Estimating a treatment effect from multidimensional longitudinal data

Sarah M. Gray, Ron Brookmeyer

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)976-988
Number of pages13
Issue number3
StatePublished - Sep 1998


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

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics


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