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    Identifying the source of food-borne disease outbreaks: An application of Bayesian variable selection.

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    Authors
    Jacobs, Rianne
    Lesaffre, Emmanuel
    Teunis, Peter Fm
    Höhle, Michael
    van de Kassteele, Jan
    Type
    Article
    Language
    en
    
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    Title
    Identifying the source of food-borne disease outbreaks: An application of Bayesian variable selection.
    Published in
    Stat Methods Med Res 2019; 28(4):1126-40
    Publiekssamenvatting
    Early identification of contaminated food products is crucial in reducing health burdens of food-borne disease outbreaks. Analytic case-control studies are primarily used in this identification stage by comparing exposures in cases and controls using logistic regression. Standard epidemiological analysis practice is not formally defined and the combination of currently applied methods is subject to issues such as response misclassification, missing values, multiple testing problems and small sample estimation problems resulting in biased and possibly misleading results. In this paper, we develop a formal Bayesian variable selection method to account for misclassified responses and missing covariates, which are common complications in food-borne outbreak investigations. We illustrate the implementation and performance of our method on a Salmonella Thompson outbreak in the Netherlands in 2012. Our method is shown to perform better than the standard logistic regression approach with respect to earlier identification of contaminated food products. It also allows relatively easy implementation of otherwise complex methodological issues.
    DOI
    10.1177/0962280217747311
    PMID
    29241399
    URI
    http://hdl.handle.net/10029/621752
    ae974a485f413a2113503eed53cd6c53
    10.1177/0962280217747311
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