Davis C S
Department of Preventive Medicine, University of Iowa, Iowa City 52242.
Stat Med. 1991 Dec;10(12):1959-80. doi: 10.1002/sim.4780101210.
Techniques applicable for the analysis of longitudinal data when the response variable is non-normal are not nearly as comprehensive as for normally-distributed outcomes. However, there have been several recent developments. Semi-parametric and non-parametric methodology for the analysis of repeated measurements is reviewed. The commonly encountered design in which, for each subject, one assesses a univariate response variable at multiple fixed time points, is considered. The types of outcomes considered include binary, ordered categorical, and continuous (but extremely non-normal) response variables. All of the methods considered allow for incomplete data due to the occurrence of missing observations. In addition, discrete and/or continuous covariates, which may be time-dependent, are accommodated by some of the approaches. The methods are demonstrated using data from three clinical trials.
当响应变量非正态分布时，适用于纵向数据分析的技术远不如用于正态分布结果的技术那样全面。然而，最近有了一些新进展。本文回顾了用于重复测量分析的半参数和非参数方法。考虑了常见的设计，即在多个固定时间点对每个受试者评估一个单变量响应变量。所考虑的结果类型包括二元、有序分类和连续（但极非正态）响应变量。所有考虑的方法都允许因缺失观测值而出现不完整数据。此外，一些方法还考虑了可能随时间变化的离散和/或连续协变量。使用来自三项临床试验的数据对这些方法进行了演示。
