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

Jaspers, Stijn; Lambert, Philippe; Aerts, Marc

doi:10.1214/16-aoas918

“…To date, the predominant approach to assessing changes in AMR has focused on evaluation of changes in the proportion of isolates resistant to a particular antibiotic over time. Several statistical methods have been employed, including the Cochran-Armitage trend test, logistic regression model with time as a co-variate [5] [6] [7], and the Mann-Kendall non-parametric method to test monotonic trends over time [8]. These statistical methods are based on MIC data that are dichotomized to resistant and non-resistant.…”

Section: Previous Work and Challengesmentioning

confidence: 99%

A hierarchical Bayesian latent class mixture model with censorship for detection of linear temporal changes in antibiotic resistance

Zhang

¹

,

Wang

²

,

O’Connor

³

2020

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Identifying and controlling the emergence of antimicrobial resistance (AMR) is a high priority for researchers and public health officials. One critical component of this control effort is timely detection of emerging or increasing resistance using surveillance programs. Currently, detection of temporal changes in AMR relies mainly on analysis of the proportion of resistant isolates based on the dichotomization of minimum inhibitory concentration (MIC) values. In our work, we developed a hierarchical Bayesian latent class mixture model that incorporates a linear trend for the mean log 2 MIC of the non-resistant population. By introducing latent variables, our model addressed the challenges associated with the AMR MIC values, compensating for the censored nature of the MIC observations as well as the mixed components indicated by the censored MIC distributions. Inclusion of linear regression with time as a covariate in the hierarchical structure allowed modelling of the linear creep of the mean log 2 MIC in the non-resistant population. The hierarchical Bayesian model was accurate and robust as assessed in simulation studies. The proposed approach was illustrated using Salmonella enterica I,4,[5],12:i:-treated with chloramphenicol and ceftiofur in human and veterinary samples, revealing some significant linearly increasing patterns from the applications. Implementation of our approach to the analysis of an AMR MIC dataset would provide surveillance programs with a more complete picture of the changes in AMR over years by exploring the patterns of the mean resistance level in the non-resistant population. Our model could therefore serve as a timely indicator of a need for antibiotic intervention before an outbreak of resistance, highlighting the relevance of this work for public health. Currently, however, due to extreme right censoring on the MIC data, this approach has limited utility for tracking changes in the resistant population.

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“…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%

See 1 more Smart Citation

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

Jaspers

¹

,

Komárek

²

,

Aerts

³

2017

Self Cite

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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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“…Identifying and controlling the emergence of antimicrobial resistance (AMR) is a high 3 priority for researchers and public health officials. A critical component of this control 4 effort is surveillance for emerging or increasing resistance, as evidenced by the number 5 and scale of surveillance programs around the world [5] [6].…”

mentioning

confidence: 99%

A hierarchical Bayesian latent class mixture model with censorship for detection of linear temporal changes in antibiotic resistance

Zhang

¹

,

Wang

²

,

O’Connor

³

2019

Preprint

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Identifying and controlling the emergence of antimicrobial resistance (AMR) is a high priority for researchers and public health officials. One critical component of this control effort is timely detection of emerging or increasing resistance using surveillance programs. Currently, detection of temporal changes in AMR relies mainly on analysis of the proportion of resistant isolates based on the dichotomization of minimum inhibitory concentration (MIC) values. In our work, we developed a hierarchical Bayesian latent class mixture model that incorporates a linear trend for the mean log 2 MIC of the non-resistant population. By introducing latent variables, our model addressed the challenges associated with the AMR MIC values, compensating for the censored nature of the MIC observations as well as the mixed components indicated by the censored MIC distributions. Inclusion of linear regression with time as a covariate in the hierarchical structure allowed modelling of the linear creep of the mean log 2 MIC in the non-resistant population. The hierarchical Bayesian model was accurate and robust as assessed in simulation studies. The proposed approach was illustrated using Salmonella enterica I,4,[5],12:i:-treated with chloramphenicol and ceftiofur in human and veterinary samples, revealing some significant linearly increasing patterns from the applications. Implementation of our approach to the analysis of an AMR MIC dataset would provide surveillance programs with a more complete picture of the changes in AMR over years by exploring the patterns of the mean resistance level in the non-resistant population. Our model could therefore serve as a timely indicator of a need for antibiotic intervention before an outbreak of resistance, highlighting the relevance of this work for public health. Currently, however, due to extreme right censoring on the MIC data, this approach has limited utility for tracking changes in the resistant population.Statistical approaches for estimation of the mean MIC have been proposed 43 previously. Kassteele et al. [17] suggested a model for mean log 2 MIC estimation that 44 incorporated the censored nature of MIC data and adjusted for such bias using the 45 interval-censored normal distribution as the underlying distribution. This model is a 46 reasonable accommodation for censorship; however, the approach did not address the 47 mixture of resistant and non-resistant populations in the observed data. Craig [18] 48 proposed that the underlying distribution of log 2 MIC can be modeled by a mixture of 49 July 12, 2019 2/18 Gaussian distributions with resistant and non-resistant populations. Jaspers et 50 al. [1] [2] [3] modeled the full continuous MIC distribution for wild-type and 51 non-wild-type bacteria populations determined by epidemiological cut-off rather than 52 clinical breakpoints. According to their definition of bacterial categorization, the 53non-wild-type population has less informative distributions and was therefore estimated 54 in non-parametric ways. These previously publishe...

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

Cited by 12 publications

References 26 publications

A hierarchical Bayesian latent class mixture model with censorship for detection of linear temporal changes in antibiotic resistance

A hierarchical Bayesian latent class mixture model with censorship for detection of linear temporal changes in antibiotic resistance

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

A hierarchical Bayesian latent class mixture model with censorship for detection of linear temporal changes in antibiotic resistance

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