Efficient Measurement Error Correction with Spatially Misaligned Data

Details

• Personal Author:
  Lumley T ; Sheppard L ; Szpiro AA
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Environmental Exposure Epidemiology Occupational Health Safety
• Keywords:
  Author Keywords: Environmental Epidemiology Biostatistics Environmental Health Monitoring Environmental Statistics Exposure Levels Exposure Modeling Kriging Measurement Error Statistical Quality Control
• ISSN:
  1465-4644
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  OSHA Region 10 ; Washington
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  12
• Issue:
  4
• NIOSHTIC Number:
  nn:20054751
• Citation:
  Biostatistics 2011 Oct; 12(4):610-623
• Contact Point Address:
  Adam A. Szpiro, Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
• Email:
  aszpiro@u.washington.edu
• Federal Fiscal Year:
  2012
• Performing Organization:
  University of Washington
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Biostatistics
• End Date:
  20250630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:f857bb9b638a786a38b9e1bd0eea284ec82fa8df6d9449d21dd0d5d83b91a5ca459698460241b075f3243789635ac6e24431cd9bbd7d7520234e9f106743f13a
• Download URL:
  https://stacks.cdc.gov/view/cdc/217187/cdc_217187_DS1.pdf
• File Type:
  [PDF - 252.57 KB ]