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Tests for trend in developmental toxicity experiments with correlated binary data

K Y Fung1, D Krewski, J N Rao

  • 1Department of Mathematics & Statistics, University of Windsor, Ontario, Canada.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|August 1, 1994
PubMed
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This study evaluates statistical tests for detecting trends in correlated binary data from developmental toxicity studies. Recommended tests offer improved accuracy in identifying potential developmental toxicants.

Area of Science:

  • Toxicology
  • Biostatistics
  • Statistical modeling

Background:

  • Correlated binary data are common in developmental toxicity studies.
  • Accurate trend detection is crucial for risk assessment.
  • Existing methods may not adequately handle data correlation and extra-binomial variation.

Purpose of the Study:

  • To examine the operating characteristics of statistical tests for trend in correlated binary data.
  • To identify tests with favorable type I and type II error rates for developmental toxicity studies.
  • To provide recommendations for practical application in risk assessment.

Main Methods:

  • Evaluation of adjusted Cochran-Armitage tests using Rao-Scott transformation.
  • Discussion of generalized score tests with generalized estimating equations.

Related Experiment Videos

  • Assessment using both simulated (computer-generated) and experimental data.

    • Main Results:

    • Specific forms of adjusted Cochran-Armitage and generalized score tests demonstrated favorable error rates.
    • These recommended tests effectively manage correlated binary data and extra-binomial variation.
    • Performance was validated across simulated and real-world developmental toxicity data.

    Conclusions:

    • The identified statistical tests are recommended for analyzing correlated binary data in developmental toxicity.
    • These methods enhance the reliability of trend detection in toxicological risk assessment.
    • Practical application is illustrated with literature data, aiding regulatory decision-making.