Weighted likelihood, pseudo-likelihood and maximum likelihood methods for logistic regression analysis of two-stage data
Weighted likelihood, pseudo-likelihood and maximum likelihood methods for logistic regression analysis of two-stage data
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DOI：
10.1002/(sici)1097-0258(19970115)16:1
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发表时间：
1997-01-15
影响因子：
2
通讯作者：
Holubkov, R
中科院分区：
文献类型：
--
作者：
Breslow, NE;Holubkov, R
在两阶段和其他分层抽样设计中收集的数据中拟合二元响应模型的一般方法包括加权似然、伪似然和完全最大似然。在以前的工作中，作者开发了大样本理论和方法，用于拟合逻辑回归模型到使用完全最大似然的两阶段病例对照数据。本文描述了允许使用加权、伪和全极大似然有效估计回归系数的计算算法。它还介绍了涉及连续协变量的模拟研究的结果，其中最大似然明显优于其他两种方法，并讨论了来自三个真实病例对照研究的数据分析，这些研究说明了三种方法之间的一些重要关系。最后一节讨论了两阶段方法在病例对照研究中的应用，该研究采用验证子抽样来控制测量误差。
General approaches to the fitting of binary response models to data collected in two-stage and other stratified sampling designs include weighted likelihood, pseudo-likelihood and full maximum likelihood. In previous work the authors developed the large sample theory and methodology for fitting of logistic regression models to two-stage case-control data using full maximum likelihood. The present paper describes computational algorithms that permit efficient estimation of regression coefficients using weighted, pseudo- and full maximum likelihood. It also presents results of a simulation study involving continuous covariables where maximum likelihood clearly outperformed the other two methods and discusses the analysis of data from three bonafide case-control studies that illustrate some important relationships among the three methods. A concluding section discusses the application of two-stage methods to case-control studies with validation subsampling for control of measurement error.