论文标题
用于诊断测试的最佳决策理论：最大程度地减少不确定的类，并应用于基于唾液的SARS-COV-2抗体测定
Optimal Decision Theory for Diagnostic Testing: Minimizing Indeterminate Classes with Applications to Saliva-Based SARS-CoV-2 Antibody Assays
论文作者
论文摘要
在诊断测试中，建立不确定类是识别无法准确分类的样本的有效方法。但是，这种方法还可以使测试效率降低，并且必须与整体测定性能保持平衡。我们通过根据约束优化问题对数据进行重新分类来解决这个问题，（i）将样品标记为不确定的可能性，而（ii）确保其余数据的分类为平均目标准确性x。我们表明，对此问题的解决方案表明，该样品以bathub原理的范围表达了与最低的x-Dective the x-dectivection th samplease coppersiple。为了说明此分析的有用性，我们将其应用于基于唾液的SARS-COV-2抗体测定法，并证明相对于更传统的方法，不确定样本的数量降低了30％。
In diagnostic testing, establishing an indeterminate class is an effective way to identify samples that cannot be accurately classified. However, such approaches also make testing less efficient and must be balanced against overall assay performance. We address this problem by reformulating data classification in terms of a constrained optimization problem that (i) minimizes the probability of labeling samples as indeterminate while (ii) ensuring that the remaining ones are classified with an average target accuracy X. We show that the solution to this problem is expressed in terms of a bathtub principle that holds out those samples with the lowest local accuracy up to an X-dependent threshold. To illustrate the usefulness of this analysis, we apply it to a multiplex, saliva-based SARS-CoV-2 antibody assay and demonstrate up to a 30 % reduction in the number of indeterminate samples relative to more traditional approaches.
