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AuthorAzevedo, Douglas R. M.
AuthorBandyopadhyay, Dipankar
AuthorPrates, Marcos O.
AuthorAbdel-Salam, Abdel-Salam G.
AuthorGarcia, Dina
Available date2023-11-29T10:06:02Z
Publication Date2020
Publication NameCancer Reports
ResourceScopus
ISSN25738348
URIhttp://dx.doi.org/10.1002/cnr2.1263
URIhttp://hdl.handle.net/10576/49805
AbstractBackground: Exploring spatial patterns in the context of cancer disease mapping (DM) is a decisive approach to bring evidence of geographical tendencies in assessing disease status and progression. However, this framework is not insulated from spatial confounding, a topic of significant interest in cancer epidemiology, where the latent correlation between the spatial random effects and fixed effects (such as covariates), often lead to misleading interpretation. Aims: To introduce three popular approaches (RHZ, HH and SPOCK; details in paper) often employed to tackle spatial confounding, and illustrate their implementation in cancer research via the popular statistical software R. Methods: As a solution to alleviate spatial confounding, restricted spatial regressions are constructed by either projecting the latent effect onto the orthogonal space of covariates, or by displacing the spatial locations. Popular parametric count data models, such as the Poisson, generalized Poisson and negative binomial, were considered for the areal count responses, while the spatial association is quantified via the conditional autoregressive (CAR) model. Our method of inference in Bayesian, sometimes aided by the integrated nested Laplace approximation (INLA) to accelerate computing. The methods are implemented in the R package RASCO available from the first author's GitHub page. Results: The results reveal that all three methods perform well in alleviating the bias and variance inflation present in the spatial models. The effects of spatial confounding were also explored, which, if ignored in practice, may lead to wrong conclusions. Conclusion: Spatial confounding continues to remain a critical bottleneck in deriving precise inference from spatial DM models. Hence, its effects must be investigated, and mitigated. Several approaches are available in the literature, and they produce trustworthy results. The central contribution of this paper is providing the practitioners the R package RASCO, capable of fitting a large number of spatial models, as well as their restricted versions.
SponsorBandyopadhyay acknowledges funding support from the VCU Massey Cancer Center Biostatistics Shared Resources, supported, in part, with funding from NIH-NCI Cancer Center Support Grant P30 CA016059. Prates acknowledges partial funding support from CNPq grants 436948/2018-4 and 307547/2018-4 and FAPEMIG grant PPM-00532-16.
Languageen
PublisherBlackwell Publishing Ltd
Subjectareal modeling
Bayesian inference
integrated nested Laplace approximation
RASCO
spatial confounding
variance inflation
TitleAssessing spatial confounding in cancer disease mapping using R
TypeArticle
Issue Number4
Volume Number3
dc.accessType Abstract Only


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