Estimation of an MIC distribution using a two-stage semi-parametric mixture model

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 Inhibition Concentration (MIC) values, which are collected through the application of dilution experiments. A semi-parametric mixture model is presented, which is able to estimate the full continuous MIC distribution. The model is based on an extended and censored-adjusted version of the penalized mixture approach often used in density estimation. A data application and simulation study are presented in which the promising behaviour of the new method is illustrated.