General multivariate linear models for longitudinal studies

Gwowen Shieh*

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Longitudinal studies occur frequently in many different disciplines. To fully utilize the potential value of the information contained in a longitudinal data, various multivariate linear models have been proposed. The methodology and analysis are somewhat unique in their own ways and their relationships are not well understood and presented. This article describes a general multivariate linear model for longitudinal data and attempts to provide a constructive formulation of the components in the mean response profile. The objective is to point out the extension and connections of some well-known models that have been obscured by different areas of application. More importantly, the model is expressed in a unified regression form from the subject matter considerations. Such an approach is simpler and more intuitive than other ways to modeling and parameter estimation. As a consequence the analyses of the general class of models for longitudinal data can be easily implemented with standard software.

Original languageEnglish
Pages (from-to)735-753
Number of pages19
JournalCommunications in Statistics Part B: Simulation and Computation
Volume29
Issue number4
DOIs
StatePublished - 1 Dec 2000

Keywords

  • Doubly multivariate linear models
  • GMANOVA
  • Growth curve models
  • MANOVA
  • Pooled time series and cross-sectional data
  • Repeated measures design
  • Seemingly unrelated regression models
  • Time-varying covariates

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