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Disease Diagnosis from Immunoassays with Plate to Plate Variability: A Hierarchical Bayesian Approach

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dc.contributor.author Entine, Oliver A.
dc.contributor.author Small, Dylan S.
dc.contributor.author Jensen, Shane T.
dc.contributor.author Sanchez, Gerardo
dc.contributor.author Bastos, Milagros
dc.contributor.author Verastegui, Manuela R.
dc.contributor.author Levy, Michael Z.
dc.date.accessioned 2019-02-22T14:53:57Z
dc.date.available 2019-02-22T14:53:57Z
dc.date.issued 2014
dc.identifier.uri http://doi.org/10.1007/s12561-014-9113-5
dc.identifier.uri http://repositorio.upch.edu.pe/handle/upch/5581
dc.description.abstract The standard methods of diagnosing disease based on antibody microtiter plates are quite crude. Few methods create a rigorous underlying model for the antibody levels of populations consisting of a mixture of positive and negative subjects, and fewer make full use of the entirety of the available data for diagnoses. In this paper, we propose a Bayesian hierarchical model that provides a systematic way of pooling data across different plates, and accounts for the subtle sources of variations that occur in the optical densities of typical microtiter data. In addition to our Bayesian method having good frequentist properties, we find that our method outperforms one of the standard crude approaches (the “3 SD Rule”) under reasonable assumptions, and provides more accurate disease diagnoses in terms of both sensitivity and specificity.
dc.language.iso eng
dc.publisher Springer Verlag
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject Monte Carlo method
dc.subject performance
dc.subject sensitivity and specificity
dc.subject immunoassay
dc.subject receiver operating characteristic
dc.subject enzyme linked immunosorbent assay
dc.subject health care planning
dc.subject simulation
dc.subject optical density
dc.subject Bayesian learning
dc.title Disease Diagnosis from Immunoassays with Plate to Plate Variability: A Hierarchical Bayesian Approach
dc.type info:eu-repo/semantics/article
dc.identifier.journal Statistics in Biosciences

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