Aggregating multiple test results to improve medical decision-making.

IF 3.8 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2025-01-07 eCollection Date: 2025-01-01 DOI:10.1371/journal.pcbi.1012749

Lucas Böttcher, Maria R D'Orsogna, Tom Chou

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Abstract

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.

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汇总多项检测结果，提高医疗决策。

为医疗决策收集观察数据往往涉及由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来研究如何通过汇总重复诊断和筛选测试的结果来改进医疗决策。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，因为在SARS-CoV-2大流行期间需要快速、具有成本效益的检测方法。我们的模型能够开发具有任意数量测试的测试协议，这些测试可以定制以满足类型I和类型II错误的要求。这允许我们根据特定应用程序的需要调整灵敏度和特异性。此外，我们推导出疾病流行的广义罗根-格拉登估计值，该估计值可以解释任意数量的具有潜在不同I型和II型错误的测试。我们还提供了相应的不确定度量化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY

CiteScore

7.10

自引率

4.70%

发文量

820

审稿时长

2.5 months

期刊介绍： PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.