Work

Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures

Year: 2015

Type: article

Abstract: Spatial modeling of air pollution exposures is widespread in air pollution epidemiology research as a way to improve exposure assessment. However, there are key sources of exposure model uncertainty when air pollution is modeled, including estimation error and model misspecification. We examine the use of predicted air pollution levels in linear health effect models under a measurement error framework. For the prediction of air pollution exposures, we consider a universal Kriging framework, which may include land-use regression terms in the mean function and a spatial covariance structure for the residuals. We derive the bias induced by estimation error and by model misspecification in the exposure model, and we find that a misspecified exposure model can induce asymptotic bias in the effect estimate of air pollution on health. We propose a new spatial simulation extrapolation (SIMEX) procedure, and we demonstrate that the procedure has good performance in correcting this asymptotic bias. We illustrate spatial SIMEX in a study of air pollution and birthweight in Massachusetts.

Source: Biostatistics

DOI: 10.1093/biostatistics/kxv048

Authors: Stacey E. Alexeeff, Raymond J. Carroll, Brent Coull

Institutions: NSF National Center for Atmospheric Research, Texas A&M University, Harvard University

Language: English

Topic: Statistical Methods in Epidemiology

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: U.S. Environmental Protection Agency, National Institutes of Health, National Cancer Institute, National Institute of Environmental Health Sciences