Joint Modeling of Multiple Data Sources

多数据源联合建模

• 批准号: RGPIN-2019-04404
• 负责人: Béliveau, Audrey
• 金额: $ 1.17万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=737609
• 关键词: Joint; Modeling; Multiple; Data; Sources

In recent years, joint statistical analyses of sources of data have gained traction in numerous fields of application, including animal population dynamics and human population health. This research program aims to advance the statistical methodology and applications for the joint modeling of multiple data sources. The first objective (O1) of this research is to address unexplored issues in integrated population modeling for the study of animal population dynamics. My team and I will develop continuous time integrated population models and address the questions of how to optimally allocate survey effort between the different surveys, how to deal with conflicting evidence between data sources and other assumptions breakdown. The second objective (O2) is to develop novel statistical methodologies for the analysis of disconnected networks in network meta-analysis where the purpose is to synthesize evidence across randomized studies to assess the relative efficacy or safety of treatments. With standard methods, the treatment effects between disconnected treatments cannot be estimated. To achieve this, my team and I will develop new modelling strategies including parametric and non parametric models for study-specific baseline treatment effects and a joint model that will use correlation between outcomes. The third objective (O3) is to develop statistical methodologies for the joint modeling of data sources in two new application areas. Specifically, my team and I will develop a novel methodology for estimating the size of yearly smolt runs when some of the daily data is missing due to trap failures on some days. Information will be shared across years through the use of rainfall covariates. We will also develop a novel methodology for estimating the size of homeless populations when the data is collected through a plant-capture scheme, and planted homeless may be unsure whether they were counted by the survey or not. We will develop a new model that shares evidence across multiple cities. In addition to addressing several important methodological issues for the joint analysis of multiple data sources, this research will allow, for the first time, to make important connections between the various literatures on joint analysis methods and highlight differences. By making the methodology readily available through software implementations, this work is expected to significantly promote more complex and realistic joint analyses in the natural sciences. The applications of the novel methodologies will have real world impact by influencing policymaking in Canada and worldwide, from wildlife management and conservation to drug review and more. The knowledge gained from this research will be crucial for conservation managers to understand and react to the effects of climate change. This research will undertake considerable training of HQP (8 students) which will significantly contribute to capacity building in scientific and statistical modeling in Canada.

近年来，对数据来源的联合统计分析在包括动物种群动态和人类种群健康在内的许多应用领域取得了进展。本研究项目旨在推进多数据源联合建模的统计方法和应用。本研究的第一个目标(1)是解决动物种群动态研究中综合种群建模中尚未探索的问题。我和我的团队将开发连续时间整合的人口模型，并解决如何在不同的调查之间优化分配调查工作的问题，如何处理数据源之间相互矛盾的证据和其他假设崩溃。第二个目标（O2）是开发新的统计方法，用于分析网络荟萃分析中的断开网络，其目的是综合随机研究中的证据，以评估治疗的相对有效性或安全性。使用标准方法，无法估计不相关处理之间的处理效果。为了实现这一目标，我和我的团队将开发新的建模策略，包括用于研究特定基线治疗效果的参数和非参数模型，以及将使用结果之间相关性的联合模型。第三个目标（O3）是为两个新应用领域的数据源联合建模开发统计方法。具体来说，我和我的团队将开发一种新的方法，用于在某些日子由于陷阱故障而丢失一些日常数据时估计每年小鲑鱼的数量。通过使用降雨协变量，信息将在不同年份共享。我们还将开发一种新的方法，当数据通过植物捕获计划收集时，估计无家可归人口的规模，而种植的无家可归者可能不确定他们是否被调查计算在内。我们将开发一种新模式，在多个城市共享证据。除了解决多个数据源联合分析的几个重要方法论问题外，本研究还将首次在联合分析方法的各种文献之间建立重要联系，并突出差异。通过软件实现使方法易于获得，这项工作有望显著促进自然科学中更复杂和更现实的联合分析。新方法的应用将通过影响加拿大和全世界的政策制定，从野生动物管理和保护到药物审查等等，对现实世界产生影响。从这项研究中获得的知识将对保护管理者理解和应对气候变化的影响至关重要。这项研究将对HQP（8名学生）进行大量培训，这将大大有助于加拿大科学和统计建模的能力建设。