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Spatial+: A novel approach to spatial confounding.

Emiko Dupont1, Simon N Wood2, Nicole H Augustin2

  • 1Department of Mathematical Sciences, University of Bath, Bath, UK.

Biometrics
|March 8, 2022
PubMed
Summary
This summary is machine-generated.

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Spatial confounding in regression models biases estimates. The novel spatial+ method addresses this by regressing spatial dependence away from covariates, improving tree health modeling accuracy.

Area of Science:

  • Environmental Science
  • Statistical Modeling
  • Forestry Science

Background:

  • Spatial regression models can suffer from spatial confounding, where collinearity between covariates and spatial effects biases estimates.
  • This issue complicates reliable inference, particularly in forestry, when assessing factors like temperature's impact on tree health.

Purpose of the Study:

  • To introduce a novel approach, spatial+, to mitigate spatial confounding in regression models.
  • To address bias in covariate effect estimates when the covariate is spatially dependent but not solely determined by location.

Main Methods:

  • The spatial+ approach utilizes a thin plate spline formulation.
  • It involves regressing spatial dependence away from the covariate of interest, replacing the covariate with its residuals.
Keywords:
bias reductioncollinearityforestrypartial thin plate spline regressionspatial confounding

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  • Asymptotic analysis and simulation studies are employed to evaluate the method.

    • Main Results:

    • Spatial+ effectively reduces the sensitivity of covariate effect estimates to spatial smoothing bias.
    • Asymptotic analysis confirms that spatial+ avoids the bias issues inherent in traditional spatial models.
    • Simulation studies validate the effectiveness of the spatial+ method.

    Conclusions:

    • Spatial+ offers a robust solution for spatial confounding, enhancing the reliability of regression models.
    • The method is easily implementable with existing software and compatible with standard model selection criteria.
    • A key advantage is its extension to non-Gaussian response distributions and adaptability to various spatial model formulations.