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MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models

Author

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  • Douglas R. M. Azevedo

    (Universidade Federal de Minas Gerais)

  • Marcos O. Prates

    (Universidade Federal de Minas Gerais)

  • Dipankar Bandyopadhyay

    (Virginia Commonwealth University)

Abstract

Exploring spatial patterns in the context of disease mapping is a decisive approach to bring evidence of geographical tendencies in assessing disease status and progression. In most cases, multiple count responses (corresponding to disease incidences of multiple types, such as cancer in men and women) are recorded at each spatial location, which may exhibit similar spatial patterns in addition to disease-specific patterns. These are typically modeled using multivariate shared component models, where the spatial (random) effects may be shared between the disease types to model their association. However, this framework is not immune to spatial confounding, where the latent correlation between the spatial random effects and the fixed effects often leads to misleading interpretation. A recent approach to attenuate spatial confounding is the “SPatial Orthogonal Centroid ‘K’orrection”, aka SPOCK, which displaces the geographical centroids, ensuring orthogonality of the spatial random effects and the fixed effects. In this paper, we introduce MSPOCK, or Multiple SPOCK, to tackle spatial confounding for the multiple counts scenario. The methodology is evaluated on synthetic data, and illustrated via an application to new cases of respiratory system cancer for men and women for the US state of California in 2016. Our studies show that the MSPOCK correction leads to a reduction of the posterior variance estimates of model parameters, while maintaining the interpretation of the model.

Suggested Citation

  • Douglas R. M. Azevedo & Marcos O. Prates & Dipankar Bandyopadhyay, 2021. "MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 464-491, September.
    • Handle: RePEc:spr:jagbes:v:26:y:2021:i:3:d:10.1007_s13253-021-00451-5
    DOI: 10.1007/s13253-021-00451-5
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    1. Carlos García & Zaida Quiroz & Marcos Prates, 2023. "Bayesian spatial quantile modeling applied to the incidence of extreme poverty in Lima–Peru," Computational Statistics, Springer, vol. 38(2), pages 603-621, June.
    2. Marcos O. Prates & Douglas R. M. Azevedo & Ying C. MacNab & Michael R. Willig, 2022. "Non‐separable spatio‐temporal models via transformed multivariate Gaussian Markov random fields," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1116-1136, November.

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