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Related Experiment Videos

Explaining community-level variance in group randomized trials.

Z Feng1, P Diehr, Y Yasui

  • 1Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.

Statistics in Medicine
|April 21, 1999
PubMed
Summary
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Controlling for covariates can significantly reduce between-community variance in group randomized trials, lowering costs. Community-by-time variance is minimal, suggesting blocking may not be needed for trials focused on change from baseline.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Public Health Research

Background:

  • Group randomized trials (GRTs) are essential for public health interventions but are often costly.
  • Between-community variance is a major contributor to the high cost of GRTs.
  • Minimizing variance is crucial for improving statistical power and reducing trial expenses.

Purpose of the Study:

  • To investigate methods for reducing between-community variance in GRTs.
  • To identify individual- and community-level covariates that can effectively reduce variance.
  • To assess the impact of covariate adjustment on community-by-time variance.

Main Methods:

  • Empirical investigation using data from four large community-based studies: Working Well Trial, Kaiser Adolescent Survey, Kaiser Adults Survey, and Eating Patterns Study.

Related Experiment Videos

  • Analysis of between-community variance and community-by-time variance components.
  • Assessment of covariate adjustment strategies on variance reduction.

    • Main Results:

    • Adjusting for covariates substantially reduced the between-community variance component in the analyzed datasets.
    • Community-by-time variance components were consistently near zero, particularly in cohort survey data.
    • Covariate adjustment had a lesser impact on reducing community-by-time variance in cohort samples compared to cross-sectional samples.

    Conclusions:

    • Investigators can reduce the cost and improve the power of group randomized trials by controlling for or blocking on identified covariates.
    • Blocking may be unnecessary for GRTs where the primary interest is the change from baseline, given the minimal community-by-time variance.
    • These findings offer practical strategies for optimizing the design and efficiency of community-based research.