Preferential sampling has been defined in the context of geostatistical modeling as the dependence between the sampling locations and the process that describes the spatial structure of the data. It can occur when networks are designed to find high values. For example, in networks based on the U.S. Clean Air Act monitors are sited to determine whether air quality standards are exceeded. We study the impact of the design of monitor networks in the context of air pollution epidemiology studies. The effect of preferential sampling has been illustrated in the literature by highlighting its impact on spatial predictions. In this paper, we use these predictions as input in a second stage analysis, and we assess how they affect health effect inference. Our work is motivated by data from two United States regulatory networks and health data from the Multi-Ethnic Study of Atherosclerosis and Air Pollution. The two networks were designed to monitor air pollution in urban and rural areas respectively, and we found that the health analysis results based on the two networks can lead to different scientific conclusions. We use preferential sampling to gain insight into these differences. We designed a simulation study, and found that the validity and reliability of the health effect estimate can be greatly affected by how we sample the monitor locations. To better understand its effect on second stage inference, we identify two components of preferential sampling that shed light on how preferential sampling alters the properties of the health effect estimate.

中文翻译：

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空气污染流行病学背景下优先采样对暴露预测和健康效应推断的影响

在地质统计建模的背景下，优先采样被定义为采样位置与描述数据空间结构的过程之间的依赖关系。当网络旨在寻找高值时，就会发生这种情况。例如，在基于美国清洁空气法案的网络中，安装了监测器以确定空气质量是否超出标准。我们在空气污染流行病学研究的背景下研究了监测网络设计的影响。文献中已经通过强调其对空间预测的影响来说明优先采样的影响。在本文中，我们将这些预测用作第二阶段分析的输入，并评估它们如何影响健康影响推断。我们的工作受到来自两个美国监管网络的数据和来自动脉粥样硬化和空气污染多种族研究的健康数据的启发。这两个网络分别用于监测城市和农村地区的空气污染，我们发现基于这两个网络的健康分析结果可以得出不同的科学结论。我们使用优先抽样来深入了解这些差异。我们设计了一项模拟研究，发现健康效应估计的有效性和可靠性会受到我们如何对监测器位置进行采样的影响。为了更好地理解它对第二阶段推理的影响，我们确定了优先采样的两个组成部分，它们阐明了优先采样如何改变健康效应估计的属性。