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Assessing spatial confounding in cancer disease mapping using R.

Douglas R M Azevedo1, Dipankar Bandyopadhyay2, Marcos O Prates1

  • 1Dept. of Statistics, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.

Cancer Reports (Hoboken, N.J.)
|July 29, 2020
PubMed
Summary
This summary is machine-generated.

This study addresses spatial confounding in cancer disease mapping by introducing three methods to correct biased results. The R package RASCO is provided to help researchers mitigate these issues for more accurate cancer epidemiology insights.

Keywords:
Bayesian inferenceRASCOareal modelingintegrated nested Laplace approximationspatial confoundingvariance inflation

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Geographic Information Systems (GIS)

Background:

  • Spatial patterns are crucial for understanding cancer disease progression.
  • Spatial confounding, a correlation between spatial effects and covariates, can lead to misinterpretations in cancer epidemiology.
  • Accurate spatial analysis is essential for effective public health interventions.

Purpose of the Study:

  • To introduce and illustrate three methods (RHZ, HH, SPOCK) for tackling spatial confounding in cancer research.
  • To demonstrate the implementation of these methods using the R statistical software.
  • To provide a practical tool for researchers to improve spatial disease mapping models.

Main Methods:

  • Restricted spatial regressions were employed, involving projection onto orthogonal covariate spaces or spatial location displacement.
  • Parametric count data models (Poisson, generalized Poisson, negative binomial) were used for areal counts.
  • The conditional autoregressive (CAR) model quantified spatial association, with Bayesian inference often accelerated by integrated nested Laplace approximation (INLA).

Main Results:

  • All three introduced methods effectively reduced bias and variance inflation in spatial models.
  • The study confirmed that ignoring spatial confounding can lead to erroneous conclusions in cancer research.
  • The developed methods provide more reliable estimates for spatial disease mapping.

Conclusions:

  • Spatial confounding remains a significant challenge in spatial disease mapping, necessitating careful investigation and mitigation.
  • The R package RASCO offers a valuable resource for fitting various spatial models and their restricted versions.
  • Implementing these approaches enhances the precision of inference in cancer epidemiology.