Kenward M G, Jones B
Department of Applied Statistics, University of Reading, England.
J Biopharm Stat. 1992;2(2):137-70. doi: 10.1080/10543409208835036.
The purpose of this paper is to describe, illustrate, and compare a number of different approaches to the analysis of repeated binary and categorical data. These approaches include empirical generalized least squares and generalized estimating equations, as well as traditional log-linear modeling methods. It is shown that the interpretation of the parameters in the various models depends critically on the type of model fitted. In particular, we contrast the population-averaged and subject-specific models. Two example data sets are used to illustrate the approaches, and throughout we concentrate on methods that can be easily implemented.
本文的目的是描述、阐释并比较多种分析重复二元和分类数据的不同方法。这些方法包括经验广义最小二乘法和广义估计方程，以及传统的对数线性建模方法。结果表明，各种模型中参数的解释关键取决于所拟合模型的类型。特别是，我们对比了总体平均模型和个体特定模型。使用两个示例数据集来说明这些方法，并且在整个过程中我们专注于易于实施的方法。
