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An empirical study of cluster randomization

A Donner

    International Journal of Epidemiology
    |September 1, 1982
    PubMed
    Summary
    This summary is machine-generated.

    Randomizing groups, not individuals, in lifestyle trials presents challenges. This study examines the

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    Area of Science:

    • Epidemiological research methodology
    • Biostatistics
    • Public health intervention design

    Background:

    • Lifestyle intervention trials increasingly randomize clusters (groups) instead of individuals.
    • Methodological challenges arise from cluster randomization, impacting statistical power and analysis.
    • Understanding these issues is crucial for accurate interpretation of trial results.

    Purpose of the Study:

    • To explore methodological issues in cluster randomization for lifestyle intervention trials.
    • To empirically assess the 'effective sample size' reduction caused by cluster randomization.
    • To discuss the measurement of within-cluster dependence for dichotomous outcomes.

    Main Methods:

    • Empirical study of 'effective sample size' in three common experimental units: spouse pairs, general practices, and counties.

    Related Experiment Videos

  • Discussion of measuring within-cluster dependence for dichotomous outcomes.
  • Demonstration of a relationship between Fleiss's kappa and Cornfield's inflation factor.

    • Main Results:

    • Cluster randomization significantly reduces the effective sample size compared to individual randomization.
    • The degree of sample size reduction varies depending on the cluster type (e.g., spouse pair, practice, county).
    • A quantifiable relationship exists between measures of within-cluster agreement and the inflation factor.

    Conclusions:

    • Methodological considerations for cluster randomization are critical in lifestyle intervention trials.
    • Accurate estimation of effective sample size is necessary to avoid underpowered studies.
    • The findings provide tools for better analysis of clustered data in epidemiological research.