Random-effects regression models for clustered data with an example from smoking prevention research.
Random-effects regression models for clustered data with an example from smoking prevention research.
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DOI：
10.1037//0022-006x.62.4.757
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发表时间：
1994-08
影响因子：
5.9
通讯作者：
Donald Hedeker;Robert D. Gibbons;Brian R. Flay
中科院分区：
文献类型：
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作者：
Donald Hedeker;Robert D. Gibbons;Brian R. Flay
本文提出了一个随机效应回归模型用于聚类数据的分析。与聚类数据的普通回归分析不同，随机效应回归模型并不假设每个观测值都是独立的，而是假设聚类中的数据在某种程度上是相关的。这种依赖性的程度是沿着与通常的模型参数的估计，从而调整这些影响的依赖性，从聚类的数据。最大边际似然解的描述，和现有的统计软件的模型进行了讨论。一个数据集的分析，其中学生聚集在教室和学校是用来说明随机效应回归分析的功能，相对于个人层面的分析，忽略了数据的聚类，和教室层面的分析，聚合的个人数据。
A random-effects regression model is proposed for analysis of clustered data. Unlike ordinary regression analysis of clustered data, random-effects regression models do not assume that each observation is independent but do assume that data within clusters are dependent to some degree. The degree of this dependency is estimated along with estimates of the usual model parameters, thus adjusting these effects for the dependency resulting from the clustering of the data. A maximum marginal likelihood solution is described, and available statistical software for the model is discussed. An analysis of a dataset in which students are clustered within classrooms and schools is used to illustrate features of random-effects regression analysis, relative to both individual-level analysis that ignores the clustering of the data, and classroom-level analysis that aggregates the individual data.