Statistical methods for informative and outcome-dependent data

信息性和结果相关数据的统计方法

• 批准号: RGPIN-2022-03068
• 负责人: McGee, Glen
• 金额: $ 1.38万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765779
• 关键词: Statistical; methods; informative; outcome; dependent

In many practical data settings, the way in which we observe data is related to the data themselves. When complete data exist (e.g. a full cohort) but some are unobserved, this is treated as a missing data problem. Elsewhere, when there is no notion of complete data, the phenomenon goes by various names. In cluster-correlated settings when cluster sizes are related to outcomes, this is known as informative cluster size. In longitudinal settings when measurement frequency is related to outcomes, this is known as an informative visit process. In each case, the observation mechanism is informative in that how and whether we observe data is related to the outcomes themselves. While informativeness is typically a challenge to be accounted for, the same principle can be harnessed to one's advantage: outcome-dependent sampling designs are informative by design to improve efficiency relative to simple random sampling-as in the classic case-control study. Informative and outcome-dependent observation mechanisms pose several analysis challenges as well as opportunities. The long-term goal of this program is to investigate and develop statistical tools for modelling data under informative or outcome-dependent observation mechanisms. In the near term, this program will develop estimation methods for data subject to informative observation mechanisms or collected via outcome-dependent sampling designs, propose novel outcome-dependent designs for efficient estimation when data are subject to informative cluster size or visit processes, and leverage the logic of outcome-dependent designs in modern machine learning analyses. This program will contribute to the field by developing a statistical toolbox for analyzing the messy and informatively observed data that are all too common outside of idealized scenarios. Moreover, the proposed methods are motivated by applications in epidemiology, public health and beyond, and the proposed research will have many secondary effects. For example, proposed designs for data subject to informative cluster size can permit researchers to effectively study exposures with multigenerational effects-ones that not only affect those exposed but have cascading effects as the population reproduces. And methods for analyzing data subject to complex and informative visit processes would empower scientists to investigate relationships between complex diseases via electronic health records databases in a principled way. A key priority of the proposed research will be to support and train nine students to become well-rounded statisticians and thinkers, each of whom will make major creative contributions toward the program goals. In so doing, they will not only gain expertise in cutting-edge statistical tools and in how to conduct methodological research but will learn to clearly communicate statistical phenomena to colleagues and non-statistician stakeholders alike-precisely the skills needed to succeed as independent statisticians.

在许多实际的数据设置中，我们观察数据的方式与数据本身有关。当完整的数据存在（例如，一个完整的队列），但有些是未观察到的，这被视为缺失数据问题。在其他地方，当没有完整数据的概念时，这种现象有各种各样的名字。在集群相关设置中，当集群大小与结果相关时，这被称为信息集群大小。在纵向设置中，当测量频率与结果相关时，这被称为信息访问过程。在每种情况下，观察机制都是信息性的，因为我们如何以及是否观察数据与结果本身有关。虽然信息量通常是一个需要考虑的挑战，但同样的原则也可以被利用起来：结果依赖性抽样设计通过设计提供信息，以提高相对于简单随机抽样的效率，就像经典的病例对照研究一样。信息和结果依赖的观察机制提出了一些分析挑战，也带来了机遇。 该计划的长期目标是调查和开发统计工具，用于在信息或依赖结果的观察机制下对数据进行建模。在短期内，该计划将为受信息性观察机制影响或通过结果依赖性抽样设计收集的数据开发估计方法，当数据受信息性聚类大小或访问过程影响时，提出新的结果依赖性设计以进行有效估计，并在现代机器学习分析中利用结果依赖性设计的逻辑。该计划将通过开发一个统计工具箱来分析理想化场景之外的混乱和信息化观察数据，从而为该领域做出贡献。此外，所提出的方法的动机是在流行病学，公共卫生和其他领域的应用，所提出的研究将有许多副作用。例如，建议的设计数据的信息集群大小可以允许研究人员有效地研究暴露与多代效应，不仅影响那些暴露，但有级联效应的人口繁殖。而分析数据的方法将使科学家能够通过电子健康记录数据库以原则性的方式调查复杂疾病之间的关系。拟议研究的一个关键优先事项将是支持和培训9名学生成为全面的统计学家和思想家，他们每个人都将为实现计划目标做出重大的创造性贡献。在这样做，他们不仅将获得在尖端的统计工具和如何进行方法研究的专业知识，但将学会清楚地传达统计现象的同事和非统计利益相关者一样，正是需要作为独立的统计人员取得成功的技能。