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Bayesian tree based methods for longitudinally assessed environmental mixtures

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dc.contributor.authorIm, Seongwon, author
dc.contributor.authorWilson, Ander, advisor
dc.contributor.authorKeller, Kayleigh, committee member
dc.contributor.authorKoslovsky, Matt, committee member
dc.contributor.authorNeophytou, Andreas, committee member
dc.date.accessioned2024-09-09T20:52:11Z
dc.date.available2025-08-16
dc.date.issued2024
dc.description.abstractIn various fields, there is interest in estimating the lagged association between an exposure and an outcome. This is particularly common in environmental health studies, where exposure to an environmental chemical is measured repeatedly during gestation for the assessment of its lagged effects on a birth outcome. The relationship between longitudinally assessed environmental mixtures and a health outcome is also of greater interest. For a single exposure, a distributed lag model (DLM) is a widely used method that provides an appropriate temporal structure for estimating the time-varying effects. For mixture exposures, a distributed lag mixture model is used to address the main effect of each exposure and lagged interactions among exposures. The main inferential goals include estimating the lag-specific effects and identifying a window of susceptibility, during which a fetus is particularly vulnerable. In this dissertation, we propose novel statistical methods for estimating exposure effects of longitudinally assessed environmental mixtures in various scenarios. First, we propose a method that can estimate a linear exposure-time-response function between mixture exposures and a count outcome that may be zero-inflated and overdispersed. To achieve this, we employ a Bayesian PĆ³lya-Gamma data augmentation with a treed distributed lag mixture model framework. We apply the method to estimate the relationship between weekly average fine particulate matter (PM2.5) and temperature and pregnancy loss with live-birth identified conception time series design with administrative data from Colorado. Second, we propose a tree triplet structure to allow for heterogeneity in exposure effects in an environmental mixture exposure setting. Our method accommodates modifier and exposure selection, which allows for personalized and subgroup-specific effect estimation and windows of susceptibility identification. We apply the method to Colorado administrative birth data to examine the heterogeneous relationship between PM2.5 and temperature and birth weight. Finally, we introduce an R package dlmtree that integrates tree structured DLM methods into convenient software. We provide an overview of the embedded tree structured DLMs and use simulated data to demonstrate a model fitting process, statistical inference, and visualization.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierIm_colostate_0053A_18503.pdf
dc.identifier.urihttps://hdl.handle.net/10217/239266
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: 08/16/2025.
dc.subjectbirth outcome
dc.subjectenvironmental mixture
dc.subjectzero-inflated count data
dc.subjectdistributed lag model
dc.subjectair pollution
dc.subjectheterogeneity
dc.titleBayesian tree based methods for longitudinally assessed environmental mixtures
dc.typeText
dcterms.embargo.expires2025-08-16
dcterms.embargo.terms2025-08-16
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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