Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium.
Stat Med. 2014 Jan 30;33(2):289-303. doi: 10.1002/sim.5939. Epub 2013 Aug 15.
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.
抗菌药物耐药性已经成为过去几十年中主要的公共卫生负担之一，因此，监测非野生型分离株的发展和传播引起了越来越多的关注。监测是基于最小抑菌浓度（MIC）值进行的，这些值是通过稀释实验收集的。为了考虑野生型和非野生型分离株未观察到的群体异质性，混合物模型非常有用。我们的主要目标不是全局估计整个混合物，而是仅估计野生型的第一分量。一旦为给定的抗菌药物确定了野生型种群，该第一分量的特征预计不会随时间变化。为此，我们开发了一种基于多项分布的新方法，并进行了模拟研究以研究新估计量的性质。由于新方法适合似然框架，我们可以比较不同的分布假设，以确定最适合野生型种群的分布。我们基于 AIC 准则确定最优参数，并注意使用 Akaike 权重的模型平均方法。后者被认为非常适合得出野生型分布的特定特征，并确定野生型 MIC 范围的限制。通过这种方式，新方法提供了一种优雅的手段来比较不同的分布假设，并量化特定抗生素-细菌组合的野生型 MIC 分布。
