Glynn R J, Rosner B
Department of Medicine, Harvard Medical School, Boston, MA.
Stat Med. 1994 May 30;13(10):1023-36. doi: 10.1002/sim.4780131005.
We used simulated data, derived from real ophthalmologic examples, to evaluate the performance of alternative logistic regression approaches for paired binary data. Approaches considered were: standard logistic regression (ignoring the correlation between fellow eyes, treating individuals classified on the basis of their more impaired eye as the unit of analysis, or considering only right eyes); marginal logistic regression models fitted by the maximum likelihood approach of Lipsitz, Laird and Harrington or the estimating equation approach of Liang and Zeger; and conditional logistic regression models fitted by the maximum likelihood approach of Rosner or the estimating equation approach of Connolly and Liang. Taylor series approximations were used to compare conditional and marginal parameter estimates. Consideration of type I and II error rates found application of standard logistic regression to be inferior to methods that treated the eye as the unit of analysis and accounted for the correlation between fellow eyes. Among these latter approaches, none was uniformly superior to the others across the range of condition considered.
我们使用从真实眼科实例中得出的模拟数据，来评估用于配对二元数据的替代逻辑回归方法的性能。所考虑的方法包括：标准逻辑回归（忽略双眼之间的相关性，将根据其受影响更严重的眼睛进行分类的个体作为分析单位，或仅考虑右眼）；通过Lipsitz、Laird和Harrington的最大似然法或Liang和Zeger的估计方程法拟合的边际逻辑回归模型；以及通过Rosner的最大似然法或Connolly和Liang的估计方程法拟合的条件逻辑回归模型。使用泰勒级数近似来比较条件和边际参数估计。对I型和II型错误率的考量发现，应用标准逻辑回归不如将眼睛作为分析单位并考虑双眼之间相关性的方法。在这些后一种方法中，在所考虑的条件范围内，没有一种方法始终优于其他方法。
