NIRG: Evaluation of interventions with rare events: methods for parallel cluster randomised trials and stepped-wedge cluster randomised trials

NIRG：罕见事件干预措施的评估：平行整群随机试验和阶梯楔形整群随机试验的方法

• 批准号: MR/X029492/1
• 负责人: Jennifer Thompson
• 金额: $ 52.79万
• 依托单位: London School of Hygiene & Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX029492%2F1
• 关键词: NIRG; Evaluation; interventions; rare; events

This project will improve the statistical methods of trials when the outcome of the trial is rare. It will focus on trials that randomise groups of people rather than individual people. Trials are the gold standard for assessing whether an intervention has a positive impact across all areas of health research and beyond. However, it is sometimes difficult to randomise individual people. This could be because the intervention being tested affects a whole village or hospital ward, or the trial is testing the impact of large-scale rollout of an individual intervention. When this is the case, we can randomise groups of individuals such as whole villages or hospital wards that we call clusters. This randomisation of groups has implications for how we analyse data from the trial because individuals in the same cluster are likely to be more like one another than individuals from different clusters. This analysis is further complicated if the outcome we are trying to affect in the trial is a rare event, such as reducing levels of a disease from an already low starting point. For example, in infectious diseases, even a small increase in the number of disease cases can have a large impact on the disease trajectory. When we are interested in a rare event, there might be some clusters (the groups of people) where no one experience the event of interest (e.g. contracted the disease). In this project, I will explore how we deal with these rare events in the analysis of these trials, so that even when we have a rare outcome, we can trust the results. The project will focus on two types of trials. In the first type, the clusters are randomised to receive either the new intervention or a control condition for the duration of the trial. Current methods of analysing these trials are known to inaccurately estimate the effect of the intervention or underestimate our confidence in that estimate. I will develop a new method of analysis to over come this issue. New software will be created to help researchers implement our new analysis method.The second type of trial is called a stepped-wedge cluster randomised trial. Here, we randomise clusters to switch to the intervention at different times during the trial so that by the end of the trial all or most clusters have received the intervention. When events are rare, it is expected that standard methods of analysis will struggle to provide results or will give inaccurate estimates of our uncertainty in results. This project will determine how to conduct an existing analysis method for stepped-wedge cluster randomised trials; the method has not been used for these trials before. Like the first type of trial, I will also create software to help researchers implement the analysis methods I find work well.It is vital that we have validated and trustworthy analysis methods to use with these trials to be able to improve human health. By the end of this project, both types of trials will have improved, validated methods of analysis that can be used when the outcome of the trial is a rare event.

当试验结果很少时，该项目将改善试验的统计方法。它将着重于随机分组人而不是个人人群的试验。试验是评估干预措施是否在所有健康研究及其他领域都产生积极影响的黄金标准。但是，有时很难随机化个人。这可能是因为接受测试的干预措施会影响整个村庄或医院病房，或者试验正在测试大规模推出个人干预措施的影响。在这种情况下，我们可以随机分组我们称为集群的整个村庄或医院病房。组的这种随机化对我们如何分析试验的数据具有影响，因为同一集群中的个体可能比来自不同集群的个体更像彼此。如果我们试图在试验中影响的结果是罕见的事件，例如从已经很低的起点降低疾病水平，则该分析将更加复杂。例如，在传染病中，即使是疾病病例数量的少量增加也会对疾病轨迹产生很大的影响。当我们对罕见事件感兴趣时，可能会有一些集群（一群人），没有人会遇到感兴趣的事件（例如，疾病感染）。在这个项目中，我将探讨我们如何在分析这些试验的分析中处理这些罕见事件，以便即使我们有罕见的结果，我们也可以相信结果。该项目将集中在两种类型的试验上。在第一种类型中，簇被随机分配以在试验期间接收新的干预措施或控制条件。当前分析这些试验的方法是不准确地估计干预措施的影响或低估了我们对该估计的信心。我将开发一种新的分析方法，以解决这个问题。将创建新软件以帮助研究人员实施我们的新分析方法。第二种试验称为阶梯式斜线群集随机试验。在这里，我们在试验期间在不同时间切换到干预措施，以便在试验结束时，全部或大多数群集都接受了干预。当事件很少见，预计标准的分析方法将难以提供结果，或者对结果的不确定性估计不准确。该项目将确定如何为阶梯式临时聚类随机试验进行现有分析方法；该方法以前尚未用于这些试验。像第一种试验一样，我还将创建软件来帮助研究人员实施我发现工作良好的分析方法。至关重要的是，我们已经验证了可信赖的分析方法，可以与这些试验一起使用，以便能够改善人类健康。到该项目结束时，两种类型的试验都将改进，经过验证的分析方法，当试验的结果是罕见事件时，可以使用。