Statistical methods for informative and outcome-dependent data

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。当存在完整的数据（例如，完整的队列）但有些数据未被观察到时，这被视为数据缺失问题。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称。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，观察机制都具有信息性，因为我们如何以及是否观察数据与结果本身有关。虽然信息性通常是一个需要考虑的挑战，但同样的原则可以被利用为一个人的优势：结果依赖的抽样设计通过设计来提高效率，从而提高简单随机抽样的效率——就像在经典的病例对照研究中一样。信息和结果依赖的观察机制带来了一些分析挑战和机遇。该计划的长期目标是调查和开发在信息或结果依赖的观察机制下建模数据的统计工具。在短期内，该计划将开发受信息观察机制影响的数据估计方法或通过结果相关抽样设计收集的数据，提出新颖的结果相关设计，以便在数据受信息簇大小或访问过程影响时进行有效估计，并在现代机器学习分析中利用结果相关设计的逻辑。该计划将通过开发一个统计工具箱来分析混乱和信息丰富的观察数据，从而为该领域做出贡献，这些数据在理想场景之外太常见了。此外，所提出的方法的动机是在流行病学、公共卫生等领域的应用，所提出的研究将产生许多次要影响。例如，根据信息簇大小提出的数据设计可以使研究人员有效地研究具有多代效应的暴露，这些效应不仅影响暴露者，而且随着人口的繁殖而产生级联效应。分析复杂和信息性访问过程的数据的方法将使科学家能够通过电子健康记录数据库以有原则的方式调查复杂疾病之间的关系。这项研究的一个关键优先事项是支持和培养9名学生成为全面发展的统计学家和思想家，他们每个人都将为项目目标做出重大的创造性贡献。在这样做的过程中，他们不仅将获得尖端统计工具和如何进行方法学研究方面的专业知识，还将学会向同事和非统计学家利益相关者清楚地传达统计现象——这正是作为独立统计学家取得成功所需要的技能。

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.