Application of the Vertex Exchange Method to estimate a semi-parametric mixture model for the MIC density of<i>Escherichia coli</i>isolates tested for susceptibility against ampicillin

Jaspers, Stijn; Verbeke, Geert; Böhning, Dankmar; Aerts, Marc

doi:10.1093/biostatistics/kxv030

Cited by 7 publications

(11 citation statements)

References 21 publications

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“…In a univariate setting, while using the mixtures to model the MIC distribution (e.g. Jaspers et al., ), the wild‐type subpopulation of microbials was characterized by the mixture component with the lowest mean value. In a multivariate setting, with K mixture means

μ_{k} = false(μ_{k, 1}, \dots, μ_{k, q} false)

k = 1, \dots, K

, it is however not always possible to identify a single component

g \in {1, \dots, K}

for which all marginal means

μ_{g, j}

j = 1, \dots, q

, attain their minimal value across the subpopulations.…”

Section: Modelmentioning

confidence: 99%

“…In summary, we require two extensions to the existing methodology, that is we need (E1)a procedure that is able to cope with covariate‐dependent mixing weights of the mixture and (E2)a procedure that is able to estimate the joint (multivariate) MIC density of two or more antimicrobials. Especially the extension into the multivariate setting is not straightforward for the univariate semiparametric approaches of Jaspers et al. (, , , ) mentioned above.…”

Section: Introductionmentioning

confidence: 99%

“…In a second stage, 2 (nonwild-type subpopulation) was estimated using a censored-adjusted version of the penalized mixture approach by Schellhase and Kauermann (2012). Several drawbacks related to this two-stage approach were circumvented by the Bayesian composite link model described in Jaspers, Lambert, and Aerts (2016b) and by the back-fitting algorithm presented in Jaspers, Verbeke, Böhning, and Aerts (2016c). The latter approach approximates the unknown density 2 by a mixture of Gaussian densities, a simple yet flexible approach to describe non-Gaussian distributions (Roeder & Wasserman, 1997;Richardson & Green, 1997).…”

Section: Introductionmentioning

confidence: 99%

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Bayesian estimation of multivariate normal mixtures with covariate‐dependent mixing weights, with an application in antimicrobial resistance monitoring

2017

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Bacteria with a reduced susceptibility against antimicrobials pose a major threat to public health. Therefore, large programs have been set up to collect minimum inhibition concentration (MIC) values. These values can be used to monitor the distribution of the nonsusceptible isolates in the general population. Data are collected within several countries and over a number of years. In addition, the sampled bacterial isolates were not tested for susceptibility against one antimicrobial, but rather against an entire range of substances. Interest is therefore in the analysis of the joint distribution of MIC data on two or more antimicrobials, while accounting for a possible effect of covariates. In this regard, we present a Bayesian semiparametric density estimation routine, based on multivariate Gaussian mixtures. The mixing weights are allowed to depend on certain covariates, thereby allowing the user to detect certain changes over, for example, time. The new approach was applied to data collected in Europe in 2010, 2012, and 2013. We investigated the susceptibility of Escherichia coli isolates against ampicillin and trimethoprim, where we found that there seems to be a significant increase in the proportion of nonsusceptible isolates. In addition, a simulation study was carried out, showing the promising behavior of the proposed method in the field of antimicrobial resistance.

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μ_{k} = false(μ_{k, 1}, \dots, μ_{k, q} false)

k = 1, \dots, K

, it is however not always possible to identify a single component

g \in {1, \dots, K}

for which all marginal means

μ_{g, j}

j = 1, \dots, q

, attain their minimal value across the subpopulations.…”

Section: Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian estimation of multivariate normal mixtures with covariate‐dependent mixing weights, with an application in antimicrobial resistance monitoring

2017

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“…In the absence of a golden standard, we decided to compare between three existing methods. The adjusted CLM approach is compared to the back-fitting algorithm and to the two-stage penalized mixture (PM) approach presented in Jaspers et al (2014bJaspers et al ( , 2016, respectively.…”

Section: Simulation Studymentioning

confidence: 99%

“…In addition, there seemed to be some kind of discontinuity in the region of overlap between the first and second component, resulting from the used two-stage approach. These drawbacks were circumvented by the back-fitting algorithm presented in Jaspers et al (2016). In their paper, the authors proposed a likelihood based method for the estimation of both the wild-type and nonwild-type component.…”

mentioning

confidence: 99%

A Bayesian approach to the semiparametric estimation of a minimum inhibitory concentration distribution

Jaspers¹,

Lambert²,

Aerts³

2016

Ann. Appl. Stat.

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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.

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Estimating MIC distributions and cutoffs through mixture models: an application to establish M. Tuberculosis resistance

Grazian¹

2019

Preprint

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Antimicrobial resistance is becoming a major threat to public health throughout the world. Researchers from around the world are attempting to contrast it by developing both new antibiotics and patientspecific treatments. It is, therefore, necessary to study these treatments, via phenotypic tests, and it is essential to have robust methods available to analyze the resistance patterns to medication, which could be applied to both new treatments and to new phenotypic tests. A general method is here proposed to study minimal inhibitory concentration (MIC) distributions and fixed breakpoints in order to separate sensible from resistant strains. The method has been applied to a new 96-well microtiter plate.

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Application of the Vertex Exchange Method to estimate a semi-parametric mixture model for the MIC density ofEscherichia coliisolates tested for susceptibility against ampicillin

Cited by 7 publications

References 21 publications

Bayesian estimation of multivariate normal mixtures with covariate‐dependent mixing weights, with an application in antimicrobial resistance monitoring

Bayesian estimation of multivariate normal mixtures with covariate‐dependent mixing weights, with an application in antimicrobial resistance monitoring

A Bayesian approach to the semiparametric estimation of a minimum inhibitory concentration distribution

Estimating MIC distributions and cutoffs through mixture models: an application to establish M. Tuberculosis resistance

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