Geostatistical design and analysis of randomised evaluations with a geographic basis

基于地理的随机评估的地统计设计和分析

• 批准号: MR/V038591/1
• 负责人: Samuel Watson
• 金额: $ 61.76万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FV038591%2F1
• 关键词: Geostatistical; design; analysis; randomised; evaluations

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.

随机分配个体接受治疗或替代比较的研究使我们能够估计患者接受治疗时会发生什么和不会发生什么，从而估计其效果。随机研究的成功导致了研究的发展，而不是随机分组的人，或“集群”，如村庄，儿童教室或养老院的居民，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，集群中的个体可能是相似的，并且相互作用。对一个群体中的一个个体进行干预可能会对该群体的其他成员产生间接影响，这将破坏随机化个体的研究，而不是基于群体的研究。许多随机研究在多个时间点观察研究参与者或集群，可能在应用干预之前和之后。在统计学文献中，有很多关于如何处理我们捕获的数据如何随时间变化的分析-例如，事情可能不太相似，因为它们被测量的时间越远。捕捉时间的影响对于确保我们的研究设计良好并进行适当的分析非常重要。然而，对于随机研究来说，几乎没有分析如何处理数据在空间上的变化--越接近的东西越可能相似--因此，当这可能很重要时，几乎没有关于最佳设计的指导。本项目将考虑如何设计和分析基于人们居住地创建“集群”的研究，通常通过包括接近可能的干预位置的人。一个例子是，研究在低收入国家的一个城市安装新的威尔斯水井的影响，并将居住在每个群组中可能的水井位置附近的人包括在内。在这些研究中，空间很重要。对居住在一起的人的结果的测量可能比居住在一起的人更相似，例如，人们可以相互传播传染病。然而，我们通常认为，集群中的人彼此相距多远，或者他们距离干预有多远都无关紧要。虽然这种方法不一定会导致对干预效果的估计错误，但它可能意味着我们不太精确，需要更大，更昂贵的研究。这也意味着我们不了解干预措施的效果如何随空间变化，如果我们想在现实世界中推广干预措施，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聚类试验的空间设计和分析方法。对空间的解释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，而是随机选择一个干预的位置。我们称之为“空间试验”，它对评估干预措施在不存在自然集群的地方的效果有潜在的好处。例如，如果一个城市在城市的许多地方推广新的威尔斯井，我们的工作主要是统计，包括分析不同的统计模型如何在随机研究设计中工作。为了能够使用这些新方法，我们将制作在标准统计软件包中运行的软件，并提供详细的文件和实例，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”干预措施的效果。我们的工作将有助于设计这些干预措施的推广方式，以便能够可靠地衡量其效果。然而，任何设计对某一地区进行干预研究的学术领域都将受益，包括农业，经济学和生态学。

项目成果

An individual randomised efficacy trial of autologous blood products, leukocyte and platelet-rich fibrin (L-PRF), to promote ulcer healing in leprosy in Nepal: the TABLE trial protocol.

• DOI: 10.1186/s13063-021-05392-5
• 发表时间: 2021-07-15
• 期刊: Trials
• 影响因子: 2.5
• 作者: Napit IB;Shrestha D;Bishop J;Choudhury S;Dulal S;Gill P;Gkini E;Gwyther H;Hagge DA;Neupane K;Sartori J;Slinn G;Watson SI;Lilford R
• 通讯作者: Lilford R

Optimal Study Designs for Cluster Randomised Trials: An Overview of Methods and Results

整群随机试验的最佳研究设计：方法和结果概述

• DOI: 10.48550/arxiv.2303.07953
• 发表时间: 2023
• 作者: Watson S

Low cost and real-time surveillance of enteric infection and diarrhoeal disease using rapid diagnostic tests: A pilot study

使用快速诊断测试对肠道感染和腹泻病进行低成本实时监测：一项试点研究

• DOI: 10.1101/2022.03.07.22271752
• 发表时间: 2022
• 作者: Watson S

Efficient design of geographically-defined clusters with spatial autocorrelation.

• DOI: 10.1080/02664763.2021.1941807
• 发表时间: 2022
• 期刊: JOURNAL OF APPLIED STATISTICS
• 影响因子: 1.5
• 作者: Watson, Samuel, I

Evaluations of water, sanitation and hygiene interventions should not use diarrhoea as (primary) outcome.

• DOI: 10.1136/bmjgh-2022-008521
• 发表时间: 2022-05
• 期刊: BMJ GLOBAL HEALTH
• 影响因子: 8.1
• 作者: Watson, Samuel, I;Rego, Ryan T. T.;Hofer, Timothy;Lilford, Richard J.
• 通讯作者: Lilford, Richard J.