Measurement error caused by spatial misalignment in environmental epidemiology.

ABSTRACT: In many environmental epidemiology studies, the locations and/or times of exposure measurements and health assessments do not match. In such settings, health effects analyses often use the predictions from an exposure model as a covariate in a regression model. Such exposure predictions contain some measurement error as the predicted values do not equal the true exposures. We provide a framework for spatial measurement error modeling, showing that smoothing induces a Berkson-type measurement error with nondiagonal error structure. From this viewpoint, we review the existing approaches to estimation in a linear regression health model, including direct use of the spatial predictions and exposure simulation, and explore some modified approaches, including Bayesian models and out-of-sample re

SUBMITTER: Gryparis A 

PROVIDER: S-EPMC2733173 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature