Random-Effects Models for Longitudinal Data

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ID: 289251
1982
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Abstract
Models for the analysis of longitudinal data must recognize the relationship between serial observations on the same unit. Multivariate models with general covariance structure are often difficult to apply to highly unbalanced data, whereas two-stage random-effects models can be used easily. In two-stage models, the probability distributions for the response vectors of different individuals belong to a single family, but some random-effects parameters vary across individuals, with a distribution specified at the second stage. A general family of models is discussed, which includes both growth models and repeated-measures models as special cases. A unified approach to fitting these models, based on a combination of empirical Bayes and maximum likelihood estimation of model parameters and using the EM algorithm, is discussed. Two examples are taken from a current epidemiological study of the health effects of air pollution.
Reference Key
openalex_W2082246284 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nan M. Laird, James H. Ware
Journal biometrics
Year 1982
DOI
10.2307/2529876
URL
https://doi.org/10.2307/2529876
Keywords Keywords not found

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