Extended generalized estimating equations for clustered data
Extended generalized estimating equations for clustered data
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DOI：
10.2307/2670052
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发表时间：
1998-12-01
影响因子：
3.7
通讯作者：
Severini, TA
中科院分区：
文献类型：
--
作者：
Hall, DB;Severini, TA
通常，对一个簇上由多个观测数据组成的数据的分析由于簇内相关而变得复杂。聚类数据广义线性建模的估计方程近年来受到广泛关注。本文对Liang和Zeger提出的广义估计方程方法进行了扩展，将聚类内相关作为干扰参数。利用扩展拟似然的思想，同时给出了回归和关联参数的估计方程。在一定条件下，证明了所得到的估计量是渐近正态的和相合的。回归估计的一致性允许对重复响应之间的相关性进行不正确的建模。用一项发育毒性研究的数据分析说明了这种方法。
Typically, analysis of data consisting of multiple observations on a cluster is complicated by within-cluster correlation. Estimating equations for generalized linear modeling of clustered data have recently received much attention. This article proposes an extension to the generalized estimating equation method proposed by Liang and Zeger, which treats within-cluster correlations as nuisance parameters. Using ideas from extended quasi-likelihood, estimating equations for regression and association parameters are provided simultaneously. The resulting estimators are proven to be asymptotically normal and consistent under certain conditions. The consistency of regression estimators allows incorrect modeling of the correlation among repeated responses. The method is illustrated with an analysis of data from a developmental toxicity study.