ESTIMATION EFFICIENCY AND TESTS OF COVARIATE EFFECTS WITH CLUSTERED BINARY DATA
ESTIMATION EFFICIENCY AND TESTS OF COVARIATE EFFECTS WITH CLUSTERED BINARY DATA
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DOI：
10.2307/2532241
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发表时间：
1993-12-01
期刊：
影响因子：
1.9
通讯作者：
NEUHAUS, JM
中科院分区：
文献类型：
--
作者：
NEUHAUS, JM
已经提出了几种方法来分析聚集的二进制数据，这些数据出现在畸形学和眼科学等领域。这些方法包括混合效应方法和准似然方法，以及使用集群反应作为协变量的模型。这三种方法测量协变量对二元反应的不同影响，但简单的近似与它们参数的大小有关。在本文中，我们给出了模型参数的标准误差之间的近似关系，以及从不同方法获得的协变量效应的Wald检验。这些近似表明，使用不同的方法，涉及簇级协变量的Wald检验将大致等价。然而，建模集群内相关性的方法，如混合效应模型，比那些没有建模相关性的方法提供了更强大的集群内协变量测试。模拟和实例数据说明了这些发现。
Several approaches have been proposed to analyze clustered binary data, which arise in fields such as teratology and ophthalmology. These methods include mixed-effects and quasi-likelihood approaches, as well as models that use cluster responses as covariates. The three approaches measure different effects of covariates on binary responses, but simple approximations relate the magnitudes of their parameters. In this article, we present approximations to relate the standard errors of model parameters and Wald tests for covariate effects obtained from the different approaches. These approximations show that Wald tests involving cluster-level covariates will be approximately equivalent using the different approaches. However, approaches that model intracluster correlation, such as the mixed-effects model, provide more powerful tests of within-cluster covariates than those that do not model the correlation. Simulations and example data illustrate these findings.