{"id":{"repo_id":"colostate","oai_identifier":"oai:mountainscholar.org:10217/241090"},"canonical_url":"https://search.dev.ndltd.org/etd/colostate/oai:mountainscholar.org:10217/241090","repository":{"repo_id":"colostate","name":"Colorado State University","base_url":"https://api.mountainscholar.org/server/oai/request"},"display":{"title":"Accounting for spatial confounding in large scale epidemiological studies","abstract":"Epidemiological 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.","abstract_html":"Epidemiological 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.","abstract_has_math":false,"creators":["Rainey, Maddie J., author","Keller, Kayleigh, advisor","Wilson, Ander, committee member","Guan, Yawen, committee member","Anderson, Brooke, committee member"],"institution":"Colorado State University. Libraries","degree_name":"Doctor of Philosophy (Ph.D.)","degree_level":"Doctoral","degree_discipline":"Statistics","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-27T19:13:06Z","subjects":["regression models","spatial splines","spatial confounding","environmental epidemiology"],"languages":["eng","English"],"rights":["Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. 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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."]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["born digital","doctoral dissertations"]},{"key":"dc:title","label":"Title","values":["Accounting for spatial confounding in large scale epidemiological studies"]}]}],"canonical_facts":{"dc:creator":["Rainey, Maddie J., author","Keller, Kayleigh, advisor","Wilson, Ander, committee member","Guan, Yawen, committee member","Anderson, Brooke, committee member"],"dc:date.accessioned":["2025-06-02T15:21:29Z"],"dc:date.available":["2026-05-28"],"dc:date.issued":["2025"],"dc:description.abstract":["Epidemiological 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. 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