[SaFEGen]: A Statistical Framework for efficient Evidence Generation in diagnostics

[SaFEGen]：诊断中有效生成证据的统计框架

• 批准号: EP/X041298/1
• 负责人: Kevin Wilson
• 金额: $ 84.5万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX041298%2F1
• 关键词: SaFEGen; Statistical; Framework; efficient; Evidence

Diagnostic tests are a critical component of any modern healthcare system: it is estimated that approximately 70% of clinical decisions are influenced by the use of in vitro diagnostics (IVDs). They are used both to enable diagnosis and to rule out causes of ill health. They are also used to monitor, screen and assess people for potential health problems. Increasingly, they allow people with chronic disease to manage their own conditions. However, less than 1% of the NHS budget is dedicated to the uptake of new and innovative IVD products, and it typically takes more than 10 years and considerable resources to achieve widespread adoption of a novel diagnostic test. Despite these challenges, diagnostics play a significant role in the UK economy, employing over 8,000 people, and in the UK the IVD market accounts for over £1.1 billion pounds annually. An independent report on diagnostic tests was commissioned by the Royal Statistical Society (RSS) in part since "the RSS has been particularly concerned that many new diagnostic tests for SARS-CoV-2 antigen or antibodies were coming to market for use both in clinical practice and for surveillance without adequate provision for statistical evaluation of their analytical and clinical performance. Against a wider background of concern about standards applied to the evaluation of in vitro diagnostic tests, there was a need for clear statistical thinking on the principles of diagnostic testing in general, and their application in a pandemic in particular". One of the key recommendations of the report was that "undertaking well designed, adequately powered and correctly analysed studies of the clinical performance of an in vitro diagnostic is important for each intended use of the test." In this project we aim to develop a statistical framework that ensures that not only are diagnostic studies well designed, adequately powered and correctly analysed, but also make the most efficient use of data between development stages and takes advantage of cutting-edge advances in conventional clinical trial design.The SaFEGen project will achieve a step change in the time to market of novel diagnostic tests by developing cutting-edge integrated statistical methods for the design and analysis of diagnostic studies from inception to adoption into the NHS. SaFEGen will make best use of data to save time and money in the development of novel diagnostic tests by developing adaptive (modifying a study design during a study based on data observed so far) and seamless (combining separate studies to reduce time and increase efficiency) designs. The effect of the uptake of the framework will be a faster and more efficient evidence generation process for development of diagnostics, with no loss of rigour, and, ultimately, better outcomes for patients when high-quality diagnostics reach the NHS more quickly.

诊断测试是任何现代医疗保健系统的关键组成部分：据估计，大约70%的临床决策受到体外诊断（IVD）的影响。它们既可用于诊断，也可用于排除健康不良的原因。它们还用于监测、筛查和评估人们的潜在健康问题。它们越来越多地允许慢性病患者管理自己的病情。然而，只有不到1%的NHS预算专门用于采用新的和创新的IVD产品，并且通常需要10年以上的时间和大量的资源才能实现广泛采用新型诊断测试。尽管存在这些挑战，诊断在英国经济中发挥着重要作用，雇用了8，000多人，在英国，IVD市场每年超过11亿英镑。皇家统计学会（RSS）委托编写了一份关于诊断测试的独立报告，部分原因是“皇家统计学会特别关注的是，许多新的SARS-CoV-2抗原或抗体诊断测试正在进入市场，用于临床实践和监测，但没有对其分析和临床性能进行充分的统计评估。在人们对体外诊断测试评价标准表示关切的更广泛背景下，需要对诊断测试的一般原则，特别是在大流行病中的应用进行明确的统计思考”。该报告的主要建议之一是，“对体外诊断的临床性能进行设计良好、把握度充分和分析正确的研究，对于检测的每种预期用途都很重要。“在这个项目中，我们的目标是开发一个统计框架，不仅确保诊断研究设计良好，动力充足，分析正确，同时也能最有效地利用开发阶段之间的数据，并利用传统临床试验设计的前沿技术。SaFEGen项目将通过开发新的诊断测试，边缘综合统计方法的设计和分析的诊断研究从开始到采用到NHS。SaFEGen将充分利用数据，通过开发自适应（基于迄今为止观察到的数据在研究期间修改研究设计）和无缝（结合单独的研究以减少时间和提高效率）设计，节省开发新型诊断测试的时间和金钱。采用该框架的效果将是为诊断开发提供更快、更有效的证据生成过程，而不会损失严格性，并最终在高质量的诊断更快地到达NHS时为患者带来更好的结果。