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Accounting for spatial confounding in large scale epidemiological studies

dc.contributor.authorRainey, Maddie J., author
dc.contributor.authorKeller, Kayleigh, advisor
dc.contributor.authorWilson, Ander, committee member
dc.contributor.authorGuan, Yawen, committee member
dc.contributor.authorAnderson, Brooke, committee member
dc.date.accessioned2025-06-02T15:21:29Z
dc.date.available2026-05-28
dc.date.issued2025
dc.description.abstractEpidemiological analyses of environmental risk factors often include spatially-varying exposures and outcomes. Unmeasured, spatially-varying factors can lead to confounding bias in estimates of associations. In this dissertation, I present a comparison of existing and new methods that use thin plate regression splines to mitigate spatial confounding bias for both cross-sectional and longitudinal analyses. I also introduce a metric to quantify the spatial smoothing induced by thin plate regression splines in varying geographic domains. I first investigate cross-sectional data, directly comparing existing approaches based on information criteria and cross-validation metrics and additionally introduce a hybrid method to selection that combines features from multiple existing approaches. Based on a simulation study, I make a recommendation for the best approach for different settings and demonstrate their use in a study of environmental exposures on birth weight in a Colorado cohort. Next, I develop an effective bandwidth metric that quantifies the relationship between spatial splines and the range of implied spatial smoothing. I present an R Shiny application, spconfShiny, that provides a user-friendly platform to compute the metric. spconfShiny can be accessed at https://g2aging.shinyapps.io/spconfShiny/. We illustrate the procedure to compute the effective bandwidth and demonstrate its use for different numbers of spatial splines across England, India, Ireland, Northern Ireland, and the United States. Finally, I extend two cross-sectional methods for spatial confounding adjustment to model longitudinal and time-to-event data. The additional temporal component existing in the data requires an additional selection of which coordinates to use to create thin-plate regression splines basis: the spatial coordinates, temporal coordinates, or both the spatial and temporal coordinates. I demonstrate these methods for mixed models, generalized estimating equation models, and a proportional hazard regression framework. I demonstrate the application of these methods in a study of tropical cyclone wind exposures on preterm birth in a North Carolina cohort.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierRainey_colostate_0053A_18958.pdf
dc.identifier.urihttps://hdl.handle.net/10217/241090
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.rights.accessEmbargo expires: 05/28/2026.
dc.subjectregression models
dc.subjectspatial splines
dc.subjectspatial confounding
dc.subjectenvironmental epidemiology
dc.titleAccounting for spatial confounding in large scale epidemiological studies
dc.typeText
dcterms.embargo.expires2026-05-28
dcterms.embargo.terms2026-05-28
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineStatistics
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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