COMPARISON OF ALTERNATIVE REGRESSION-MODELS FOR PAIRED BINARY DATA
COMPARISON OF ALTERNATIVE REGRESSION-MODELS FOR PAIRED BINARY DATA
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DOI：
10.1002/sim.4780131005
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发表时间：
1994-05-30
影响因子：
2
通讯作者：
ROSNER, B
中科院分区：
文献类型：
--
作者：
GLYNN, RJ;ROSNER, B
我们使用来自真实眼科实例的模拟数据来评估配对二进制数据的替代Logistic回归方法的性能。所考虑的方法有：标准Logistic回归(忽略双眼之间的相关性，以受损程度较高的个体为分析单位，或仅考虑右眼)，边际Logistic回归模型，用Lipsitz，Laird和Harrington的最大似然法或梁和Zeger的估计方程方法拟合；以及条件Logistic回归模型，用Rosner的最大似然方法或Connolly和梁的估计方程方法拟合。泰勒级数近似用于比较条件参数估计和边际参数估计。对I型和II型错误率的考虑发现，标准Logistic回归的应用不如以眼睛为分析单位的方法，并考虑了双眼之间的相关性。在这些后一种方法中，没有一种方法在所考虑的条件范围内一致优于其他方法。
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 conditions considered.