Open-cohort designs for cluster-randomised trials in institutional settings: A methodology bolt-on to DCM-EPIC

随机临床试验（RCT）是一种公认的测试干预措施的研究方法。被招募的参与者通常被随机分配到接受干预或对照组。一开始就进行计算，以确定需要多少参与者才能得到精确的结果。然而，许多护理院或医院病房的干预措施是在环境（单位、病房或护理院）层面上起作用的。这意味着护理院或医院病房，以及其中的个体参与者，被随机作为一个集群。这被称为聚类随机对照试验。在聚类随机对照试验中，聚类的数量和参与者的数量都很重要。申请人目前正在进行一项集群随机对照试验，研究一种名为痴呆症护理测绘（DCM）的干预措施在减少痴呆症患者躁动方面的效果。工作人员使用DCM来确定他们可以在养老院做出的改进。因此，当使用DCM时，任何住在养老院的居民都可能受益。设计聚类随机对照试验有两种公认的方法。封闭队列设计在一段时间内跟踪同一个人，在试验的开始、结束和有时在中间时间点收集他们的数据。这样就可以分析干预对个人和群体的影响。我们的DCM试验采用了这种设计。护理院、医院和其他环境中的一个问题是，由于出院/搬迁或死亡，人员流动率很高。在此期间，其他个体也进入了环境，并暴露于干预措施中，但不包括在随机对照试验中。随机对照试验的时间越长，退出的个体就越多。为了做到这一点，必须在随机对照试验开始时招募更多的群体和个人，以确保在结束时仍然有足够的个体。这使得这项研究非常昂贵。在我们的DCM试验中，大约50%的住院医师将在我们最后16个月的随访中离开试验，主要是由于死亡。横截面设计是当前的备选方案。该设计在随机对照试验开始和结束时收集集群中个体的数据。它没有长期跟踪个体，而是假设个体在每个数据收集点都有所不同。这再次减少了以后可以招募的人员数量。另一种可采用的设计是开放队列随机对照试验。在本设计中，招募所有符合条件的新个体，并在随机化后连续或在设定的数据收集时间点收集数据。这种设计的潜在优势在于它可能更加(1)经济，因为需要招募的群体更少；(2)灵活，因为它可以回答与在随机对照试验中接触干预的个体有关的研究问题。然而，由于这种设计没有被广泛使用，目前还没有标准的指导方针来进行开放队列集群随机对照试验，统计学家应该如何计算所需的样本量以及如何最好地分析数据仍然存在问题。这是使用开放队列设计的一个障碍。在本研究中，我们将解决这些障碍。我们将回顾开放队列随机对照试验的文献。我们通过重新分析我们的DCM试验和我们在文献综述中确定的另外两个随机对照试验的数据来观察开放队列随机对照试验的统计特性。这将包括如何计算样本量，如何分析数据，以及在开放队列随机对照试验是最佳选择设计的情况下为研究人员提供建议。我们还将通过专家组会议和在线调查咨询进行随机对照试验的研究人员，以确保我们的发现被使用它们的人视为可接受的。我们将制定开放队列集群随机对照试验的实用指南，供计划进行未来试验的研究人员使用。这些将通过学术论文、会议和研究资助者的简报文件进行分享。

A randomised clinical trial (RCT) is an accepted research method for testing interventions. Recruited participants are often randomly assigned to receive the intervention or to a control group. Calculations are made at the start to work out how many participants are needed to get a precise result. However, many interventions in care homes or hospital wards work at a setting (unit, ward or care home) level. This means the care home or hospital ward, and individual participants in them, are randomised as a cluster. This is called a cluster RCT. In cluster RCTs the number of clusters as well as the number of participants is important.The applicants are currently carrying out a cluster RCT that is looking at how effective an intervention called Dementia Care Mapping (DCM) is at reducing agitation in people with dementia. Staff members use DCM to identify improvements they can make in the care home. Therefore, any resident living in the care home while DCM is being used, will potentially benefit.There are two accepted ways of designing a cluster RCT. A closed-cohort design follows the same individuals over time, collecting data on them at the start, end and sometimes at middle time points during a trial. This allows the effects of the intervention to be analysed on individuals and the group. Our DCM trial uses this design. A problem in care homes, hospitals and other settings is the high turnover of individuals due to discharge/moving or death. Additional individuals also enter the setting over this time and are exposed to the intervention but are not included in the RCT. The longer the RCT is, the more individuals are likely to drop out. To manage this many more clusters and individuals have to be recruited at the start of the RCT to be sure of having enough individuals still there at the end. This makes the research very expensive. In our trial of DCM, around 50% of residents will have left the trial by our final 16-month follow-up, mainly due to death. A cross-sectional design is the current alternative. This design collects data on individuals in a cluster at the start and at end of the RCT. It does not track individuals over time, instead assuming that individuals differ at each data collection point. This again reduces the number of individuals that can be recruited at later time points.A different design that could be used is an open-cohort cluster RCT. In this design, all newly eligible individuals are recruited and data collected on them either continuously or at set data collection time points after randomisation. Potential advantages of this design are it may be more (i) economical because fewer clusters need to be recruited, (ii) flexible as it can allow research questions relating to individuals exposed to the intervention throughout an RCT to be answered. However, as this design is not widely used, there is currently no standard guidance for those carrying out open-cohort cluster RCTs and there remain questions about how statisticians should work out the sample size needed and how to best analyse the data. This is a barrier to using an open-cohort design.In this study, we will address these barriers. We will review the literature on open-cohort RCTs. We look at the statistical properties of open-cohort RCTs by reanalysing data from our DCM trial and from two further RCTs we identify in the literature review. This will include how to work out the sample size, how to analyse the data and advice for researchers on the situations when an open-cohort RCT will be the best choice design. We will also consult researchers who carry out RCTs, through expert group meetings and on-line surveys to make sure our findings are seen as acceptable by those who will use them. We will develop practical guidelines about open-cohort cluster RCTs that can be used by researchers who are planning to carry future trials. These will be shared through academic papers, conferences and briefing documents for research funders.