仿真研究，比较了相关估计器的有限样本行为，并对阿尔茨海默病研究的真实数据进行了分析，进一步进行了比较。

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Comparison of Two Frameworks for Analyzing Longitudinal Data

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.

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来源期刊

Statistical Science 数学-统计学与概率论

CiteScore

6.50

自引率

1.80%

发文量

审稿时长

>12 weeks

期刊介绍： The central purpose of Statistical Science is to convey the richness, breadth and unity of the field by presenting the full range of contemporary statistical thought at a moderate technical level, accessible to the wide community of practitioners, researchers and students of statistics and probability.