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Abstract:
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In vaccine efficacy trials, diagnostic assays are typically used to define disease cases. Inaccurate counting of disease cases leads to systematic under-estimation or “dilution” of vaccine efficacy, which can result in unwarranted no-go decisions in vaccine development. This problem is exacerbated in trials of vaccines for self-limiting infections, in which frequent sampling may be needed to ensure that a case is detected before pathogen biomarkers decay. We propose several replicate testing strategies to combat this problem, considering the additional challenge of uncertainty in both disease incidence and diagnostic assay specificity/sensitivity. A strategy that counts an infection case only if a majority of replicate assays return a positive result can dramatically reduce efficacy dilution. We also find that a cost-effective variant of this strategy, using confirmatory assays only if an initial assay is positive, yields a comparable benefit. The properties of these replicate testing strategies are compared over a range of assay and trial parameters, and their ability to reduce efficacy dilution is demonstrated.
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