Improving the statistical efficiency of randomised controlled trials through their design

随机对照试验（RCT）是一种评估药物治疗是否有效的方法，其中患者被随机分配到接受治疗或对照组，并比较其结果。随机对照试验被认为是比较治疗的金标准方法，但它们昂贵且耗时。因此，良好的设计至关重要。改善随机对照试验设计的一种方法是提高其统计效率，这是一种衡量随机对照试验检测治疗效果（如果存在）能力的指标。如果我们能够设计出统计学上更有效的随机对照试验，那么就有可能用更少的患者或更短的时间来运行它们，这也将降低成本。本研究的主要目的是扩展现有的方法来比较不同随机对照试验设计的统计效率，并在几个病例研究中探索不同设计的效果。提高随机对照试验统计效率的一种方法是不止一次地测量患者的结局。在计划此类随机对照试验时，有必要选择设计特征，如随访访视的次数和间隔。该奖学金的第一个目的是调查，并在必要时扩展，现有的方法来评估不同的RCT设计功能的效率。这些方法将应用于几个案例研究，包括两个随机对照试验的数据-一个是多发性硬化症患者，另一个是他汀类药物对肌肉疼痛的影响。许多随机对照试验都存在数据缺失的问题，例如，如果一些患者错过了随访。此外，患者并不总是接受分配给他们的治疗，例如，如果他们忘记或不想接受治疗。本项目的第二个目标是扩展上述方法以科普这些情况，并研究在这些情况下设计特征的最佳选择如何变化。研究金的最终目标是探索哪些类型的数据可以用于规划未来RCT的设计。以前的随机对照试验可能没有任何数据可用于帮助设计未来的随机对照试验，但可能有常规收集的数据来源，如疾病登记和电子健康记录。然而，目前还不清楚这些数据集是否可以用来帮助选择设计功能。该研究将使用两个案例研究来评估使用常规收集的数据来设计RCT的可行性。第一个案例研究将使用来自亨廷顿病患者的两种不同来源的数据：一种是以非常受控的方式收集的，另一种是疾病登记处。第二个案例研究将使用来自初级保健电子健康记录的数据，通过开发评估不同设计特征的统计效率的方法，本研究将为未来设计RCT的研究人员提供可使用的工具和信息。

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.