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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/22762

Title: A BAYESIAN APPROACH TO THE SEMIPARAMETRIC ESTIMATION OF A MINIMUM INHIBITORY CONCENTRATION DISTRIBUTION
Authors: Jaspers, Stijn
Lambert, Philippe
Aerts, Marc
Issue Date: 2016
Publisher: INST MATHEMATICAL STATISTICS
Citation: ANNALS OF APPLIED STATISTICS, 10(2), p. 906-924
Abstract: Bacteria that have developed a reduced susceptibility against antimicrobials pose a major threat to public health. Hence, monitoring their distribution in the general population is of major importance. This monitoring is performed based on minimum inhibitory concentration (MIC) values, which are collected through dilution experiments. We present a semiparametric mixture model to estimate the MIC density on the full continuous scale. The wild-type first component is assumed to be of a parametric form, while the nonwild-type second component is modelled nonparametrically using Bayesian P-splines combined with the composite link model. A Metropolis within Gibbs strategy was used to draw a sample from the joint posterior. The newly developed method was applied to a specific bacterium-antibiotic combination, that is, Escherichia coli tested against ampicillin. After obtaining an estimate for the entire density, model-based classification can be performed to check whether or not an isolate belongs to the wild-type subpopulation. The performance of the new method, compared to two existing competitors, is assessed through a small simulation study.
Notes: [Jaspers, Stijn; Aerts, Marc] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, Agoralaan Gebouw D, B-3590 Diepenbeek, Belgium. [Lambert, Philippe] Univ Liege, Inst Sci Humaines & Sociales Methodes Quantitat, Blvd Rectorat 7 B31, B-4000 Liege, Belgium. [Lambert, Philippe] Catholic Univ Louvain, Inst Stat Biostat & Sci Actuarielles ISBA, Louvain La Neuve, Belgium.
URI: http://hdl.handle.net/1942/22762
DOI: 10.1214/16-AOAS918
ISI #: 000385029700015
ISSN: 1932-6157
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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