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Proceedings of the Second Seattle Symposium in Biostatistics

Volume 179 of the series Lecture Notes in Statistics pp 71-87

Small Sample Inference for Clustered Data

  • Ziding FengAffiliated withCancer Prevention Research Program, Fred Hutchinson Cancer Research CenterDepartment of Biostatistics, University of MichiganDepartment of Epidemiology and Biostatistics
  • , Thomas BraunAffiliated withCancer Prevention Research Program, Fred Hutchinson Cancer Research CenterDepartment of Biostatistics, University of MichiganDepartment of Epidemiology and Biostatistics
  • , Charles McCullochAffiliated withCancer Prevention Research Program, Fred Hutchinson Cancer Research CenterDepartment of Biostatistics, University of MichiganDepartment of Epidemiology and Biostatistics

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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