Department of Clinical Biochemistry, North West London Pathology, Imperial College Healthcare NHS Trust, Charing Cross Hospital, London, UK.
Clin Chem Lab Med. 2023 Apr 13;61(10):1730-1739. doi: 10.1515/cclm-2023-0158. Print 2023 Sep 26.
According to international standards, clinical laboratories are required to verify the performance of assays prior to their implementation in routine practice. This typically involves the assessment of the assay's imprecision and trueness vs. appropriate targets. The analysis of these data is typically performed using frequentist statistical methods and often requires the use of closed source, proprietary software. The motivation for this paper was therefore to develop an open-source, freely available software capable of performing Bayesian analysis of verification data.
The veRification application presented here was developed with the freely available R statistical computing environment, using the Shiny application framework. The codebase is fully open-source and is available as an R package on GitHub.
The developed application allows the user to analyze imprecision, trueness against external quality assurance, trueness against reference material, method comparison, and diagnostic performance data within a fully Bayesian framework (with frequentist methods also being available for some analyses).
Bayesian methods can have a steep learning curve and thus the work presented here aims to make Bayesian analyses of clinical laboratory data more accessible. Moreover, the development of the application and seeks to encourage the dissemination of open-source software within the community and provides a framework through which Shiny applications can be developed, shared, and iterated upon.
根据国际标准,临床实验室在将检测方法常规应用于临床实践之前,需要对其性能进行验证。这通常涉及评估检测方法的不精密度和与适当目标的准确度。这些数据的分析通常使用频率统计学方法进行,并且通常需要使用闭源、专有的软件。因此,本文的目的是开发一个能够对验证数据进行贝叶斯分析的开源、免费软件。
这里介绍的 veRification 应用程序是使用免费的 R 统计计算环境和 Shiny 应用程序框架开发的。代码库完全开源,并可在 GitHub 上作为 R 包获得。
开发的应用程序允许用户在完全贝叶斯框架内分析不精密度、外部质量保证的准确度、参考物质的准确度、方法比较和诊断性能数据(一些分析也提供频率统计学方法)。
贝叶斯方法可能具有陡峭的学习曲线,因此这里介绍的工作旨在使临床实验室数据的贝叶斯分析更易于访问。此外,该应用程序的开发旨在鼓励在社区内传播开源软件,并提供一个框架,通过该框架可以开发、共享和迭代 Shiny 应用程序。