Impact of preferential sampling on exposure prediction and health effect inference in the context of air pollution epidemiology.

ABSTRACT: 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 Unite

SUBMITTER: Lee A 

PROVIDER: S-EPMC5863931 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature