Functional regression analysis using an F test for longitudinal data with large numbers of repeated measures.
Functional regression analysis using an F test for longitudinal data with large numbers of repeated measures.
复制标题
使用 F 检验对具有大量重复测量的纵向数据进行函数回归分析。
DOI：
10.1002/sim.2609
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发表时间：
2007
影响因子：
2
通讯作者：
Shoptaw,Steven
中科院分区：
文献类型：
--
作者：
Yang,Xiaowei;Shen,Qing;Xu,Hongquan;Shoptaw,Steven
来自生物医学研究某些领域的纵向数据集通常由对每个主题重复测量的几个变量组成，产生大量的观察结果。这一特点使传统纵向建模策略的使用变得复杂，传统纵向建模策略主要是针对每个受试者重复测量次数相对较少的研究开发的。一个创新的方法来模拟这样的“广泛”的数据是应用功能回归分析，一个新兴的统计方法，其中观察同一主题被视为一个样本从功能空间。Shen和Faraway介绍了一个函数响应线性模型的F检验。本文阐述了如何将F检验和函数回归分析应用于纵向数据的设置。对美沙酮维持的吸烟者的戒烟研究进行了分析以供证明。在估计治疗效应时，函数回归分析提供了有意义的临床解释，而functionalF检验提供了由混合效应线性回归模型支持的一致结果。在吸烟数据的条件下进行模拟研究，以考察F检验、Wilks似然比检验和使用AIC的线性混合效应模型的统计功效。版权所有© 2006约翰威利父子有限公司。
Longitudinal data sets from certain fields of biomedical research often consist of several variables repeatedly measured on each subject yielding a large number of observations. This characteristic complicates the use of traditional longitudinal modelling strategies, which were primarily developed for studies with a relatively small number of repeated measures per subject. An innovative way to model such ‘wide’ data is to apply functional regression analysis, an emerging statistical approach in which observations of the same subject are viewed as a sample from a functional space. Shen and Faraway introduced anFtest for linear models with functional responses. This paper illustrates how to apply thisFtest and functional regression analysis to the setting of longitudinal data. A smoking cessation study for methadone‐maintained tobacco smokers is analysed for demonstration. In estimating the treatment effects, the functional regression analysis provides meaningful clinical interpretations, and the functionalFtest provides consistent results supported by a mixed‐effects linear regression model. A simulation study is also conducted under the condition of the smoking data to investigate the statistical power for theFtest, Wilks' likelihood ratio test, and the linear mixed‐effects model using AIC. Copyright © 2006 John Wiley & Sons, Ltd.