Fitzmaurice G M, Laird N M, Lipsitz S R
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115.
Biometrics. 1994 Sep;50(3):601-12.
In this paper, we describe a likelihood-based method for analysing balanced but incomplete longitudinal binary responses that are assumed to be missing at random. Following the approach outlined in Zhao and Prentice (1990, Biometrika 77, 642-648), we focus on "marginal models" in which the marginal expectation of the response variable is related to a set of covariates. The association between binary responses is modelled in terms of conditional log odds-ratios. We describe a set of scoring equations for jointly estimating both the marginal parameters and the conditional association parameters. An outline of the EM algorithm used to obtain the maximum likelihood estimates is presented. This approach yields valid and efficient estimates when the responses are missing at random, but not necessarily missing completely at random. An example, using data from the Muscatine Coronary Risk Factor Study, is presented to illustrate this methodology.
在本文中，我们描述了一种基于似然性的方法，用于分析平衡但不完整的纵向二元反应，这些反应被假定为随机缺失。遵循Zhao和Prentice（1990年，《生物统计学》77卷，642 - 648页）中概述的方法，我们关注“边际模型”，其中反应变量的边际期望与一组协变量相关。二元反应之间的关联通过条件对数优势比进行建模。我们描述了一组用于联合估计边际参数和条件关联参数的计分方程。给出了用于获得最大似然估计的EM算法的概述。当反应是随机缺失而非一定完全随机缺失时，该方法能产生有效且高效的估计。给出了一个使用马斯卡廷冠心病危险因素研究数据的示例来说明这种方法。
