Leite M L, Nicolosi A
Department of Epidemiology and Medical Informatics, National Research Council, Milan, Italy.
Ophthalmic Epidemiol. 1998 Sep;5(3):117-31. doi: 10.1076/opep.5.3.117.8365.
Ophthalmological studies often deal with correlated binary outcome variables. We propose a weighted logistic regression method to account for the intraclass correlations between eyes. Using simulation studies, we compared this method with two standard logistic regression approaches: a) based on eyes as the unit of analysis and b) treating individuals classified as cases if at least one eye is affected. The considered approaches were evaluated in terms of type I error, power and estimation properties. The simulation results reveal that the subject-based approach can lead to substantial bias in regression coefficient estimates when the correlation between eyes is heterogeneous across groups or when it is low, and that power is directly affected by this bias. Furthermore, the standard eye-based approach, which ignores intrasubject correlations, leads to inflated type I error rates. The proposed weighted approach performed well in all of the situations considered. This is a simple method which can be implemented using any current statistical or epidemiological package that includes logistic regression analysis.
眼科研究常常涉及相关的二元结局变量。我们提出一种加权逻辑回归方法，以考虑双眼之间的组内相关性。通过模拟研究，我们将该方法与两种标准逻辑回归方法进行了比较：a）以眼为分析单位；b）若至少一只眼受影响，则将个体归类为病例。从一类错误、检验效能和估计特性方面对所考虑的方法进行了评估。模拟结果表明，当双眼之间的相关性在不同组间存在异质性或相关性较低时，基于个体的方法可能会导致回归系数估计出现实质性偏差，且检验效能会直接受到这种偏差的影响。此外，忽略个体内相关性的标准基于眼的方法会导致一类错误率升高。在所考虑的所有情况下，所提出的加权方法表现良好。这是一种简单的方法，可以使用任何包含逻辑回归分析的现有统计或流行病学软件包来实现。
