Real-world treatment effectiveness in people with type 2 diabetes: Maximising the applicability of clinical trials

了解一种新药是否有效的最好方法是进行临床试验。在临床试验中，参与者被随机选择不同的药物（即偶然）。因此，平均而言，接受不同治疗的参与者是相似的。因此，研究人员可以比较接受不同治疗的组，并决定哪些是最有效的。然而，试验参与者通常比其他患者更年轻，更健康。因此，临床医生和其他人对试验结果是否与“真实世界”中的许多患者相关表示不确定，为了解决这个问题，一些研究人员转而使用常规数据研究治疗方法。与试验不同，常规数据可以从所有接受医疗保健的患者（例如从医疗记录）中收集，包括许多老年体弱患者。然而，当这种数据用于比较治疗时，很难确定（即使是非常复杂的分析）任何差异都是由药物引起的。这是因为，与临床试验不同，患者的临床特征不同，治疗方法也不同。本课题将联合收割机临床试验与常规数据相结合，发挥两者的优势。我们将使用常规数据来“校准”试验结果。当对试验结果进行校准时，来自代表性不足的群体（如老年女性）的结果比来自其他代表性过高的群体（如年轻男性）的结果对总体结果的影响更大。校准后，我们可以更加确信试验结果是相关的。此外，校准不会“打破”随机性;校准后的结果仍然可靠。一些较早的校准方法要求研究人员能够获得每个相关试验的非常详细的结果（例如每个试验参与者的结果）。在大多数情况下，这意味着校准不可行。然而，我们最近开发了一种执行校准的方法，该方法不需要每次试验都进行这种详细程度的校准。这种校准适用于更多的条件和治疗。我们现在首次提出使用这种新方法来校准使用常规数据的试验。具体来说，我们将进行校准，以确定哪些新的糖尿病药物对苏格兰和中国的实际患者最有效。我们将从苏格兰糖尿病患者的完整登记册和中国的两家医院获得常规数据。在确定了一组适合使用新药治疗的患者后，我们将计算每种新药可能的益处和危害，就像最初的临床试验是在中国或苏格兰进行的一样。我们将从所有试验中得出一个总体总结结果，供临床医生和糖尿病患者使用。我们还将把结果输入一个健康经济模型，以预测可能的成本、效益和资金价值。NICE等组织使用这些模型来提供有关药物的指南和法规。为了更好地传达我们对每种药物的有效性和性价比的研究结果，我们将开发一个交互式网络应用程序，供研究人员、临床医生和糖尿病患者使用。它将允许用户比较传统方法获得的结果，以及使用校准获得的结果。如果获得资助，该项目将产生关于糖尿病新药有效性差异的结果，这些结果是可靠的，临床医生可以放心地将其应用于现实世界中的糖尿病患者。除了为糖尿病患者带来实实在在的好处外，这还将首次证明校准可以提高试验结果的相关性。

The best way to know whether a new medicine works is to perform a clinical trial. In clinical trials, participants are selected to have different medicines at random (ie by chance). As a result, participants receiving different treatments are, on average, similar. Consequently, researchers can compare groups receiving different treatments, and decide which are the most effective.However, trial participants are commonly younger and fitter than other patients. As a result, clinicians and others have expressed uncertainty as to whether results from trials are relevant to many patients in "real-world" settings.To address this concern, some researchers have instead studied treatments using routine data. Unlike with trials, routine data can be collected from all patients receiving healthcare (eg from medical records) including many older frailer patients. However, when this kind of data is used to compare treatments it is very difficult to be sure (even with very sophisticated analyses) that any differences have been caused by the medication. This is because, unlike in trials, different treatments are offered to patients because of differences in their clinical features.In this project, we propose to combine clinical trial and routine data, using the strengths of both. We will use routine data to "calibrate" trial results. When trial results are calibrated, findings from under-represented groups (eg older women) influence the overall results more than findings from other over-represented groups (eg younger men). After calibration we can be more confident that the trial results are relevant. Also, calibration does not "break" the randomness; calibrated results remain reliable.Some older calibration methods required researchers to have access to very detailed results from every relevant trial (eg the result for every trial participant). In most situations, this meant calibration was unfeasible. However, we recently developed a method to perform calibration which does not require this level of detail for every trial. This calibration feasible for many more conditions and treatments. We now propose, for the first time, to use this new method to calibrate trials using routine data. Specifically, we will perform the calibration to decide which of the newer diabetes medicines are most effective in real-world patients in Scotland and China. We will obtain routine data from a complete register of people with diabetes in Scotland and from two hospitals in China. Having identified a group of patients suitable for treatment with the newer medicines, we will calculate the likely benefits and harms of each of the newer medicines as if the original clinical trials had been conducted in China or in Scotland.We will produce an overall summary result from all the trials, making this available to clinicians and people with diabetes. We will also feed the results into a health economic model to predict the likely costs, benefits and value for money. Such models are used by organisations such as NICE to inform guidelines and regulations about medicines.To better communicate our findings about the effectiveness and value for money of each medicine, we will develop an interactive web app, designed to be used by researchers, clinicians and people with diabetes. It will allow users to compare results which have been obtained the conventional way, alongside results obtained using calibration.If funded, this project will produce results about differences in the effectiveness of newer drugs for diabetes that are reliable, and that clinicians can confidently apply to patients with diabetes in real-world settings. As well producing tangible benefits for people with diabetes, this will also demonstrate, for the first time, that calibration can improve the relevance of trial results.