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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/6458

Title: Measurement error caused by spatial misalignment in environmental epidemiology
Authors: Gryparis, A
Paciorek, CJ
Zeka, A
Schwartz, J
Coull, BA
Keywords: Air pollution
Measurement error
Predictions
Spatial misalignment
Publication Date: 2009
Publisher: Oxford University Press
Citation: BioStatistics 10(2): 258 - 274, Apr 2009
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 regression calibration, motivated by measurement error principles. We then extend this work to the generalized linear model framework for health outcomes. Based on analytical considerations and simulation results, we compare the performance of all these approaches under several spatial models for exposure. Our comparisons underscore several important points. First, exposure simulation can perform very poorly under certain realistic scenarios. Second, the relative performance of the different methods depends on the nature of the underlying exposure surface. Third, traditional measurement error concepts can help to explain the relative practical performance of the different methods. We apply the methods to data on the association between levels of particulate matter and birth weight in the greater Boston area.
Description: Copyright @ 2009 Gryparis et al - Published by Oxford University Press.
Sponsorship: This research was supported by NIEHS grants ES012044 (AG, BAC), ES009825 (JS, BAC), ES007142 (CJP), and ES000002 (CJP), and EPA grant R-832416 (JS, BAC).
URI: http://biostatistics.oxfordjournals.org/content/10/2/258
http://bura.brunel.ac.uk/handle/2438/6458
DOI: http://dx.doi.org/10.1093/biostatistics/kxn033
ISSN: 1465-4644
Appears in Collections:Institute for the Environment Research Papers
Health
Environment
Publications

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