Breslow N E, Holubkov R
Department of Biostatistics, University of Washington, Seattle 98195-7232, USA.
Stat Med. 1997;16(1-3):103-16. doi: 10.1002/(sici)1097-0258(19970115)16:1<103::aid-sim474>3.0.co;2-p.
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 bona fide 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.
将二元响应模型应用于两阶段及其他分层抽样设计中所收集数据的一般方法包括加权似然法、拟似然法和完全最大似然法。在之前的工作中，作者们运用完全最大似然法，针对两阶段病例对照数据，开发了用于拟合逻辑回归模型的大样本理论和方法。本文描述了一些计算算法，这些算法允许使用加权、拟似然和完全最大似然法对回归系数进行有效估计。本文还给出了一项涉及连续协变量的模拟研究结果，其中最大似然法明显优于其他两种方法，并讨论了来自三项真实病例对照研究的数据的分析情况，这些分析阐明了这三种方法之间的一些重要关系。结语部分讨论了两阶段方法在具有验证子样本以控制测量误差的病例对照研究中的应用。
