Zhang Hui, Lu Naiji, Feng Changyong, Thurston Sally W, Xia Yinglin, Zhu Liang, Tu Xin M
Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, U.S.A..
Stat Med. 2011 Sep 10;30(20):2562-72. doi: 10.1002/sim.4265. Epub 2011 Jun 10.
The generalized linear mixed-effects model (GLMM) is a popular paradigm to extend models for cross-sectional data to a longitudinal setting. When applied to modeling binary responses, different software packages and even different procedures within a package may give quite different results. In this report, we describe the statistical approaches that underlie these different procedures and discuss their strengths and weaknesses when applied to fit correlated binary responses. We then illustrate these considerations by applying these procedures implemented in some popular software packages to simulated and real study data. Our simulation results indicate a lack of reliability for most of the procedures considered, which carries significant implications for applying such popular software packages in practice.
广义线性混合效应模型（GLMM）是一种将横断面数据模型扩展到纵向数据设置的常用范式。当应用于二元反应建模时，不同的软件包甚至同一软件包内的不同程序可能会给出截然不同的结果。在本报告中，我们描述了这些不同程序背后的统计方法，并讨论了将它们应用于拟合相关二元反应时的优缺点。然后，我们通过将一些流行软件包中实现的这些程序应用于模拟和实际研究数据来说明这些注意事项。我们的模拟结果表明，所考虑的大多数程序缺乏可靠性，这对在实际中应用此类流行软件包具有重要影响。
