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Effect of diagnostic testing error on intracluster correlation coefficient estimation.

A J Branscum1, I A Gardner, B A Wagner

  • 1Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, CA 95616, USA.

Preventive Veterinary Medicine
|May 19, 2005
PubMed
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Estimating the intracluster correlation coefficient (ICC) for animal disease surveys is crucial. Imperfect diagnostic tests lead to underestimation of the true ICC, but a Bayesian model can account for test accuracy.

Area of Science:

  • Veterinary epidemiology
  • Statistical modeling
  • Survey methodology

Background:

  • Intracluster correlation coefficient (ICC) estimation is vital for infectious animal disease surveys, impacting survey design and prevalence calculations.
  • Surveys commonly use diagnostic tests with imperfect sensitivity and specificity, complicating accurate ICC estimation.

Purpose of the Study:

  • To evaluate the impact of using apparent (test-based) prevalence data instead of true infection status on an ANOVA estimator of ICC.
  • To assess how diagnostic test sensitivity and specificity affect ICC estimation.
  • To propose and illustrate a Bayesian model for ICC estimation that accounts for imperfect diagnostic tests.

Main Methods:

  • Monte Carlo simulation to examine the ANOVA estimator's performance with imperfect diagnostic tests.

Related Experiment Videos

  • Analysis under the beta-binomial model to compare ICC based on apparent versus true infection status.
  • Development and application of a Bayesian model incorporating diagnostic test sensitivity and specificity.

    • Main Results:

    • The ANOVA estimator significantly underestimated the true ICC when diagnostic tests were imperfect.
    • ICC derived from apparent infection status was consistently less than or equal to the ICC from true infection status.
    • The proposed Bayesian model effectively estimated ICC while accounting for test imperfections.

    Conclusions:

    • Using apparent prevalence data with standard ICC estimators can lead to biased results in infectious disease surveys.
    • A Bayesian approach offers a robust method for estimating ICC in the presence of imperfect diagnostic tests.
    • Accurate ICC estimation is essential for reliable survey planning and variance calculations in animal health studies.