Design and Inference for Hybrid Ecological Studies

描述（由申请人提供）：生态学研究可以定义为在组水平上检查协会。它们之所以吸引人，是因为它们利用了常规可用的数据，并且由于人口众多和广泛的暴露对比，还提供了高功效的潜力。然而，他们也容易受到一系列关于个人层面协会的偏见，统称为生态偏见，并可能导致生态谬误。在流行病学中，根本的困难是生态数据无法描述暴露和混杂因素的组内变异性。这导致无法控制混杂，以及个体水平模型的一般不可识别性。生态推理问题的唯一解决方案是补充生态数据与个人水平的样本，在这个建议中，我们描述和开发了各种各样的混合动力研究，追求这种解决方案。具体来说，我们开发了一种混合设计，其中的病例对照研究嵌入在生态研究。直观的吸引力是，个人层面的数据提供了控制偏差的基础，而生态数据提供了效率增益。此外，我们扩展现有的方法，包括聚集数据设计和两阶段方法，生态环境。这将基于这些设计的贝叶斯方法的开发，尚未进行探索。此外，我们将比较各种方法在各种数据/采样场景中的性能。一个关键的研究问题是，群体层面的数据是否为个人的收集提供了有用的信息。我们将探索最佳的研究设计，从多少个人的样本和从哪些组。两个癌症数据集和一个流感数据集的方法进行说明。

DESCRIPTION (provided by applicant): Ecological studies may be defined examining associations at the group level. They are appealing in that they make use of routinely available data, and also offer the potential of high power due to large populations and broad exposure contrasts. However, they are also susceptible to a range of biases with respect to individual-level associations, collectively termed ecological bias, and may lead to the ecological fallacy. In epidemiology, the fundamental difficulty is the inability of ecological data to characterize within-group variability in exposures and confounders. This results in an inability to control for confounding, and general non-identifiability of the individual-level model. The only solution to the ecological inference problem is to supplement ecological data with individual-level samples; in this proposal we describe and develop a variety of hybrid studies that pursue this solution. Specifically, we develop a hybrid design in which a case-control study is embedded within an ecological study. The intuitive appeal is that the individual-level data provide the basis for the control of bias, while the ecological data provide efficiency gains. In addition, we extend current methods, including the aggregate data design and two-phase method, to the ecological setting. This will be based on the development of Bayesian methods for these designs, which have not been explored. Further, we will compare performance of the various methods in a variety of data/sampling scenarios. A key research question is whether the group-level data provide useful information for the collection of individuals. We will explore optimal study design in terms of how many individuals to sample and from which groups. The methods are illustrated with two cancer data sets and one influenza data set.