Multidimensional Longitudinal Data: Estimating a Treatment Effect from Continuous, Discrete, or Time-to-Event Response Variables
本文提出一种方法，从包含连续、离散或时间至事件响应的多维纵向数据中估计处理效应，通过时间尺度变换整合不同类型响应变量的信息，并应用于艾滋病临床试验和阿尔茨海默病研究。
Abstract Multidimensional data arise when a number of different response variables are required to measure the outcome of interest. Examples of such outcomes include quality of life, cognitive ability, and health status. The goal of this article is to develop a methodology to estimate a treatment effect from multidimensional data that have been collected longitudinally using continuous, discrete, or time-to-event responses or a mixture of these types of responses. A transformation of the time scale that does not depend on the units of the response variables is used to capture the effect of treatment. This allows information about the treatment effect to be combined across response variables of different types. The model is specified using a pair of regression models for the first two moments, and generalized estimating equations are used for parameter estimation. The methodology is applied to quality-of-life data from an AIDS clinical trial and health status data from an Alzheimer's disease study.