Gray, S. M. and Brookmeyer, R. (2000) Multidimensional longitudinal data: estimating a treatment effect from continuous, discrete, or time to event response variables. Journal of the American Statistical Association, 95 (450). pp. 396-406.Full text not available from this repository.
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.
|Journal or Publication Title:||Journal of the American Statistical Association|
|Uncontrolled Keywords:||Acceleration ; Alzheimer's disease ; Generalized estimating equations ; Longitudinal data analysis ; Multidimensional data ; Quality of life.|
|Subjects:||Q Science > QA Mathematics|
|Departments:||Faculty of Science and Technology > Mathematics and Statistics|
|Deposited On:||20 Nov 2008 15:34|
|Last Modified:||07 Jan 2015 13:10|
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