Neuhaus J M
Department of Epidemiology and Biostatistics, University of California, San Francisco 94143-0560.
Biometrics. 1993 Dec;49(4):989-96.
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.
已经提出了几种方法来分析聚集性二元数据，这种数据出现在诸如畸形学和眼科学等领域。这些方法包括混合效应和拟似然方法，以及将聚类响应用作协变量的模型。这三种方法衡量协变量对二元响应的不同效应，但简单的近似关系将它们参数的大小联系起来。在本文中，我们给出了近似关系，以关联从不同方法获得的模型参数的标准误差和协变量效应的 Wald 检验。这些近似关系表明，使用不同方法时，涉及聚类水平协变量的 Wald 检验将大致等效。然而，对聚类内相关性进行建模的方法，如混合效应模型，比不建模相关性的方法对聚类内协变量提供更强大的检验。模拟和示例数据说明了这些发现。
