Bayesian network-based framework for exposure-response study design and interpretation

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dc.contributor.authorOrak, Nur H.
dc.contributor.authorSmall, Mitchell J.
dc.contributor.authorDruzdzel, Marek J.
dc.date.accessioned2020-04-30T22:40:05Z
dc.date.available2020-04-30T22:40:05Z
dc.date.issued2019
dc.departmentDÜ, Mühendislik Fakültesi, Çevre Mühendisliği Bölümüen_US
dc.descriptionOrak, Nur H./0000-0002-3830-9260en_US
dc.descriptionWOS: 000462148800002en_US
dc.descriptionPubMed: 30902096en_US
dc.description.abstractConventional environmental-health risk-assessment methods are often limited in their ability to account for uncertainty in contaminant exposure, chemical toxicity and resulting human health risk. Exposure levels and toxicity are both subject to significant measurement errors, and many predicted risks are well below those distinguishable from background incident rates in target populations. To address these issues methods are needed to characterize uncertainties in observations and inferences, including the ability to interpret the influence of improved measurements and larger datasets. Here we develop a Bayesian network (BN) model to quantify the joint effects of measurement errors and different sample sizes on an illustrative exposure-response system. Categorical variables are included in the network to describe measurement accuracies, actual and measured exposures, actual and measured response, and the true strength of the exposure-response relationship. Network scenarios are developed by fixing combinations of the exposure-response strength of relationship (none, medium or strong) and the accuracy of exposure and response measurements (low, high, perfect). Multiple cases are simulated for each scenario, corresponding to a synthetic exposure response study sampled from the known scenario population. A learn-from-cases algorithm is then used to assimilate the synthetic observations into an uninformed prior network, yielding updated probabilities for the strength of relationship. Ten replicate studies are simulated for each scenario and sample size, and results are presented for individual trials and their mean prediction. The model as parameterized yields little-to-no convergence when low accuracy measurements are used, though progressively faster convergence when employing high accuracy or perfect measurements. The inferences from the model are particularly efficient when the true strength of relationship is none or strong with smaller sample sizes. The tool developed in this study can help in the screening and design of exposure-response studies to better anticipate where such outcomes can occur under different levels of measurement error. It may also serve to inform methods of analysis for other network models that consider multiple streams of evidence from multiple studies of cumulative exposure and effects.en_US
dc.identifier.doi10.1186/s12940-019-0461-yen_US
dc.identifier.issn1476-069X
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1186/s12940-019-0461-y
dc.identifier.urihttps://hdl.handle.net/20.500.12684/2899
dc.identifier.volume18en_US
dc.identifier.wosWOS:000462148800002en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherBmcen_US
dc.relation.ispartofEnvironmental Healthen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHealth risk assessmenten_US
dc.subjectExposure-responseen_US
dc.subjectBayesian networksen_US
dc.subjectMeasurement erroren_US
dc.subjectToxicologyen_US
dc.subjectEnvironmenten_US
dc.subjectEnvironmental healthen_US
dc.titleBayesian network-based framework for exposure-response study design and interpretationen_US
dc.typeArticleen_US

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