Clustered data analysis is characterized by the need to describe both systematic variation in a mean model and cluster‐dependent random variation in an association model. Marginalized multilevel models embrace the robustness and interpretations of a marginal mean model, while retaining the likelihood inference capabilities and flexible dependence structures of a conditional association model. Although there has been increasing recognition of the attractiveness of marginalized multilevel models, there has been a gap in their practical application arising from a lack of readily available estimation procedures. We extend the marginalized multilevel model to allow for nonlinear functions in both the mean and association aspects. We then formulate marginal models through conditional specifications to facilitate estimation with mixed model computational solutions already in place. We illustrate the MMM and approximate MMM approaches on a cerebrovascular deficiency crossover trial using SAS and an epidemiological study on race and visual impairment using R. Datasets, SAS and R code are included as supplemental materials. Copyright © 2013 John Wiley & Sons Ltd

中文翻译：

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实用的边缘化多层次模型。

聚类数据分析的特点是需要描述均值模型中的系统变异和关联模型中的聚类相关随机变异。边缘化多级模型包含边缘均值模型的稳健性和解释，同时保留条件关联模型的似然推理能力和灵活的依赖结构。尽管人们越来越认识到边缘化多层次模型的吸引力，但由于缺乏现成的估计程序，它们在实际应用中存在差距。我们扩展了边缘化的多级模型，以允许在均值和关联方面使用非线性函数。然后，我们通过条件规范制定边际模型，以促进使用已经存在的混合模型计算解决方案进行估计。我们在使用 SAS 的脑血管缺陷交叉试验和使用 R 的种族和视觉障碍流行病学研究中说明了 MMM 和近似 MMM 方法。数据集、SAS 和 R 代码作为补充材料包括在内。版权所有 © 2013 John Wiley & Sons Ltd