Böttcher Lucas, D'Orsogna Maria R, Chou Tom
Department of Computational Science and Philosophy, Frankfurt School of Finance and Management, Frankfurt am Main, Germany.
Department of Mathematics, California State University at Northridge, Los Angeles, California, United States of America.
PLoS Comput Biol. 2025 Jan 7;21(1):e1012749. doi: 10.1371/journal.pcbi.1012749. eCollection 2025 Jan.
Gathering observational data for medical decision-making often involves uncertainties arising from both type I (false positive) and type II (false negative) errors. In this work, we develop a statistical model to study how medical decision-making can be improved by aggregating results from repeated diagnostic and screening tests. Our approach is relevant to not only clinical settings such as medical imaging, but also to public health, as highlighted by the need for rapid, cost-effective testing methods during the SARS-CoV-2 pandemic. Our model enables the development of testing protocols with an arbitrary number of tests, which can be customized to meet requirements for type I and type II errors. This allows us to adjust sensitivity and specificity according to application-specific needs. Additionally, we derive generalized Rogan-Gladen estimates of disease prevalence that account for an arbitrary number of tests with potentially different type I and type II errors. We also provide the corresponding uncertainty quantification.
收集用于医疗决策的观察数据通常涉及由I型（假阳性）和II型（假阴性）错误引起的不确定性。在这项工作中，我们开发了一个统计模型，以研究如何通过汇总重复诊断和筛查测试的结果来改善医疗决策。我们的方法不仅与医学成像等临床环境相关，而且与公共卫生相关，正如在SARS-CoV-2大流行期间对快速、经济高效的检测方法的需求所凸显的那样。我们的模型能够开发具有任意数量测试的检测方案，这些方案可以定制以满足对I型和II型错误的要求。这使我们能够根据特定应用需求调整灵敏度和特异性。此外，我们推导了疾病患病率的广义Rogan-Gladen估计值，该估计值考虑了具有潜在不同I型和II型错误的任意数量的测试。我们还提供了相应的不确定性量化。
