Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/16109
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | JASPERS, Stijn | - |
dc.contributor.author | AERTS, Marc | - |
dc.contributor.author | VERBEKE, Geert | - |
dc.contributor.author | Beloeil, Pierre-Alexandre | - |
dc.date.accessioned | 2014-01-10T09:07:10Z | - |
dc.date.available | 2014-01-10T09:07:10Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | STATISTICS IN MEDICINE, 33, p. 289-303 | - |
dc.identifier.issn | 0277-6715 | - |
dc.identifier.uri | http://hdl.handle.net/1942/16109 | - |
dc.description.abstract | Antimicrobial resistance has become one of the main public health burdens of the last decades, and monitoring the development and spread of non-wild-type isolates has therefore gained increased interest. Monitoring is performed based on the minimum inhibitory concentration (MIC) values, which are collected through the application of dilution experiments. In order to account for the unobserved population heterogeneity of wild-type and non-wild-type isolates, mixture models are extremely useful. Instead of estimating the entire mixture globally, it was our major aim to provide an estimate for the wild-type first component only. The characteristics of this first component are not expected to change over time, once the wild-type population has been confidently identified for a given antimicrobial.With this purpose, we developed a new method based on the multinomial distribution, and we carry out a simulation study to study the properties of the new estimator. Because the new approach fits within the likelihood framework, we can compare distinct distributional assumptions in order to determine the most suitable distribution for the wild-type population. We determine the optimal parameters based on the AIC criterion, and attention is also paid to the model-averaged approach using the Akaike weights. The latter is thought to be very suitable to derive specific characteristics of the wild-type distribution and to determine limits for the wild-type MIC range. In this way, the new method provides an elegant means to compare distinct distributional assumptions and to quantify the wild-type MIC distribution of specific antibiotic–bacterium combinations. | - |
dc.language.iso | en | - |
dc.rights | 2013 JohnWiley & Sons, Ltd | - |
dc.subject.other | antimicrobial resistance; censoring; model averaging; multinomial distribution; wild-type distribution | - |
dc.title | Estimation of the wild-type minimum inhibitory concentration value distribution | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 303 | - |
dc.identifier.spage | 289 | - |
dc.identifier.volume | 33 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.relation.references | 1. Tenover F. Mechanisms of antimicrobial resistance in bacteria. The American Journal of Medicine 2006; 119:S3–S10. 2. Strasfeld L, Chou S. Antiviral drug resistance: mechanisms and clinical implications. Infectious Disease Clinics of North America 2010; 24:413–437. 3. Chen D, McGeer A, de Azavedo J, Low D. Decreased susceptibility of Streptococcus pneumoniae to fluoroquinolones in Canada. New England Journal of Medicine 1999; 341:233–239. 4. Kahlmeter G, Brown DFJ, Goldstein FW, MacGowan AP, Mouton JW, Osterlund A, Rodloff A, Steinbakk M, Urbaskova P, Vatopoulos A. European harmonization of MIC breakpoints for antimicrobial susceptibility testing of bacteria. Journal of Antimicrobial Chemotherapy 2003; 52:145–148. 5. Annis D, Craig B. Statistical properties and inference of the antimicrobial MIC test. Statistics in Medicine 2005; 24:3631–3644. 6. Craig B. Modeling approach to diameter breakpoint determination. Diagnostic Microbiology and Infectious Disease 2000; 36:193–202. 7. Lee MLT, Whitmore GA. Statistical inference for serial dilution assay data. Biometrics 1999; 55:1215–1220. 8. Turnidge J, Kahlmeter G, Kronvall G. Statistical characterisation of bacterial wild-type MIC value distributions and the determination of epidemiological cut-off values. Clinical Microbiology and Infection 2006; 12:418–425. 9. Böhning D. A vertex-exchange-method in D-optimal design theory. Metrika 1986; 33:337–347. 10. Schellhase C, Kauermann G. Density estimation and comparison with a penalized mixture approach. Computational Statistics 2012; 27:757–777. 11. Goldstein F. Penicillin-resistant Streptococcus pneumoniae: selection by both ˇ-lactam and non-ˇ-lactam antibiotics. Journal of Antimicrobial Chemotherapy 1999; 44:141–144. 12. Hakenbeck R, Grebe T, Zähner D, Stock JB. ˇ-lactam resistance in Streptococcus pneumoniae: penicillin-binding proteins and non-penicillin-binding proteins. Molecular Biology 1999; 33:673–678. 13. Burnham K, Anderson D. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Springer: New York, 2002. 14. Jaspers S, Aerts M, Verbeke G, Beloeil P. A new semi-parametric mixture model for interval censored data, with applications in the field of antimicrobial resistance. Computational Statistics and Data Analysis 2013; nv:np. DOI: 10.1016/j.csda.2013.01.024. | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1002/sim.5939 | - |
dc.identifier.isi | 000328373800008 | - |
dc.identifier.url | http://onlinelibrary.wiley.com/doi/10.1002/sim.5939/abstract | - |
item.fullcitation | JASPERS, Stijn; AERTS, Marc; VERBEKE, Geert & Beloeil, Pierre-Alexandre (2014) Estimation of the wild-type minimum inhibitory concentration value distribution. In: STATISTICS IN MEDICINE, 33, p. 289-303. | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2015 | - |
item.contributor | JASPERS, Stijn | - |
item.contributor | AERTS, Marc | - |
item.contributor | VERBEKE, Geert | - |
item.contributor | Beloeil, Pierre-Alexandre | - |
item.accessRights | Open Access | - |
crisitem.journal.issn | 0277-6715 | - |
crisitem.journal.eissn | 1097-0258 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Jaspers et al 2014a.pdf | Published version | 226.64 kB | Adobe PDF | View/Open |
SCOPUSTM
Citations
8
checked on Sep 3, 2020
WEB OF SCIENCETM
Citations
13
checked on Sep 20, 2024
Page view(s)
56
checked on Jun 21, 2022
Download(s)
254
checked on Jun 21, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.