Observational epidemiological studies analyzed via spatial regression methods are often affected by spatial confounding. We formulate an adjustment strategy based on spectral methods, by considering eigendecomposed conditional autoregressive models. We model exposure and confounding variable using different commonly used spatial models, to study the extent to which bias due to spatial confounding can be adjusted under different specifications.
Ventrucci, M., Page, G.L. (2022). Adjusting for spatial confounding using eigendecomposed CAR models. Nicola Torelli, Ruggero Bellio, Vito Muggeo.
Adjusting for spatial confounding using eigendecomposed CAR models
Ventrucci, Massimo
Primo
;
2022
Abstract
Observational epidemiological studies analyzed via spatial regression methods are often affected by spatial confounding. We formulate an adjustment strategy based on spectral methods, by considering eigendecomposed conditional autoregressive models. We model exposure and confounding variable using different commonly used spatial models, to study the extent to which bias due to spatial confounding can be adjusted under different specifications.File in questo prodotto:
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