Developing guidance for design and conduct of cluster randomised trials

制定整群随机试验的设计和实施指南

• 批准号: MR/W020688/1
• 负责人: Karla Hemming
• 金额: $ 7.72万
• 依托单位: University of Birmingham
• 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: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FW020688%2F1
• 关键词: Developing; guidance; design; conduct; cluster

Why this research is needed:Researchers constantly look for ways to improve patient's health. Before new treatments are introduced, they need to be tested. Researchers need to make sure they conduct these testing studies carefully. One particular type of study is called the cluster randomised trial. In this sort of study groups (called clusters) rather than individual patients are randomised to different treatments. These trials require large investments not only in terms of funding and resources but also the investment from the actual research participants. Whilst the research community has had many years to learn how to conduct patient randomised trials very well, they have a lot less experience in conducting cluster trials. Cluster trials are very different to conventional patient randomised trials. Unfortunately, many cluster trials are conducted in such a way that their results might be biased. What this research aims to do:One common source of bias occurs because recruitment of participants often happens after randomisation. For example, perhaps the experimental intervention is a new form of exercise class for patients who are over-weight. In those primary care centres randomised to the new classes, if potential research participants know they will be offered these classes many may be interested in participating. Whereas in those primary care practices where the new class is not offered, they may be no perceived incentive to participate in the research. At the end of the trial researchers would not be able to identify if any loss in weight was due to the new class, or the differences in the patients recruited. This sort of bias does not happen in patient randomised trials, because researchers understood that it was important to prevent this. They are many other types of biases that have an impact on cluster trials. Other risks are more technical in nature and mostly relate to the mathematical models used to estimate treatment effects. These models only work well when there are a large number of clusters (>40) yet the average number of clusters in cluster trials is just 30 and many have a lot fewer than this. These technicalities mean that the estimates of how well the treatments work from these trials are likely to be poor reflections of the truth. However, the understanding and ability to mitigate these risks of bias already exists. For example, to overcome the issues of recruitment differences between the two groups, the recruitment should be by someone who does not know what treatment the cluster has been allocated to. For example, in the exercise class trial, the patients shouldn't be told about whether they will get the exercise class until after they have decided if they want to participate in the trial. For the more technical bias, there are also solutions, more mathematical in nature. Unfortunately, this knowledge about these solutions is not widely known or available among those who implement these study designs. How we will do the researchWe propose to produce guidance so that cluster trials can be implemented to a much higher standard. We are a group of researchers who have learnt by experience, having helped to conduct many cluster trials, sometimes making mistakes but learning from those mistakes. We will also enlist the help of others - by ascertaining the views of other people who have practical experience in running cluster trials and also from scientists who understand the more technical issues. We will also review the literature to make sure the guidance is current. What will the research mean?Doing any study costs money, and if we know more about the study and the best ways to use it, the study will be more effective. Our guidelines and recommendations will make sure that researchers run cluster trials in the best way. Researchers will be more confident in the results, which will mean that people who make decisions on care will be more confident in using the results.

为什么需要这项研究：研究人员不断寻找改善患者健康的方法。在引入新的治疗方法之前，需要对其进行测试。研究人员需要确保他们仔细进行这些测试研究。一种特殊类型的研究被称为集群随机试验。在这类研究中，将患者分组（称为集群）而不是将单个患者随机分配到不同的治疗方案中。这些试验不仅需要资金和资源方面的大量投资，而且需要实际研究参与者的投资。虽然研究界多年来一直在学习如何很好地进行患者随机试验，但他们在进行群集试验方面的经验要少得多。聚类试验与传统的患者随机试验有很大的不同。不幸的是，许多聚类试验的进行方式可能会导致其结果出现偏差。这项研究的目的是做什么：一个常见的偏见来源是因为参与者的招募通常发生在随机化之后。例如，也许实验性干预是针对超重患者的一种新形式的运动课程。在那些随机分配到新课程的初级保健中心，如果潜在的研究参与者知道他们将被提供这些课程，许多人可能会有兴趣参加。而在那些初级保健的做法，新的类是不提供，他们可能没有感知的动机参与研究。在试验结束时，研究人员将无法确定体重的任何减轻是否是由于新的类别或招募的患者的差异。这种偏见不会发生在患者随机试验中，因为研究人员知道防止这种情况很重要。它们是许多其他类型的偏见，对集群试验有影响。其他风险在本质上更具技术性，主要与用于估计治疗效果的数学模型有关。这些模型只在有大量聚类（>40）的情况下才能很好地工作，但聚类试验中的平均聚类数只有30个，而且许多聚类数比这少得多。这些技术细节意味着，从这些试验中对治疗效果的估计很可能不能很好地反映事实。然而，已经存在减轻这些偏倚风险的理解和能力。例如，为了克服两组之间的招募差异问题，招募人员应该是不知道该群组被分配到什么治疗的人。例如，在运动课试验中，患者不应该被告知他们是否会得到运动课，直到他们决定是否要参加试验。对于更技术性的偏见，也有解决方案，更多的数学性质。不幸的是，实施这些研究设计的人并不广泛了解或获得有关这些解决方案的知识。我们将如何进行研究我们建议制定指导方针，以便集群试验能够以更高的标准实施。我们是一群从经验中学习的研究人员，他们帮助进行了许多集群试验，有时会犯错误，但会从错误中学习。我们还将寻求其他人的帮助--通过确定其他在进行群集试验方面有实际经验的人的观点，以及了解更多技术问题的科学家的观点。我们还将审查文献，以确保指导是最新的。研究意味着什么？做任何研究都是要花钱的，如果我们对这项研究有更多的了解，并知道最好的使用方法，这项研究就会更有效。我们的指导方针和建议将确保研究人员以最佳方式进行群集试验。研究人员将对结果更有信心，这意味着做出护理决定的人将更有信心使用结果。

项目成果

Commentary: Estimands in cluster trials: thinking carefully about the target of inferenceand the consequences for analysis choice.

• DOI: 10.1093/ije/dyac174
• 发表时间: 2023-02-08
• 期刊: International journal of epidemiology
• 影响因子: 7.7

Protocol for a parallel group, two-arm, superiority cluster randomised trial to evaluate a community-level complementary-food safety and hygiene and nutrition intervention in Mali: the MaaCiwara study (version 1.3; 10 November 2022).

• DOI: 10.1186/s13063-022-06984-5
• 发表时间: 2023-01-30
• 期刊: Trials
• 影响因子: 2.5