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

试验对于评估新疗法或干预措施的安全性和有效性很重要。一种类型的试验称为集群随机试验(CRT)，它使用预先存在的个体群体--也就是所谓的集群--随机分配到不同的治疗方案中。这意味着同一群组的每个成员，例如同一家庭的成员或同一家医院的病人，都将得到相同的治疗。这种类型的试验在现实世界中非常有用，因为在现实世界中，治疗的个体随机化是不可能的，或者干预自然地应用于整个集群。然而，这种类型的试验需要使用特定的分析方法来解释同一组成员之间对治疗反应的相似性。此外，组群随机试验往往容易产生偏差，这可以在分析阶段通过使用关于试验中包括的个人和组的额外信息来纠正。这些附加信息也可以用来提高试验的精确度，并确定哪些人将从正在测试的干预措施中获得最大好处。因此，选择重要的变量，并使用适当的统计方法来解释这些变量，以获得对治疗有效性和安全性的准确估计是至关重要的。然而，最好的方法仍然是未知的，特别是在一个拥有大量医疗信息的时代。在这项研究中，我将利用新兴的机器学习领域来解决这些方法论挑战，并确定如何最好地利用常规收集的医疗数据来改进CRT的设计。我将使用几个现有的试验数据集来实现这一点，包括药学和精神病学领域的CRT，以及来自英格兰癌症登记处的数据。利用这些多个数据集，同时利用数学发展和基于计算机的模拟研究，我将开发和评估各种方法，以提高CRT的通用性和精确度，并使我们离为患者提供更个性化的医学方法更近一步。

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.