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

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.