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NIHR CLAHRC YH Cohorts and Datasets Briefing paper
One such innovative method is the use of Cohorts and Big Data to drive new ways of researching and new evidence to improve the health and wealth of the people of Yorkshire and Humber. Through recruiting patients, to share data about their experience and medical conditions, into cohorts, we can allow other researchers to quickly identify and test out new treatments/interventions without having to start the recruitment process from scratch. The use of big data allows us to identify patterns at scale, ones which would not be visible through traditional methods. We can look at how people travel through a whole health care journey rather than a single piece of the jigsaw giving new insights to those who plan as well as deliver health care.
Cohort: Cohort studies are a type of medical research used to investigate the causes of disease, establishing
links between risk factors and health outcomes.
Cohort studies are usually forward-looking – that is, they are ‘prospective’ studies, or planned and carried out
over a future period.
Dataset: A collection of related sets of information that is composed of separate elements but can be
manipulated as a unit by a computer.
Trials within cohorts (TwiCs): ‘TwiCs’ stands for ‘Trials within Cohorts’. The ‘cohort multiple randomised
controlled trial’ is one approach to the design and conduct of pragmatic trials within cohorts.
Key features of the design are:
• During consent to the cohort, participants optionally consent to be contacted about additional studies and
for data linkage.
• Recruitment of a large observational cohort of patients with the condition of interest
• Regular measurement of outcomes for the whole cohort
• Capacity for multiple randomised controlled trials over time
There is a theoretical paper which outlines the new ‘cohort multiple randomised controlled trial’ design, which could help address the problems associated with existing approaches – www.bmj.com/content/340/bmj.c1066
Find out more NIHR CLAHRC YH Cohorts and Datasets Briefing paper