Real longitudinal data analysis for real people: Building a good enough mixed model
Real longitudinal data analysis for real people: Building a good enough mixed model
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DOI：
10.1002/sim.3775
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发表时间：
2010-02-20
影响因子：
2
通讯作者：
Muller, Keith E.
中科院分区：
文献类型：
--
作者：
Cheng, Jing;Edwards, Lloyd J.;Muller, Keith E.
混合效应模型已经变得非常流行，特别是对于纵向数据的分析。一个挑战是如何建立一个足够好的混合效应模型。在本文中，我们提出了应对这一挑战的系统策略，并介绍了易于实施的实用建议来构建混合效应模型。对科学策略的一般性讨论激发了推荐的模型拟合五步程序。对均值结构（固定效应）和协方差结构（随机效应和残差）进行建模的需要创造了基本的灵活性和复杂性。一些非常实用的建议有助于克服复杂性。所有预测变量的居中、缩放和全秩编码从根本上提高了收敛的机会、计算速度和数值精度。将单变量线性模型的计算和假设诊断应用于混合模型数据极大地有助于检测和解决相关的计算问题。将单变量线性模型的计算和假设诊断应用于混合模型数据可以从根本上提高收敛的机会、计算速度和数值精度。该方法有助于拟合更一般的协方差模型，这是选择防御推理所需的可信协方差模型的关键步骤。推荐策略的详细论证基于一项已发表的研究数据，该研究是一项预防青少年饮酒的多成分干预随机试验。讨论强调了混合模型需要额外的协方差和推理工具。讨论还强调需要改进科学家和统计学家教授和审查寻找足够好的混合模型的过程的方式。版权所有 (C) 2009 John Wiley & Sons, Ltd.
Mixed effects models have become very popular, especially for the analysis of longitudinal data. One challenge is how to build a good enough mixed effects model. In this paper, we suggest a systematic strategy for addressing this challenge and introduce easily implemented practical advice to build mixed effects models. A general discussion of the scientific strategies motivates the recommended five-step procedure for model fitting. The need to model both the mean structure (the fixed effects) and the covariance structure (the random effects and residual error) creates the fundamental flexibility and complexity. Some very practical recommendations help to conquer the complexity. Centering, scaling, and full-rank coding of all the predictor variables radically improve the chances of convergence, computing speed, and numerical accuracy. Applying computational and assumption diagnostics from univariate linear models to mixed model data greatly helps to detect and solve the related computational problems. Applying computational and assumption diagnostics from the univariate linear models to the mixed model data can radically improve the chances of convergence, computing speed, and numerical accuracy. The approach helps to fit more general covariance models, a crucial step in selecting a credible covariance model needed for defensible inference. A detailed demonstration of the recommended strategy is based on data from a published study of a randomized trial of a multicomponent intervention to prevent young adolescents' alcohol use. The discussion highlights a need for additional covariance and inference tools for mixed models. The discussion also highlights the need for improving how scientists and statisticians teach and review the process of finding a good enough mixed model. Copyright (C) 2009 John Wiley & Sons, Ltd.