Bristol Vaccine Centre, Schools of Population Health Sciences and of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom.
Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom.
PLoS Comput Biol. 2024 Apr 26;20(4):e1012062. doi: 10.1371/journal.pcbi.1012062. eCollection 2024 Apr.
Multiplex panel tests identify many individual pathogens at once, using a set of component tests. In some panels the number of components can be large. If the panel is detecting causative pathogens for a single syndrome or disease then we might estimate the burden of that disease by combining the results of the panel, for example determining the prevalence of pneumococcal pneumonia as caused by many individual pneumococcal serotypes. When we are dealing with multiplex test panels with many components, test error in the individual components of a panel, even when present at very low levels, can cause significant overall error. Uncertainty in the sensitivity and specificity of the individual tests, and statistical fluctuations in the numbers of false positives and false negatives, will cause large uncertainty in the combined estimates of disease prevalence. In many cases this can be a source of significant bias. In this paper we develop a mathematical framework to characterise this issue, we determine expressions for the sensitivity and specificity of panel tests. In this we identify a counter-intuitive relationship between panel test sensitivity and disease prevalence that means panel tests become more sensitive as prevalence increases. We present novel statistical methods that adjust for bias and quantify uncertainty in prevalence estimates from panel tests, and use simulations to test these methods. As multiplex testing becomes more commonly used for screening in routine clinical practice, accumulation of test error due to the combination of large numbers of test results needs to be identified and corrected for.
多重检测面板一次性识别多种病原体，使用一组组件检测。在某些面板中，组件的数量可能很大。如果面板正在检测单一综合征或疾病的病原体，那么我们可以通过组合面板的结果来估计该疾病的负担，例如通过确定由许多个别肺炎球菌血清型引起的肺炎球菌性肺炎的流行率。当我们处理具有许多组件的多重检测面板时，即使在非常低的水平下，面板中各个组件的检测错误也会导致整体错误显著增加。个别检测的敏感性和特异性的不确定性以及假阳性和假阴性数量的统计波动，将导致疾病流行率的综合估计值存在很大的不确定性。在许多情况下，这可能是一个重要的偏差来源。在本文中，我们开发了一个数学框架来描述这个问题，确定了面板检测的敏感性和特异性的表达式。在这方面，我们发现了一个与面板检测敏感性和疾病流行率之间的反直觉关系，即随着流行率的增加，面板检测变得更加敏感。我们提出了新颖的统计方法，可以校正面板检测中由于大量检测结果组合引起的偏差，并量化流行率估计的不确定性，并通过模拟来测试这些方法。随着多重检测在常规临床实践中的筛查中越来越普遍，需要识别和纠正由于大量检测结果组合而导致的检测错误的积累。
