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Linear Models for the Analysis of Longitudinal Studies
James H. Ware
The American Statistician
Vol. 39, No. 2 (May, 1985), pp. 95-101
Stable URL: http://www.jstor.org/stable/2682803
Page Count: 7
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Longitudinal investigations play an increasingly prominent role in biomedical research. Much of the literature on specifying and fitting linear models for serial measurements uses methods based on the standard multivariate linear model. This article proposes a more flexible approach that permits specification of the expected response as an arbitrary linear function of fixed and time-varying covariates so that mean-value functions can be derived from subject matter considerations rather than methodological constraints. Three families of models for the covariance function are discussed: multivariate, autoregressive, and random effects. Illustrations demonstrate the flexibility and utility of the proposed approach to longitudinal analysis.
The American Statistician © 1985 American Statistical Association