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Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
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Efficient measurement error correction with spatially misaligned data.

Adam A Szpiro1, Lianne Sheppard, Thomas Lumley

  • 1Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. aszpiro@u.washington.edu

Biostatistics (Oxford, England)
|January 22, 2011
PubMed
Summary
This summary is machine-generated.

This study addresses spatial misalignment in environmental exposure data by developing a computationally efficient "parameter bootstrap" method to correct for measurement error, improving regression analysis accuracy.

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Area of Science:

  • Environmental statistics
  • Spatial statistics
  • Geostatistics

Background:

  • Environmental association studies frequently encounter exposure and outcome data misaligned in space.
  • Universal kriging is often used to predict exposures at outcome locations, introducing measurement error.
  • This measurement error can be decomposed into Berkson-like and classical-like components.

Purpose of the Study:

  • To develop a computationally efficient method for correcting measurement error in spatial exposure data.
  • To characterize the nature of measurement error arising from spatial misalignment.
  • To compare the proposed parameter bootstrap method with existing techniques.

Main Methods:

  • Characterization of measurement error by decomposition into Berkson-like and classical-like components.
  • Development of a novel
  • parameter bootstrap
  • method as a computationally efficient alternative to the parametric bootstrap.
  • Comparison of bootstrap methods with other recently proposed techniques for handling spatial misalignment.

Main Results:

  • The parameter bootstrap method significantly reduces computational intensity compared to the parametric bootstrap.
  • The proposed method effectively corrects for measurement error introduced by spatial misalignment.
  • Simulations and analysis of Environmental Protection Agency data demonstrate the methodology's utility.

Conclusions:

  • The parameter bootstrap offers a practical and efficient solution for addressing measurement error in spatially misaligned environmental data.
  • Accurate exposure assessment is crucial for reliable association studies.
  • This methodology enhances the precision of regression parameter estimation in environmental epidemiology.