Journal
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS
Volume 65, Issue 5, Pages 731-753Publisher
OXFORD UNIV PRESS
DOI: 10.1111/rssc.12144
Keywords
Air pollution epidemiology; Measurement error bias; Multipollutant measurement; Penalized regression splines
Categories
Funding
- Intramural research program' of the National Institutes of Health, National Institute of Environmental Health Sciences [Z01-ES044005]
- National Institute of Environmental Health Science [R01-ES009411, P50-ES015915, R01-ES020871, K24-ES013195, P30-ES07033, T32-ES015459]
- US Environmental Protection Agency [R831697, RD-83479601]
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Air pollution epidemiology studies are trending towards a multipollutant approach. In these studies, exposures at subject locations are unobserved and must be predicted by using observed exposures at misaligned monitoring locations. This induces measurement error, which can bias the estimated health effects and affect standard error estimates. We characterize this measurement error and develop an analytic bias correction when using penalized regression splines to predict exposure. Our simulations show that bias from multipollutant measurement error can be severe, and in opposite directions or simultaneously positive or negative. Our analytic bias correction combined with a non-parametric bootstrap yields accurate coverage of 95% confidence intervals. We apply our methodology to analyse the association of systolic blood pressure with PM2.5 and NO2 levels in the National Institute of Environmental Health Sciences Sister Study. We find that NO2 confounds the association of systolic blood pressure with PM2.5 levels and vice versa. Elevated systolic blood pressure was significantly associated with increased PM2.5 and decreased NO2 levels. Correcting for measurement error bias strengthened these associations and widened 95% confidence intervals.
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