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On design considerations and randomization-based inference for community intervention trials

M H Gail1, S D Mark, R J Carroll

  • 1National Cancer Institute, Division of Cancer Etiology, Bethesda, MD 20892-7368, USA.

Statistics in Medicine
|June 15, 1996
PubMed
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Randomization tests in community trials are robust, especially with equal intervention and control groups. Adjustments for covariates can improve power in these public health intervention studies.

Area of Science:

  • Epidemiology and Biostatistics
  • Public Health Intervention Research

Background:

  • Randomized community intervention trials are crucial for evaluating public health strategies.
  • Longitudinal and cross-sectional data offer complementary insights into intervention effects at individual and group levels.
  • Understanding the statistical properties of randomization-based inference is key for robust trial design.

Purpose of the Study:

  • To examine design considerations for randomized community intervention trials.
  • To evaluate the performance of randomization tests under various null hypotheses.
  • To assess the impact of design choices on statistical power and test size.

Main Methods:

  • Simulation studies were conducted to assess randomization test size under strong and weak null hypotheses.

Related Experiment Videos

  • Analysis included matched and unmatched cohort designs, with varying numbers of intervention and control communities.
  • Covariate adjustment, missing data, and cross-sectional survey applications were explored.

    • Main Results:

    • Randomization tests generally maintain proper size under the strong null hypothesis.
    • Test size can exceed nominal levels under the weak null hypothesis in unmatched designs with unequal group sizes and varying variances.
    • Pair-matched designs typically maintain nominal size, confirming robustness, especially with equal group numbers.

    Conclusions:

    • Equal numbers of intervention and control communities are recommended for unmatched designs to ensure valid test sizes.
    • Randomization tests are robust for community intervention trials, particularly when using matched designs or equal group sizes.
    • Covariate adjustment can enhance power, but its effectiveness is influenced by intraclass correlation within communities.