Reliable and Efficient Estimation of the Economic Value of medical Research (REEEVR)

可靠、高效的医学研究经济价值估算 (REEEVR)

• 批准号: MR/W029855/1
• 负责人: Howard Thom
• 金额: $ 43.58万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FW029855%2F1
• 关键词: Reliable; Efficient; Estimation; Economic; Value

In the UK, taxpayers and charities pay for most healthcare research. National bodies like the National Institute for Health Research (NIHR) allocate public funds. Charities like Cancer Research UK (CRUK) rely on donations. To spend money wisely, they have to decide which areas of research matter most and which studies to fund.Value of Information (VoI) analysis estimates how much good a research study might do. Methods for estimating VoI are well established. But the people who decide what research gets funded seldom have access to the information it provides. This means we run the risk of wasting research funds. It also means that patients face unnecessary risks if they take part in research that is not justified from an NHS perspective. VoI is based on formal cost-effectiveness analysis, which NHS decision-makers already rely on. Mostly, this comes through the National Institute for Health and Care Excellence (NICE), one of our project partners. NICE uses cost-effectiveness analysis to decide which drugs, tests and procedures the NHS should provide. To get their drugs approved, manufacturers submit cost-effectiveness analyses to NICE. These are often conducted by specialist consultancies like our partner Source Health Economics. NICE also makes suggestions about what future research might be useful. But it does not conduct or require VoI, so it cannot provide formal guidance to research funders.The main reason VoI is not currently used more widely is that it is hard to do. Analysts need advanced technical skills. Even modern computers can struggle with all the calculations it needs. Online tools (including one developed by our collaborator) can approximate VoI, making it more accessible for analysts and easier for computers. Unfortunately, the approximations are only reliable for simple analyses. In a previous MRC grant, we showed how to estimate VoI accurately with much lower computational demands. We used a mathematical technique called multilevel Monte Carlo (MLMC). However, MLMC relies on complicated maths that few people developing cost-effectiveness analyses understand. We want to improve the use of VoI by creating an easy to use online tool that can quickly and reliably estimate it, even for complex analyses. We will first test existing approximation methods to understand the circumstances in which they are good enough. For more complex and realistic analyses, we will use MLMC. To do this, we will develop software to convert cost-effectiveness models from Microsoft Excel to a programming language. Microsoft Excel is the most commonly used software for cost-effectiveness analysis, but it is too slow to run MLMC itself. We will develop reusable and efficient MLMC code that users can apply to the converted models.Our partners are NICE Centre for Guidelines (NICE CfG) and Source Health Economics. They both develop large numbers of Excel-based cost-effectiveness analyses. They will provide guidance on the types of models on which to test the approximations. They will also provide example cost-effectiveness models on which we can test our software. People who work for NICE CfG and Source Health Economics will pilot our tool. We will use their feedback to make sure it is easy to use.We will also work with our research partners and NIHR to ensure people are aware of our online tool and use it in future practice. We will work with NICE CfG to include VoI in new national guidelines, underpinning recommendations for future research. Source Health Economics will recommend using our new tools to manufacturers so they can include VoI in NICE submissions. At each stage of our project, we will engage directly with NIHR, who have expressed interest in our work. We will also invite them to online workshops at the beginning and end of our project. Our ultimate ambition is to provide NIHR with tools and resources to prioritise healthcare research formally. This will reduce wasted research and benefit patients across the UK.

在英国，纳税人和慈善机构为大多数医疗保健研究买单。国家卫生研究所等国家机构分配公共资金。像英国癌症研究中心（CRUK）这样的慈善机构依赖捐款。为了明智地花钱，他们必须决定哪些研究领域最重要，哪些研究需要资助。信息价值（VoI）分析可以估计一项研究可能带来的好处。用于估计VoI的方法已经很好地建立。但是决定哪些研究得到资助的人很少能获得它提供的信息。这意味着我们有浪费研究资金的风险。这也意味着，如果患者参加从NHS的角度来看不合理的研究，他们将面临不必要的风险。VoI基于正式的成本效益分析，NHS决策者已经依赖于此。主要是通过我们的项目合作伙伴之一国家健康和护理卓越研究所（NICE）进行。NICE使用成本效益分析来决定NHS应该提供哪些药物，测试和程序。为了使他们的药物获得批准，制造商向NICE提交成本效益分析。这些通常由专业咨询公司进行，如我们的合作伙伴Source Health Economics。NICE还就未来可能有用的研究提出了建议。但它不进行或要求VoI，因此它不能为研究资助者提供正式的指导。VoI目前没有得到更广泛应用的主要原因是它很难做到。分析师需要先进的技术技能。即使是现代计算机也可能难以完成所需的所有计算。在线工具（包括我们的合作者开发的一个）可以近似VoI，使其更容易为分析师和计算机使用。不幸的是，近似值仅适用于简单的分析。在之前的MRC资助中，我们展示了如何以更低的计算需求准确估计VoI。我们使用了一种称为多级蒙特卡罗（MLMC）的数学技术。然而，MLMC依赖于复杂的数学，很少有人开发成本效益分析。我们希望通过创建一个易于使用的在线工具来改进VoI的使用，该工具可以快速可靠地估计VoI，即使是复杂的分析。我们将首先测试现有的近似方法，以了解它们足够好的情况。对于更复杂和更现实的分析，我们将使用MLMC。为此，我们将开发软件，将成本效益模型从Microsoft Excel转换为编程语言。Microsoft Excel是最常用的成本效益分析软件，但它运行MLMC本身太慢。我们将开发可重复使用和有效的MLMC代码，用户可以应用到转换后的模型。我们的合作伙伴是NICE指南中心（NICE CfG）和Source Health Economics。他们都开发了大量基于Excel的成本效益分析。它们将就检验近似值的模型类型提供指导。他们还将提供示例成本效益模型，我们可以在其上测试我们的软件。为NICE CfG和Source Health Economics工作的人将试用我们的工具。我们将使用他们的反馈，以确保它易于使用。我们还将与我们的研究合作伙伴和NIHR合作，以确保人们了解我们的在线工具，并在未来的实践中使用它。我们将与NICE CfG合作，将VoI纳入新的国家指导方针，为未来的研究提供建议。Source Health Economics将向制造商推荐使用我们的新工具，以便他们可以在NICE提交中包括VoI。在我们项目的每个阶段，我们将直接与NIHR接触，他们对我们的工作表示了兴趣。我们还将在项目开始和结束时邀请他们参加在线研讨会。我们的最终目标是为NIHR提供工具和资源，以正式优先考虑医疗保健研究。这将减少浪费的研究，并使英国各地的患者受益。