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Small Sample Inference for Clustered Data

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Proceedings of the Second Seattle Symposium in Biostatistics

Part of the book series: Lecture Notes in Statistics ((LNS,volume 179))

Abstract

When the number of independent units is not adequate to invoke large sample approximations in clustered data analysis, a situation that often arises in group randomized trials (GRTs), valid and efficient small sample inference becomes important. We review the current methods for analyzing data from small numbers of clusters, namely methods based on full distribution assumptions (mixed effect models), semi-parametric methods based on Generalized Estimating Equations (GEE), and non-parametric methods based on permutation tests.

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Author information

Authors and Affiliations

  1. Cancer Prevention Research Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave.N. MP-702, Seattle, WA, 98109-1024, USA

    Ziding Feng, Thomas Braun & Charles McCulloch

  2. Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48108, USA

    Ziding Feng, Thomas Braun & Charles McCulloch

  3. Department of Epidemiology and Biostatistics, 500 Parnassus, 420 MU-W, San Francisco, CA, 94143-0560, USA

    Ziding Feng, Thomas Braun & Charles McCulloch

Authors
  1. Ziding Feng
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  2. Thomas Braun
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  3. Charles McCulloch
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Editor information

Editors and Affiliations

  1. School of Public Health, Department of Biostatistics, CB #7420, University of North Carolina, Chapel Hill, NC, 27599-7420, USA

    D. Y. Lin

  2. School of Public Health and Community Medicine, Department of Biostatistics, University of Washington, Box 357232, Seattle, WA, 98195, USA

    P. J. Heagerty

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Feng, Z., Braun, T., McCulloch, C. (2004). Small Sample Inference for Clustered Data. In: Lin, D.Y., Heagerty, P.J. (eds) Proceedings of the Second Seattle Symposium in Biostatistics. Lecture Notes in Statistics, vol 179. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9076-1_5

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  • DOI: https://doi.org/10.1007/978-1-4419-9076-1_5

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-20862-6

  • Online ISBN: 978-1-4419-9076-1

  • eBook Packages: Springer Book Archive

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