Statistical causal modelling approaches to understanding the developmental profiles of asthma and allergy

了解哮喘和过敏发展概况的统计因果模型方法

• 批准号: 2136142
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2136142
• 关键词: Statistical; causal; modelling; approaches; understanding

The past decade has seen rapid advances in big data mining, with clustering of longitudinal data used to identify a variety of developmental profiles of asthma as well as to disaggregate allergic sensitisation. This project will aim to further the progress in this area by moving beyond current modelling approaches and focusing on data integration across different variables, from patient reported outcomes to blood biomarkers and lung function measurements.Main data sources for this project will be five population based birth cohorts that form part of the Study Team for Early Life Asthma Research (STELAR) network, which is described in detail elsewhere. Collectively, these cohorts provide data for > 14,000 children and have similar data structures that will facilitate analysis. At each time point, validated questionnaires were used for gathering information on the occurrence and frequency of wheezing, atopic eczema and rhinitis. IgE responses to different allergen components and various lung function measures were also recorded at some of the time points.A major goal of the project will be the discovery of clinically relevant phenotypes that are homogeneous and share underlying pathophysiological mechanisms. While previous studies have used data-driven approaches to derive phenotypic clusters, there is a pressing need to refine these by integrating a broader range of the data available to us, and make sure they relate to outcomes and demonstrate clinical utility. The project will aim to replicate findings across birth cohorts, and potentially verify them via disparate lines of evidence such as data from neonatal mouse models in later stages.

过去十年，大数据挖掘取得了快速进展，纵向数据聚类用于识别哮喘的各种发育特征以及分解过敏致敏。该项目旨在通过超越当前的建模方法，专注于不同变量之间的数据整合，从患者报告的结果到血液生物标志物和肺功能测量，进一步推动这一领域的进展。该项目的主要数据来源将是构成早期生命哮喘研究小组（STELAR）网络一部分的五个基于人口的出生队列，该网络在其他地方有详细描述。总的来说，这些队列提供了14000名儿童的数据，并且具有类似的数据结构，有助于分析。在每个时间点，使用有效的问卷收集有关喘息，特应性湿疹和鼻炎的发生和频率的信息。在一些时间点，还记录了不同过敏原成分的IgE反应和各种肺功能测量。该项目的一个主要目标将是发现临床相关的表型，这些表型是同质的，并共享潜在的病理生理机制。虽然以前的研究已经使用数据驱动的方法来推导表型簇，但迫切需要通过整合我们可用的更广泛的数据来完善这些方法，并确保它们与结果相关并证明临床实用性。该项目的目标是在出生队列中复制这些发现，并可能通过不同的证据线来验证它们，比如后期新生小鼠模型的数据。