Small Sample Inference for Clustered Data

  • Ziding Feng
  • Thomas Braun
  • Charles McCulloch
Part of the Lecture Notes in Statistics book series (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.

Key words

Correlated data group randomized trials linear mixed models Generalized Estimating Equations (GEE) permutation tests small sample inference 

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Copyright information

© Springer Science+Business Media New York 2004

Authors and Affiliations

  • Ziding Feng
    • 1
    • 2
    • 3
  • Thomas Braun
    • 1
    • 2
    • 3
  • Charles McCulloch
    • 1
    • 2
    • 3
  1. 1.Cancer Prevention Research ProgramFred Hutchinson Cancer Research CenterSeattleUSA
  2. 2.Department of BiostatisticsUniversity of MichiganAnn ArborUSA
  3. 3.Department of Epidemiology and BiostatisticsSan FranciscoUSA

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