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Effective population adjustment in evidence synthesis of randomised controlled trials for health technology assessment

卫生技术评估随机对照试验证据合成中的有效人群调整

基本信息

批准号：
MR/W016648/1
负责人：
David Phillippo
金额：
$ 98.61万
依托单位：
University of Bristol
依托单位国家：
英国
项目类别：
Fellowship
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%2FW016648%2F1
关键词：
Effective population adjustment evidence synthesis

项目摘要

To decide which treatments to recommend to patients, we need reliable estimates of how the different treatments compare to each other. However, studies that directly compare all treatments of interest may not be available. Instead, we often have a mixture of studies that compare a selection of different treatments, or in some cases only a single treatment. Furthermore, there may be differences between the patients in the different studies that change how well the treatments work. To address these issues, a statistical method called "multilevel network meta-regression" (ML-NMR) is available. This method combines evidence from multiple studies, where some studies provide individual-level data on every participant and some only provide published summary estimates, and accounts for differences between patient populations - a process known as "population adjustment". Importantly, this method can produce estimates that are specific to a relevant population for decision-making (e.g. the UK patient population). This means that decision makers such as the National Institute for Health and Care Excellence (NICE) can make better decisions that are targeted to the relevant population. However, there are several barriers to the use of ML-NMR in practice which need to be addressed if it is to be used more widely and effectively for decision-making. Firstly, the method requires substantial amounts of data on each treatment, which are not always available. For example, a company making a submission to NICE is likely to have individual-level data from their own trials of their own treatment, but only published summaries from their competitors' trials. Without enough data, we may instead attempt to simplify the statistical model by making assumptions about how different groups of treatments work, but these assumptions may not be appropriate, which can lead to systematic errors in the results and the wrong conclusions being drawn. Secondly, it is common for clinical trials to encounter issues such as missing data, participants not receiving the treatment they were assigned, or participants being allowed to switch treatments (e.g. if their disease progresses). Statistical methods are available to account for these issues, since if they are not handled correctly they can lead to systematic errors in the results. However, currently these methods cannot be used together with methods to account for differences between populations like ML-NMR. This project aims to address these issues to ensure that ML-NMR works well in situations most frequently encountered by decision makers. This will be achieved by: i) developing novel statistical methods for ML-NMR to use additional information available from published trial reports; ii) making recommendations to update guidelines for how clinical trials are reported, to improve the availability of this additional information in published reports; iii) investigating the performance of the statistical methods through real and simulated examples; iv) developing novel statistical methods to combine population adjustment with methods that account for common issues in clinical trials such as missing data or switching treatments; and v) developing accessible software tools and training courses to support the uptake of the methods.This research will have direct impact for decision makers such as NICE and will lead to better informed treatment decisions. The proposed advances in statistical methods and updated recommendations for reporting clinical trials have the potential to transform healthcare decision-making in wider contexts, even when only published summary data are available, such as the development of NICE clinical guidelines. Additionally, there are direct applications in personalised medicine, where recommendations are targeted to individuals or smaller groups.

为了决定向患者推荐哪种治疗方法，我们需要可靠地估计不同治疗方法之间的比较。然而，直接比较所有感兴趣的治疗方法的研究可能不可用。相反，我们经常进行混合研究来比较一系列不同的治疗方法，或者在某些情况下仅比较单一的治疗方法。此外，不同研究中的患者之间可能存在差异，从而改变治疗效果。为了解决这些问题，可以使用一种称为“多级网络元回归”（ML-NMR）的统计方法。这种方法结合了多项研究的证据，其中一些研究提供了每个参与者的个人水平数据，一些研究仅提供已发表的摘要估计，并解释了患者群体之间的差异 - 这一过程称为“群体调整”。重要的是，这种方法可以生成特定于相关人群（例如英国患者人群）的估计值以进行决策。这意味着国家健康与护理卓越研究所 (NICE) 等决策者可以针对相关人群做出更好的决策。然而，如果要更广泛、更有效地将 ML-NMR 用于决策，就需要解决在实践中使用 ML-NMR 的一些障碍。首先，该方法需要每种治疗的大量数据，而这些数据并不总是可用的。例如，向 NICE 提交材料的公司可能会拥有来自自己治疗试验的个人数据，但仅发布竞争对手试验的摘要。如果没有足够的数据，我们可能会尝试通过假设不同的治疗组如何发挥作用来简化统计模型，但这些假设可能不合适，这可能会导致结果出现系统性错误并得出错误的结论。其次，临床试验经常遇到数据缺失、参与者未接受分配的治疗或允许参与者改变治疗（例如，如果他们的疾病进展）等问题。统计方法可以解决这些问题，因为如果处理不当，可能会导致结果出现系统错误。然而，目前这些方法不能与解释群体之间差异的方法（例如 ML-NMR）一起使用。该项目旨在解决这些问题，以确保 ML-NMR 在决策者最常遇到的情况下正常工作。这将通过以下方式实现：i) 开发新的 ML-NMR 统计方法，以使用已发表的试验报告中提供的附加信息； ii) 提出建议，更新临床试验报告方式的指南，以提高已发表报告中附加信息的可用性； iii) 通过真实和模拟的例子调查统计方法的性能； iv) 开发新的统计方法，将群体调整与解决临床试验中常见问题（例如数据缺失或转换治疗）的方法结合起来； v) 开发可访问的软件工具和培训课程以支持这些方法的采用。这项研究将对 NICE 等决策者产生直接影响，并将导致更明智的治疗决策。所提出的统计方法的进步和报告临床试验的更新建议有可能在更广泛的背景下改变医疗保健决策，即使只有已发布的摘要数据可用，例如 NICE 临床指南的制定。此外，它还可以直接应用于个性化医疗，其中的建议针对个人或较小的群体。