Comparison of subject-specific and population averaged models for count data from cluster-unit intervention trials
Comparison of subject-specific and population averaged models for count data from cluster-unit intervention trials
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DOI：
10.1177/0962280206071931
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发表时间：
2007-01-01
影响因子：
2.3
通讯作者：
Wolfson, Mark
中科院分区：
文献类型：
--
作者：
Young, Mary L.;Preisser, John S.;Wolfson, Mark
个体特异性（SS）广义线性混合模型的最大似然估计技术和边际或群体平均（PA）模型的广义估计方程常用于聚类单元干预试验的分析。虽然这两类程序占集群内相关性的存在，包括干预效应参数的固定效应的解释不同的SS和PA模型。此外，通常缺乏与来自两种相应方法的SS和PA参数相关的封闭形式的数学表达式。本文研究了相关泊松响应的特殊情况，其中，对于具有正态随机效应的对数线性模型，精确的关系是可用的。本文导出了两种常用的统计学模型在巢式横断面整群试验计数资料分析中的等价PA模型表示。数学结果说明了从一个大型的非随机聚类试验，以减少未成年人饮酒的计数数据。在各自的平均值和协方差模型的参数之间的关系的知识是必不可少的理解这两种方法的经验比较。
Maximum likelihood estimation techniques for subject-specific (SS) generalized linear mixed models and generalized estimating equations for marginal or population-averaged (PA) models are often used for the analysis of cluster-unit intervention trials. Although both classes of procedures account for the presence of within-cluster correlations, the interpretations of fixed effects including intervention effect parameters differ in SS and PA models. Furthermore, closed-form mathematical expressions relating SS and PA parameters from the two respective approaches are generally lacking. This paper investigates the special case of correlated Poisson responses where, for a log-linear model with normal random effects, exact relationships are available. Equivalent PA model representations of two SS models commonly used in the analysis of nested cross-sectional cluster trials with count data are derived. The mathematical results are illustrated with count data from a large non-randomized cluster trial to reduce underage drinking. Knowledge of relationships among parameters in the respective mean and covariance models is essential to understanding empirical comparisons of the two approaches.