A pattern-mixture odds ratio model for incomplete categorical data
A pattern-mixture odds ratio model for incomplete categorical data
复制标题
不完整分类数据的模式混合优势比模型
DOI：
10.1080/03610929908832453
复制
发表时间：
1999
影响因子：
0.8
通讯作者：
S. Lipsitz
中科院分区：
文献类型：
--
作者：
B. Michiels;G. Molenberghs;S. Lipsitz
大多数不完整数据的模型都是在选择模型框架内制定的。模式-混合模型越来越被视为一种可行的替代方案，无论是从解释的角度还是从计算的角度来看都是如此(Little 1993，Hogan和Laird 1997，Ekholmand Skinner 1998)。虽然大多数应用要么是连续的正态分布数据，要么是简化的分类设置，如列联表，我们展示了如何使用多变量优势比模型(Molenberghs and Lesaffre 1994,1998)来拟合模式混合模型，以适应具有连续协变量的重复的二元结果。除了点估计，还提出了有用的区间估计方法，并分析了来自临床研究的数据以说明这些方法。
Most models for incomplete data are formulated within the selection model framework. Pattern-mixture models are increasingly seen as a viable alternative, both from an interpretational as well as from a computational point of view (Little 1993, Hogan and Laird 1997, Ekholm and Skinner 1998). Whereas most applications are either for continuous normally distributed data or for simplified categorical settings such as contingency tables, we show how a multivariate odds ratio model (Molenberghs and Lesaffre 1994, 1998) can be used to fit pattern-mixture models to repeated binary outcomes with continuous covariates. Apart from point estimation, useful methods for interval estimation are presented and data from a clinical study are analyzed to illustrate the methods.