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

Sarah M. Gray, Ron Brookmeyer

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)396-406
Number of pages11
JournalJournal of the American Statistical Association
Volume95
Issue number450
DOIs
StatePublished - Jun 1 2000

Keywords

  • Acceleration
  • Alzheimer's disease
  • Generalized estimating equations
  • Longitudinal data analysis
  • Multidimensional data
  • Quality of life

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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