Statistical Issues in Longitudinal Data Analysis for Treatment Efficacy Studies in the Biomedical Sciences
Statistical Issues in Longitudinal Data Analysis for Treatment Efficacy Studies in the Biomedical Sciences
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DOI：
10.1038/mt.2010.127
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发表时间：
2010-09-01
影响因子：
12.4
通讯作者：
Kim, Mi-Ok
中科院分区：
文献类型：
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作者：
Liu, Chunyan;Cripe-, Timothy P.;Kim, Mi-Ok
纵向收集的结果在细胞生物学和基因治疗研究中越来越普遍。在本文中，我们回顾了这些领域纵向数据统计分析的当前实践，并在大多数统计软件包中推荐了“表现最佳”的统计方法。对发表在《分子疗法》上的论文的调查表明，只有一小部分文章正确地分析了纵向数据，最流行的方法是使用方差分析（ANOVA）模型和Tukey事后检验分别分析每个测量时间点数据。我们表明，首先，这种横断面方差分析方法并没有利用研究纵向设计提供的所有权力，其次，在每个测量时间单独应用Tukey的事后检验可能导致假阳性率高达30%使用模拟研究。我们推荐混合效应模型分析。我们还讨论了由于纵向数据中存在的实验单元内部相关性而导致的事后检验中多重比较调整的复杂性。我们推荐重采样作为一种方法，它可以很容易地调整事后测试，使其仅限于有趣的比较，从而避免过度牺牲功率。
Longitudinally collected outcomes are increasingly common in cell biology and gene therapy research. In this article, we review the current practice of statistical analysis of longitudinal data in these fields, and recommend the "best performing" statistical method among those available in most statistical packages. A survey of papers published in Molecular Therapy indicates that longitudinal data are only properly analyzed in a small fraction of articles, and the most popular approach was analyzing each measurement time point data separately using an analysis of variance (ANOVA) model with Tukey's post hoc tests. We show that first, such cross-sectional ANOVA approach does not utilize all the power that the longitudinal design of a study provides, and second, Tukey's post hoc tests applied at each measurement time separately could result in a false positivity rate as high as 30% using a simulation study. We recommend mixed effects model analysis instead. We also discuss the complexities of multiple comparison adjustment in the post hoc testing that result from within experimental unit correlation existing in longitudinal data. We recommend resampling as a method that readily adjusts the post hoc testing to be limited to only interesting comparisons and thereby avoids unduly sacrificing the power.