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 language | English |
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Pages (from-to) | 735-753 |
Number of pages | 19 |
Journal | Communications in Statistics Part B: Simulation and Computation |
Volume | 29 |
Issue number | 4 |
DOIs | |
State | Published - 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