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Subject-Specific and Population-Averaged Questions for Log-Linear Regression Data

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Statistical Modelling

Part of the book series: Lecture Notes in Statistics ((LNS,volume 104))

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Abstract

This paper illustrates the relation between subject-specific and population-averaged models (Zeger et al., 1988), especially for loglinear regression data with normal random effects. The emphasis is on simple special cases. The practical implications are discussed using an example from the literature.

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References

  • Breslow, N.E. and Clayton, D. (1993). Approximate inference in generalised linear mixed models. Journal of the American Statistical Association 88 9–25.

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  • Diggle, P., Liang, K.-Y., and Zeger, S.L. (1994). Longitudinal data analysis. Oxford University Press, Oxford.

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  • Grömping, U. (1994). A note on fitting a marginal model to mixed effects log-linear regression data via GEE. To appear in Biometrics.

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  • Liang, K.-Y. and Zeger, S.L. (1986). Longitudinal data analysis using generalised linear models. Biometrika 73, 13–22.

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  • Thall, P.F. and Vail, S.C. (1990). Some covariance models for longitudinal count data with overdispersion. Biometrics 46, 657–671.

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  • Zeger, S.L., Liang, K.-Y. and Albert, P.S. (1988). Models for longitudinal data: a generalised estimating equation approach. Biometrics 44, 1049–1060.

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Authors
  1. Ulrike Grömping
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Editors and Affiliations

  1. Institut für Statistik, Universität Innsbruck, Christoph-Probst-Platz, Innsbruck, A-6020, Austria

    Gilg U. H. Seeber  & Gabriele Steckel-Berger  & 

  2. Centre for Applied Statistics, Lancaster University Fylde College, Lancaster, LA1 4YF, England

    Brian J. Francis

  3. Institut für Statistik, Wirtschaftsuniversität, Augasse 2-6, Wien, A-1090, Austria

    Reinhold Hatzinger

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© 1995 Springer Science+Business Media New York

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Grömping, U. (1995). Subject-Specific and Population-Averaged Questions for Log-Linear Regression Data. In: Seeber, G.U.H., Francis, B.J., Hatzinger, R., Steckel-Berger, G. (eds) Statistical Modelling. Lecture Notes in Statistics, vol 104. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0789-4_16

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  • DOI: https://doi.org/10.1007/978-1-4612-0789-4_16

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-94565-1

  • Online ISBN: 978-1-4612-0789-4

  • eBook Packages: Springer Book Archive

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