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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, conditionally independent tests, and to analyse the results using a variant of the approach initially proposed by Hui and Walter. We show how this method performs for tests with near-perfect specificity; in particular we show that the sample size required to deliver useful bounds on the precision becomes very large for both approaches. We illustrate these concepts using simulation studies carried out to support the design of a trial of a bTB vaccine and a diagnostic that is able to “Differentiate Infected and Vaccinated Animals” (DIVA). Both test characteristics and the efficacy of the bTB vaccine will influence the sample size required for the study. We propose an improved methodology using a two stage approach to evaluating diagnostic tests in low disease prevalence populations. By combining an initial gold standard pilot study with a larger study analyzed using a Hui–Walter approach, the sample size required for each study can be reduced and the precision of the specificity estimate improved, since information from both studies is combined.

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