Gao S, Hui S L
Department of Medicine, Indiana University School of Medicine, Indianapolis 46202-5200, USA.
Stat Med. 1997 Nov 15;16(21):2419-28. doi: 10.1002/(sici)1097-0258(19971115)16:21<2419::aid-sim686>3.0.co;2-e.
Maximum likelihood methods are used to incorporate partially observed covariate values in fitting logistic regression models. We extend these methods to data collected through complex surveys using the pseudo-likelihood approach. One can obtain parameter estimates of the logistic regression model using standard statistical software and their standard errors by Taylor series expansion or the jackknife method. We apply the approach to data from a two-phase survey screening for dementia in a community sample of African Americans age 65 and older living in Indianapolis. The binary response variable is dementia and the covariate with missing values is a daily functioning score collected from interviews with a relative of the study subject.
最大似然法用于在拟合逻辑回归模型时纳入部分观测到的协变量值。我们使用伪似然方法将这些方法扩展到通过复杂调查收集的数据。可以使用标准统计软件获得逻辑回归模型的参数估计值，并通过泰勒级数展开或刀切法获得其标准误差。我们将该方法应用于对印第安纳波利斯市65岁及以上非裔美国人社区样本进行痴呆症筛查的两阶段调查数据。二元响应变量是痴呆症，具有缺失值的协变量是从对研究对象亲属的访谈中收集的日常功能评分。
