Biostatistics Advance Access originally published online on October 16, 2008
Biostatistics 2009 10(2):258-274; doi:10.1093/biostatistics/kxn033
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Measurement error caused by spatial misalignment in environmental epidemiology
Department of Biostatistics, Harvard University, Boston, MA 02115, USA alexandros{at}post.harvard.edu
Institute for the Environment, Brunel University, Uxbridge, Middlesex UB8 3PH, UK
Department of Environmental Health, Harvard University, Boston, MA 02115, USA
Department of Biostatistics, Harvard University, Boston, MA 02115, USA
* To whom correspondence should be addressed.
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.
Keywords: Air pollution; Measurement error; Predictions; Spatial misalignment
Received December 18, 2007; revised August 13, 2008; accepted for publication September 4, 2008.