Proceedings of the Second Seattle Symposium in Biostatistics
Volume 179 of the series Lecture Notes in Statistics pp 7187
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
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), semiparametric methods based on Generalized Estimating Equations (GEE), and nonparametric methods based on permutation tests.
Key words
Correlated data group randomized trials linear mixed models Generalized Estimating Equations (GEE) permutation tests small sample inference Title
 Small Sample Inference for Clustered Data
 Book Title
 Proceedings of the Second Seattle Symposium in Biostatistics
 Book Subtitle
 Analysis of Correlated Data
 Pages
 pp 7187
 Copyright
 2004
 DOI
 10.1007/9781441990761_5
 Print ISBN
 9780387208626
 Online ISBN
 9781441990761
 Series Title
 Lecture Notes in Statistics
 Series Volume
 179
 Series ISSN
 09300325
 Publisher
 Springer New York
 Copyright Holder
 Springer Science+Business Media New York
 Additional Links
 Topics
 Keywords

 Correlated data
 group randomized trials
 linear mixed models
 Generalized Estimating Equations (GEE)
 permutation tests
 small sample inference
 Industry Sectors
 eBook Packages
 Editors

 D. Y. Lin ^{(1)}
 P. J. Heagerty ^{(2)}
 Editor Affiliations

 1. School of Public Health, Department of Biostatistics, CB #7420, University of North Carolina
 2. School of Public Health and Community Medicine, Department of Biostatistics, University of Washington
 Authors

 Ziding Feng ^{(3)} ^{(4)} ^{(5)}
 Thomas Braun ^{(3)} ^{(4)} ^{(5)}
 Charles McCulloch ^{(3)} ^{(4)} ^{(5)}
 Author Affiliations

 3. Cancer Prevention Research Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave.N. MP702, Seattle, WA, 981091024, USA
 4. Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48108, USA
 5. Department of Epidemiology and Biostatistics, 500 Parnassus, 420 MUW, San Francisco, CA, 941430560, USA
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