Multidimensional longitudinal data: Estimating a treatment effect from continuous, discrete, or time-to-event response variables

Multidimensional data arise when a number of different response variables a re required to measure the outcome of interest. Examples of such outcomes i nclude quality of life, cognitive ability, and health status. The goal of t his: article is to develop a methodology to estimate a treatment effect fro m multidimensional data that have been collected longitudinally using conti nuous, 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 u nits of the response variables is used to capture the effect of treatment. This allows information about the treatment effect to be combined across re sponse variables of different types. The model is specified using a pair of regression models for the first two moments, and generalized estimating eq uations are used for parameter estimation. The methodology is applied to qu ality-of-life data from an AIDS clinical trial and health status data from an Alzheimer's disease study.