The design and interpretation of prevalence studies rely on point estimates of the performance characteristics of the diagnostic test used. When the test characteristics are not well defined and a limited number of tests are available, such as during an outbreak of a novel pathogen, tests can be used either for the field study itself or for additional validation to reduce uncertainty in the test characteristics. Because field data and validation data are based on finite samples, inferences drawn from these data carry uncertainty. In the absence of a framework to balance those uncertainties during study design, it is unclear how best to distribute tests to improve study estimates. Here, we address this gap by introducing a joint Bayesian model to simultaneously analyze lab validation and field survey data. In many scenarios, prevalence estimates can be most improved by apportioning additional effort towards validation rather than to the field. We show that a joint model provides superior estimation of prevalence, as well as sensitivity and specificity, compared with typical analyses that model lab and field data separately.

中文翻译：

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联合建模流行率，敏感性和特异性以优化样品分配

患病率研究的设计和解释依赖于所用诊断测试的性能特征的点估计。当测试特征的定义不明确且可用的测试数量有限时（例如在新型病原体爆发期间），可以将测试用于现场研究本身或用于其他验证，以减少测试特征的不确定性。由于现场数据和验证数据基于有限样本，因此从这些数据得出的推论带有不确定性。在缺乏平衡研究设计过程中不确定性的框架的情况下，尚不清楚如何最好地分配测试以改善研究估计。在这里，我们通过引入联合贝叶斯模型同时分析实验室验证和现场调查数据来解决这一差距。在许多情况下 可以通过将更多的精力分配给验证而不是现场来最大程度地提高患病率估计。我们表明，与分别对实验室和现场数据进行建模的典型分析相比，联合模型可以提供更高的患病率估计以及灵敏度和特异性。