An integrated population-averaged approach to the design, analysis and sample size determination of cluster-unit trials

Authors

  • John S. Preisser,

    Corresponding author
    1. Department of Biostatistics, University of North Carolina School of Public Health, Chapel Hill, North Carolina, 27599-7420 U.S.A.
    • Department of Biostatistics, CB # 7420, School of Public Health, University of North Carolina, Chapel Hill, NC 27599, U.S.A.
    Search for more papers by this author
  • Mary L. Young,

    1. Department of Biostatistics, University of North Carolina School of Public Health, Chapel Hill, North Carolina, 27599-7420 U.S.A.
    Search for more papers by this author
  • Daniel J. Zaccaro,

    1. Section on Biostatistics, Department of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina, U.S.A.
    Search for more papers by this author
  • Mark Wolfson

    1. Section on Social Sciences and Health Policy, Department of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina, U.S.A.
    Search for more papers by this author

Abstract

While the mixed model approach to cluster randomization trials is relatively well developed, there has been less attention given to the design and analysis of population-averaged models for randomized and non-randomized cluster trials. We provide novel implementations of familiar methods to meet these needs. A design strategy that selects matching control communities based upon propensity scores, a statistical analysis plan for dichotomous outcomes based upon generalized estimating equations (GEE) with a design-based working correlation matrix, and new sample size formulae are applied to a large non-randomized study to reduce underage drinking. The statistical power calculations, based upon Wald tests for summary statistics, are special cases of a general power method for GEE. Copyright © 2003 John Wiley & Sons, Ltd.

Ancillary