Enhancing the design and analysis of cluster randomised trials using machine learning

点击翻译按钮获取中文摘要

Trials are important to evaluate the safety and efficacy of new treatments or interventions. One type of trial, called a cluster randomised trial (CRT) uses pre-existing groups of individuals - known as clusters - who are randomly allocated to different treatments. It means that every member of the same cluster, for instance members of the same family or patients from the same hospital, will receive the same treatment. This type of trial is very useful in real world settings where individual randomisation to treatments is not possible or the intervention is naturally applied to a whole cluster. However, this type of trial requires the use of specific analysis methods to account for the similarity in the response to treatments among members of the same cluster. Moreover, cluster randomised trials are often prone to bias, which can be corrected at the analysis stage with the use of additional information about the individuals and clusters included in the trial. This additional information can also be used to improve the precision of the trial, and to identify the individuals who will most benefit from the intervention being tested. Therefore, it is crucial to select the important variables and use appropriate statistical methods accounting for these variables to obtain an accurate estimation of the treatment efficacy and safety. However, the best way to do so remains unknown, especially in an era where there is a large amount of medical information available. In this fellowship I will draw on the emerging field of machine learning to address these methodological challenges and to determine how routinely collected medical data can be best used to improve the design of CRTs. I will use several existing trial datasets to achieve this, including CRTs in the fields of pharmacy and psychiatry, as well as data from the England Cancer Registry. Drawing on these multiple datasets but also using mathematical developments and computer-based simulation studies, I will develop and evaluate methods to improve the generalisability of CRTs, their precision, and bring us one step closer to a more personalized medicine approach for patients.