Gray S M, Brookmeyer R
Department of Biostatistics, Johns Hopkins University, School of Hygiene and Public Health, Baltimore, Maryland 21205, USA.
Biometrics. 1998 Sep;54(3):976-88.
Multidimensional longitudinal data result when researchers measure an outcome through time that is quantified by many different response variables. These response variables are often defined on different numerical scales. The objective of this paper is to present a method to summarize and estimate an overall treatment effect from this type of longitudinal data. A regression model is proposed that assumes the treatment effect can be parameterized as an acceleration or deceleration of the time scale of each response variable's trajectory. Generalized estimating equations are used to estimate the model parameters. Cognitive and functional ability data from Alzheimer's disease patients and quality of life data from an AIDS clinical trial are used to illustrate the model.
当研究人员通过时间来测量一个由许多不同响应变量量化的结果时，就会产生多维纵向数据。这些响应变量通常是在不同的数值尺度上定义的。本文的目的是提出一种方法，用于总结和估计这类纵向数据的总体治疗效果。提出了一种回归模型，该模型假设治疗效果可以参数化为每个响应变量轨迹的时间尺度的加速或减速。使用广义估计方程来估计模型参数。来自阿尔茨海默病患者的认知和功能能力数据以及来自艾滋病临床试验的生活质量数据被用来阐明该模型。
