Geostatistical design and analysis of randomised evaluations with a geographic basis

随机分配个人接受治疗或替代对照的研究使我们能够估计患者接受治疗时发生什么和不发生什么，从而估计其效果。随机化研究的成功导致了研究的发展，取而代之的是将村庄、儿童教室或养老院居民等人群随机分组，让他们一起接受干预。基于集群的研究是有用的，因为在许多情况下，集群中的个人可能是相似的，并相互作用。对一个集群中的一个个体进行干预，可能会对该集群的其他成员产生间接影响，这将破坏对个体进行随机化的研究，但不会对基于集群的研究产生影响。许多随机研究在多个时间点观察研究参与者或集群，可能是在实施干预之前和之后。在统计文献中，已经有很多关于如何处理我们捕获的数据随时间变化的分析-例如，测量的时间间隔越远，事情可能就越不相似。捕捉时间的影响对于确保我们的研究设计得当和分析得当很重要。然而，对于随机研究，几乎没有关于如何处理不同空间的数据的分析--距离越近，它们可能越相似--因此，对于最好的设计(这可能是重要的)，几乎没有什么指导。这个项目将考虑如何根据人们居住的地方设计和分析研究，通常是通过将靠近可能干预地点的人包括在内。一个例子是对在低收入国家的城市安装新油井的影响进行研究，并将居住在每个集群中可能的油井位置附近的人包括在内。在这些研究中，太空很重要。对住得很近的人的结果的测量可能比他们住得很远的人更相似，因为例如，人们可能会将传染病传播给另一个人。然而，我们通常认为，集群中的人彼此之间的距离有多远，或者他们距离干预的距离有多远，都是无关紧要的。虽然这种方法不一定会导致对干预效果的错误估计，但它可能意味着我们的精确度低于我们所需的精确度，需要进行更大规模、更昂贵的研究。这也意味着我们不了解干预的效果在空间上是如何变化的，如果我们想在现实世界中推出干预，这是一个重要的考虑因素。我们将采用地理空间统计领域的方法来开发集群试验的空间设计和分析方法。明确考虑空间也为一种新型的随机研究打开了大门，在这种研究中，我们不是随机分配患者或集群接受干预，而是随机选择干预的地点。我们称之为“空间试验”，它对评估干预措施在不存在自然聚集区的地方的效果有潜在的好处。例如，如果一个城市在全市范围内向许多地点铺设新油井，我们的工作主要是统计，包括在随机研究设计中分析不同的统计模型是如何起作用的。为了能够使用新方法，我们将制作在标准统计包中运行的软件，并提供详细的文件和例子，我们将在网上提供。我们看到了“实施科学”研究的特别好处，该研究旨在研究“现实世界”干预措施会发生什么。我们的工作将有助于设计这些干预措施可以推广的方式，以便能够可靠地衡量其影响。然而，任何在一个地区设计干预研究的学术领域都将受益，包括农业、经济学和生态学。

Studies that randomly allocate individual people to receive a treatment or an alternative comparator allow us to estimate what does and does not happen when patients receive a treatment and hence estimate its effect. The success of the randomised study has led to the development of studies that instead randomise groups of people, or "clusters", such as villages, classrooms of children, or residents of nursing homes, to receive an intervention together. Cluster-based studies are useful as in many contexts individuals in a cluster will likely be similar and interact with one another. An intervention applied to one individual in a cluster could have indirect effects on other members of that cluster, which would undermine studies that randomise individuals, but not cluster-based ones. Many randomised studies observe study participants or clusters at multiple points in time, perhaps before and after an intervention is applied. In the statistical literature, there has been a lot of analysis about how to deal with how the data we capture changes over time - things are likely to be less similar the further apart in time they're measured, for example. Capturing the effects of time is important to making sure our studies are designed well and analysed properly. However, for randomised studies there has been little analysis about how to deal with data varying over space - the closer things are the more similar they are likely to be - and so there is little guidance on the best design when this is likely to matter.This project will consider how to design and analyse studies where a "cluster" is created based on where people live, typically by including people close to a possible intervention location. An example would be a study of the effect of installing new wells in a city in a low-income country and including people who live close to possible well locations in each cluster. In these studies, space matters. Measurements of outcomes from people who live near to one another are likely to be more similar than if they lived far apart as, for example, people can spread infectious disease to one another. However, we normally assume that it does not matter how far apart the people in a cluster are from one another nor how far from the intervention they are. While this approach does not necessarily lead to errors in the estimates of an intervention's effects, it can mean we are less precise than we need to be, requiring larger, more expensive studies. It also means we do not learn about how the effect of an intervention changes over space, an important consideration if we want to roll-out the intervention in the real-world.We will adapt methods from the field of geospatial statistics to develop methods for the spatial design and analysis of cluster trials. Explicitly accounting for space also opens up the door to a novel type of randomised study in which, instead of randomly assigning patients or clusters to receive an intervention, we randomly choose a location for an intervention. We call this a "spatial trial" and it has potential benefits for evaluating how well interventions work in places where natural clusters do not exist. For example, if a city were rolling-out new wells across the city to numerous locations.Our work is primarily statistical and consists of analysing how different statistical models work in a randomised study design. To enable the use of the new methods we will produce software that will run in standard statistical packages and provide detailed documentation and examples that we will make available online. We see particular benefit for "implementation science" research, which aims to study what happens with "real-world" interventions. Our work will aid in designing ways these interventions can be rolled out so that their effects can be reliably measured. However, any academic field that designs studies of interventions over an area will benefit, including agriculture, economics, and ecology.