期刊
BIOSTATISTICS
卷 12, 期 4, 页码 610-623出版社
OXFORD UNIV PRESS
DOI: 10.1093/biostatistics/kxq083
关键词
Environmental epidemiology; Environmental statistics; Exposure modeling; Kriging; Measurement error
资金
- US Environmental Protection Agency [R831697]
- Assistance Agreement [CR-83407101]
- National Institute of Environmental Health Sciences [R01-ES009411, P50 ES051915]
- EPA [R831697, 908987] Funding Source: Federal RePORTER
Association studies in environmental statistics often involve exposure and outcome data that are misaligned in space. A common strategy is to employ a spatial model such as universal kriging to predict exposures at locations with outcome data and then estimate a regression parameter of interest using the predicted exposures. This results in measurement error because the predicted exposures do not correspond exactly to the true values. We characterize the measurement error by decomposing it into Berkson-like and classical-like components. One correction approach is the parametric bootstrap, which is effective but computationally intensive since it requires solving a nonlinear optimization problem for the exposure model parameters in each bootstrap sample. We propose a less computationally intensive alternative termed the parameter bootstrap that only requires solving one nonlinear optimization problem, and we also compare bootstrap methods to other recently proposed methods. We illustrate our methodology in simulations and with publicly available data from the Environmental Protection Agency.
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