Evaluating Diagnostic Tests With Near-Perfect Specificity: Use of a Hui-Walter Approach When Designing a Trial of a DIVA Test for Bovine Tuberculosis

Frontiers in Veterinary Science
Gustaf RydevikIain J McKendrick

Abstract

Active surveillance of rare infectious diseases requires diagnostic tests to have high specificity, otherwise the false positive results can outnumber the true cases detected, leading to low positive predictive values. Where a positive result can have economic consequences, such as the cull of a bovine Tuberculosis (bTB) positive herd, establishing a high specificity becomes particularly important. When evaluating new diagnostic tests against a "gold standard" reference test with assumed perfect sensitivity and specificity, calculation of sample sizes are commonly done using a normal approximation to the binomial distribution, although this approach can be misleading. As the expected specificity of the evaluated diagnostic test nears 100%, the errors arising from this approximation are appreciable. Alternatively, it is straightforward to calculate the sample size by using more appropriate confidence intervals, while precisely quantifying the effect of sampling variability using the binomial distribution. However, regardless of the approach, if specificity is high the sample size required becomes large, and the gold standard may be prohibitively costly. An alternative to a gold standard test is to use at least two imperfect, con...Continue Reading

References

Dec 1, 2009·Trends in Microbiology·Paul R Torgerson, David J Torgerson
Sep 22, 2010·Journal of General Internal Medicine·Esteban Walker, Amy S Nowacki
Aug 3, 2011·Expert Review of Vaccines·Martin VordermeierAdam O Whelan
Feb 20, 2015·PLoS Computational Biology·Andrew J K ConlanJames L N Wood

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