Maurissen Jacques P, Vidmar Thomas J
JPM NeuroTox, LLC, 2415 N Woodland Estates Drive, Midland, MI 48642, USA.
BioSTAT Consultants, 528 West Centre Avenue, Portage, MI 49024, USA.
Neurotoxicol Teratol. 2017 Jan-Feb;59:78-84. doi: 10.1016/j.ntt.2016.10.003. Epub 2016 Oct 13.
Repeated-measure analysis of variance is a general term that can imply a number of different statistical models used to analyze data from studies in which measurements are taken from each subject on more than one occasion. Repeated-measure analyses encompass univariate models (with or without sphericity adjustment), multivariate models, mixed models, analysis of covariance, multilevel models, latent growth models, and hybrids of these models. These models are based on different assumptions, especially regarding correlations (sphericity) between within-subject factors, which comprise the variance-covariance matrix. Violation of this assumption may lead to misleading and erroneous conclusions. Because many papers do not provide enough information about what analysis was really conducted, and about why it was done, the reader is unable to evaluate the validity of the analysis. Here a brief overview of several of the most commonly used models for analyzing data from repeated-measure designs is provided, and guidance is suggested for describing the statistical approach employed. The goals of this paper are (1) to give authors an overview of the diversity of commonly used models and associated assumptions, and (2) to facilitate reporting sufficient information about the tests to allow the reader to evaluate the validity of the tests and the credibility of the inferences made by the authors. Among the available approaches to repeated-measure analyses, the mixed model is recommended for its flexibility in handling different covariance structures and its insensitivity to missing data. Whether or not it is used, the overall guiding principles in reporting should always be Accuracy, Completeness, and Transparency (ACT principles): tell the reader precisely all what you did and why.
重复测量方差分析是一个通用术语，它可以指代多种不同的统计模型，这些模型用于分析来自这样一类研究的数据：在这些研究中，对每个受试者进行不止一次的测量。重复测量分析包括单变量模型（有或没有球形性调整）、多变量模型、混合模型、协方差分析、多层次模型、潜在增长模型以及这些模型的混合形式。这些模型基于不同的假设，尤其是关于受试者内因素之间的相关性（球形性），这些因素构成了方差协方差矩阵。违反这一假设可能会导致误导性和错误的结论。由于许多论文没有提供足够的信息说明实际进行了何种分析以及为何这样做，读者无法评估分析的有效性。在此，提供了对几种最常用的用于分析重复测量设计数据的模型的简要概述，并针对描述所采用的统计方法给出了建议。本文的目标是：（1）让作者了解常用模型及其相关假设的多样性；（2）便于报告关于检验的足够信息，使读者能够评估检验的有效性以及作者所做推断的可信度。在现有的重复测量分析方法中，混合模型因其在处理不同协方差结构方面的灵活性以及对缺失数据的不敏感性而被推荐。无论是否使用混合模型，报告中的总体指导原则都应该始终是准确性、完整性和透明度（ACT原则）：准确地告诉读者你所做的一切以及原因。
