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

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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.