Between- and within-cluster covariate effects in the analysis of clustered data
Between- and within-cluster covariate effects in the analysis of clustered data
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DOI：
10.2307/3109770
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发表时间：
1998-06-01
期刊：
影响因子：
1.9
通讯作者：
Kalbfleisch, JD
中科院分区：
文献类型：
--
作者：
Neuhaus, JM;Kalbfleisch, JD
聚类数据回归分析的标准方法假定模型将协变量与响应关联起来，而不考虑聚类内和聚类间的协变量效应。这些分析隐含的假设是，这些影响是相同的。示例数据表明，情况往往并非如此，忽略群内和群间协变量效应差异的分析可能会产生误导。考虑之间和集群内的影响也有助于解释观察到的和理论上的差异，混合模型分析和那些基于条件似然方法。特别是，我们表明，条件似然方法估计纯粹的集群内协变量的影响，而混合模型方法估计之间和集群内协变量的影响的加权平均值。
Standard methods for the regression analysis of clustered data postulate models relating covariates to the response without regard to between- and within-cluster covariate effects. Implicit in these analyses is the assumption that these effects are identical. Example data show that this is frequently not the case and that analyses that ignore differential between- and within-cluster covariate effects can be misleading. Consideration of between- and within-cluster effects also helps to explain observed and theoretical differences between mixture model analyses and those based on conditional likelihood methods. In particular, we show that conditional likelihood methods estimate purely within-cluster covariate effects, whereas mixture model approaches estimate a weighted average of between- and within-cluster covariate effects.