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

为了决定向患者推荐哪种治疗方法，我们需要对不同治疗方法之间的比较进行可靠的估计。然而，可能无法获得直接比较所有感兴趣治疗的研究。相反，我们经常进行混合研究来比较所选择的不同治疗方法，或者在某些情况下只比较单一治疗方法。此外，不同研究中的患者之间可能存在差异，这些差异会改变治疗的效果。为了解决这些问题，一种称为“多级网络元回归”（ML-NMR）的统计方法是可用的。这种方法结合了来自多项研究的证据，其中一些研究提供了每个参与者的个人水平数据，而另一些研究仅提供了已发表的汇总估计值，并考虑了患者人群之间的差异-这一过程称为“人群调整”。重要的是，这种方法可以产生特定于相关人群的估计值，以供决策（例如英国患者人群）。这意味着国家健康和护理卓越研究所（NICE）等决策者可以针对相关人群做出更好的决策。然而，在实践中使用ML-NMR存在一些障碍，如果要更广泛和有效地用于决策，则需要解决这些障碍。首先，该方法需要关于每种处理的大量数据，而这些数据并不总是可用的。例如，一家公司向NICE提交的申请可能有来自他们自己的治疗试验的个人水平数据，但只发表了来自竞争对手试验的摘要。如果没有足够的数据，我们可能会试图通过对不同治疗组的工作方式进行假设来简化统计模型，但这些假设可能并不合适，这可能导致结果中的系统性错误和得出错误的结论。其次，临床试验通常会遇到一些问题，例如数据缺失，参与者没有接受分配给他们的治疗，或者参与者被允许转换治疗（例如，如果他们的疾病进展）。统计方法可用于解释这些问题，因为如果处理不当，可能会导致结果中的系统性错误。然而，目前这些方法不能与用于解释群体之间差异的方法（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工作良好。这将通过以下方式实现：i）为ML-NMR开发新的统计方法，以使用从已发表的试验报告中获得的额外信息; ii）提出建议，以更新如何报告临床试验的指南，以提高已发表报告中额外信息的可用性; iii）通过真实的和模拟的例子研究统计方法的性能; iv）开发新的统计方法，将联合收割机群体调整与解释临床试验中常见问题（如缺失数据或转换治疗）的方法相结合;以及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，并将导致更好的知情治疗决定。统计方法的拟议进展和报告临床试验的最新建议有可能在更广泛的背景下改变医疗保健决策，即使只有已发表的总结数据可用，例如NICE临床指南的制定。此外，还有直接应用于个性化医疗，其中建议针对个人或较小的群体。

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.