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Intra-cluster correlation selection for cluster randomized trials.

Philip M Westgate1

  • 1Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, 40536, KY, U.S.A.

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Summary
This summary is machine-generated.

This study introduces a method for selecting the best intra-cluster correlation coefficient (ICC) structure in cluster randomized trials. This approach enhances statistical power for testing intervention effects by minimizing standard errors.

Keywords:
empirical covariance matrixgeneralized estimating equationsgroup randomized trialpowervariance inflation

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Cluster randomized trials (CRTs) involve randomizing groups (clusters) of subjects.
  • Intra-cluster correlation (ICC) describes outcome similarity within clusters, impacting statistical power.
  • Accurate ICC structure selection is crucial for reliable intervention effect testing in CRTs.

Purpose of the Study:

  • To propose a method for selecting an optimal working intra-cluster correlation coefficient (ICC) structure in cluster randomized trials.
  • To enhance the statistical power of tests assessing intervention impact on marginal mean outcomes.
  • To provide practical guidance for applying these methods in real-world CRTs.

Main Methods:

  • Utilizing recently developed statistical methods for ICC structure selection.
  • Employing small-sample corrections for covariance matrix estimation of regression parameters.
  • Incorporating correlation selection criteria from generalized estimating equations literature.
  • Evaluating multiple working ICC structures to identify the most efficient one.

Main Results:

  • The proposed method aims to minimize standard errors of regression parameter estimates.
  • This minimization directly leads to increased statistical power for intervention effect testing.
  • Simulation studies and an application example demonstrate the approach's utility and power.

Conclusions:

  • The developed method offers a robust approach to selecting ICC structures in CRTs.
  • This selection process improves the precision of intervention effect estimates.
  • The findings have practical implications for designing and analyzing cluster randomized trials.