Selecting a Linear Mixed Model for Longitudinal Data: Repeated Measures Analysis of Variance, Covariance Pattern Model, and Growth Curve Approaches
Selecting a Linear Mixed Model for Longitudinal Data: Repeated Measures Analysis of Variance, Covariance Pattern Model, and Growth Curve Approaches
复制标题
DOI：
10.1037/a0026971
复制
发表时间：
2012-03-01
影响因子：
7
通讯作者：
Molenaar, Peter C. M.
中科院分区：
文献类型：
--
作者：
Liu, Siwei;Rovine, Michael J.;Molenaar, Peter C. M.
随着增长曲线模型的日益普及，越来越多地被认为是纵向数据分析的首选方法。尽管增长曲线方法通常是一个很好的选择，但其他建模策略可能更直接地回答感兴趣的问题。研究人员在不考虑其他建模策略的情况下拟合生长曲线模型是很常见的。在本文中，我们比较了三种分析纵向数据的方法：方差重复测量分析、协方差模式模型和增长曲线模型。由于它们都是一般线性混合模型家族的成员，它们代表了关于个体变化方式的不同假设。这些假设导致了固定效应周围残差之间不同的共变模式。在本文中，我们首先指出了每一种模型适当建模的数据类型，并使用实际数据示例来演示与生长曲线模型的总体选择相关的可能问题。然后，我们给出了一个仿真，表明赤池信息准则和贝叶斯信息准则在选择适当的残差协方差结构中的效用。这些结果使人们对不比较不同模型的拟合而自动使用生长曲线建模来处理纵向数据的流行做法产生了怀疑。最后，我们为在相关数据存在的情况下评估平均变化提供了一些实用的建议。
With increasing popularity, growth curve modeling is more and more often considered as the 1st choice for analyzing longitudinal data. Although the growth curve approach is often a good choice, other modeling strategies may more directly answer questions of interest. It is common to see researchers fit growth curve models without considering alterative modeling strategies. In this article we compare 3 approaches for analyzing longitudinal data: repeated measures analysis of variance, covariance pattern models, and growth curve models. As all are members of the general linear mixed model family, they represent somewhat different assumptions about the way individuals change. These assumptions result in different patterns of covariation among the residuals around the fixed effects. In this article, we first indicate the kinds of data that are appropriately modeled by each and use real data examples to demonstrate possible problems associated with the blanket selection of the growth curve model. We then present a simulation that indicates the utility of Akaike information criterion and Bayesian information criterion in the selection of a proper residual covariance structure. The results cast doubt on the popular practice of automatically using growth curve modeling for longitudinal data without comparing the fit of different models. Finally, we provide some practical advice for assessing mean changes in the presence of correlated data.