Improving the statistical efficiency of randomised controlled trials through their design

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Randomised controlled trials (RCTs) are a way of assessing whether medical treatments are effective or not, in which patients are randomly allocated to receive the treatment or to a comparison group and their outcomes compared. RCTs are considered to be the gold-standard method of comparing treatments, but they are expensive and time consuming to conduct. Designing them well is therefore crucial. One way of improving the design of RCTs is to increase their statistical efficiency, which is a measure of the ability of an RCT to detect a treatment effect if one is present. If we can design more statistically efficient RCTs, then it would be possible to run them with fewer patients or for a shorter length of time, which will also reduce costs. The major objectives of this fellowship are to extend existing methods to compare the statistical efficiency of different RCT designs, and to explore the effect of different designs in several case-studies.One way of increasing the statistical efficiency of an RCT is to measure a patient's outcome more than once. When planning such an RCT, it is necessary to make choices about design features such as the number and spacing of follow-up visits. The first aim of this fellowship is to investigate, and extend as necessary, existing methods for assessing the efficiency of different RCT design features. The methods will be applied to several case studies, including data from two RCTs - one of multiple sclerosis patients and the other looking at the effect of statins on muscle pain.Many RCTs suffer from missing data, for example if some patients miss a follow-up visit. In addition, patients do not always receive the treatment they are assigned, for example if they forget or do not want to take their treatment. The second aim of this project will be to extend the aforementioned methods to cope with these situations, and to investigate how the optimal choice of design features changes in these circumstances.The final aim of the fellowship is to explore what types of data can be used to plan the design of future RCTs. There may not be any data from previous RCTs available to help design future ones, but there might be routinely collected data sources, such as disease registries and electronic health records. However, it is not clear whether such data sets can be used to help choose design features. This fellowship will use two case-studies to assess the feasibility of using routinely collected data to design RCTs. The first case-study will use two different sources of data from patients with Huntington's disease: one which is collected in a very controlled manner, and the other which is a disease registry. The second case-study will use data from primary care electronic health records.By developing methods to assess the statistical efficiency of different design features, this research will provide tools and information that can be used by researchers who design RCTs in the future.