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Accounting for spatial confounding in epidemiological studies with individual‐level exposures: An exposure‐penalized spline approach

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  • Jennifer F. Bobb
  • Maricela F. Cruz
  • Stephen J. Mooney
  • Adam Drewnowski
  • David Arterburn
  • Andrea J. Cook

Abstract

In the presence of unmeasured spatial confounding, spatial models may actually increase (rather than decrease) bias, leading to uncertainty as to how they should be applied in practice. We evaluated spatial modelling approaches through simulation and application to a big data electronic health record study. Whereas the risk of bias was high for purely spatial exposures (e.g. built environment), we found very limited potential for increased bias for individual‐level exposures that cluster spatially (e.g. smoking status). We also proposed a novel exposure‐penalized spline approach that selects the degree of spatial smoothing to explain spatial variability in the exposure. This approach appeared promising for efficiently reducing spatial confounding bias.

Suggested Citation

  • Jennifer F. Bobb & Maricela F. Cruz & Stephen J. Mooney & Adam Drewnowski & David Arterburn & Andrea J. Cook, 2022. "Accounting for spatial confounding in epidemiological studies with individual‐level exposures: An exposure‐penalized spline approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1271-1293, July.
    • Handle: RePEc:bla:jorssa:v:185:y:2022:i:3:p:1271-1293
    DOI: 10.1111/rssa.12831
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    References listed on IDEAS

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    1. Abhirup Datta & Sudipto Banerjee & Andrew O. Finley & Alan E. Gelfand, 2016. "Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 800-812, April.
    2. Matthew J. Heaton & Abhirup Datta & Andrew O. Finley & Reinhard Furrer & Joseph Guinness & Rajarshi Guhaniyogi & Florian Gerber & Robert B. Gramacy & Dorit Hammerling & Matthias Katzfuss & Finn Lindgr, 2019. "A Case Study Competition Among Methods for Analyzing Large Spatial Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 398-425, September.
    3. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
    4. Chi Wang & Giovanni Parmigiani & Francesca Dominici, 2012. "Bayesian Effect Estimation Accounting for Adjustment Uncertainty," Biometrics, The International Biometric Society, vol. 68(3), pages 661-671, September.
    5. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
    6. Bader, M.D.M. & Mooney, S.J. & Rundle, A.G., 2016. "Protecting personally identifiable information when using online geographic tools for public health research," American Journal of Public Health, American Public Health Association, vol. 106(2), pages 206-208.
    7. Garritt L. Page & Yajun Liu & Zhuoqiong He & Donchu Sun, 2017. "Estimation and Prediction in the Presence of Spatial Confounding for Spatial Linear Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(3), pages 780-797, September.
    8. John Hughes & Murali Haran, 2013. "Dimension reduction and alleviation of confounding for spatial generalized linear mixed models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 139-159, January.
    9. Brian J. Reich & James S. Hodges & Vesna Zadnik, 2006. "Effects of Residual Smoothing on the Posterior of the Fixed Effects in Disease-Mapping Models," Biometrics, The International Biometric Society, vol. 62(4), pages 1197-1206, December.
    10. Ephraim M. Hanks & Erin M. Schliep & Mevin B. Hooten & Jennifer A. Hoeting, 2015. "Restricted spatial regression in practice: geostatistical models, confounding, and robustness under model misspecification," Environmetrics, John Wiley & Sons, Ltd., vol. 26(4), pages 243-254, June.
    11. Hauke Thaden & Thomas Kneib, 2018. "Structural Equation Models for Dealing With Spatial Confounding," The American Statistician, Taylor & Francis Journals, vol. 72(3), pages 239-252, July.
    12. Chi Wang & Giovanni Parmigiani & Francesca Dominici, 2012. "Rejoinder: Bayesian Effect Estimation Accounting for Adjustment Uncertainty," Biometrics, The International Biometric Society, vol. 68(3), pages 680-686, September.
    13. Hodges, James S. & Reich, Brian J., 2010. "Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love," The American Statistician, American Statistical Association, vol. 64(4), pages 325-334.
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