Developing guidance for design and conduct of cluster randomised trials

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，需要对它们进行测试。研究人员需要确保他们谨慎地进行这些测试研究。一种特殊类型的研究被称为集群随机试验。在这种类型的研究中，分组（称为集群）而不是单个患者被随机分配到不同的治疗方法。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大量投资。虽然研究界已经有很多年的时间来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。群集试验与传统的患者随机试验有很大不同。不幸的是，许多群集试验是以这样一种方式进行的，结果可能是有偏见的。这项研究的目的是做什么：一个常见的偏差来源是，参与者的招募通常是在随机化之后进行的。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。然而，在那些没有提供新课程的初级保健实践中，他们可能没有参与研究的明显动机。在试验结束时，研究人员将无法确定是否有任何体重减轻是由于新的类别，还是由于招募的患者的差异。这种偏倚在病人随机试验中不会发生，因为研究人员明白预防这种情况很重要。还有许多其他类型的偏差会对群集试验产生影响。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。这些模型只有在有大量集群（大约40个）的情况下才有效，但在集群试验中，平均集群数量只有30个，而且很多都比这个少得多。这些技术细节意味着，从这些试验中得出的对治疗效果的估计很可能不能如实反映事实。然而，减轻这些偏见风险的理解和能力已经存在。例如，为了克服两组之间的招聘差异问题，招聘应该由不知道集群分配到什么待遇的人进行。例如，在运动班试验中，在患者决定是否要参加试验之前，不应该告诉他们是否要参加运动班。对于技术性更强的偏见，也有解决方案，本质上更数学化。不幸的是，关于这些解决方案的知识在实施这些研究设计的人中并不广为人知或可用。我们将如何进行研究？我们建议制定指南，以便以更高的标准实施群集试验。我们是一群从经验中学习的研究人员，帮助进行了许多群集试验，有时会犯错误，但从这些错误中吸取教训。我们还将争取其他人的帮助——通过确定在进行集群试验方面有实际经验的其他人的观点，以及了解更多技术问题的科学家的观点。我们还将回顾文献，以确保指导是最新的。这项研究意味着什么？做任何研究都是要花钱的，如果我们对这项研究了解得更多，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对使用结果更有信心。

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.