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• W3048398812 abstract "The development of antimicrobial resistance (AMR) represents a significant threat to humans and food animals. The use of antimicrobials in human and veterinary medicine may select for resistant bacteria, resulting in increased levels of AMR in these populations. As the threat presented by AMR increases, it becomes critically important to find methods for effectively interpreting minimum inhibitory concentration (MIC) tests. Currently, a wide array of techniques for analyzing these data can be found in the literature, but few guidelines for choosing among them exist. Here, we examine several quantitative techniques for analyzing the results of MIC tests and discuss and summarize various ways to model MIC data. The goal of this review is to propose important considerations for appropriate model selection given the purpose and context of the study. Approaches reviewed include mixture models, logistic regression, cumulative logistic regression, and accelerated failure time–frailty models. Important considerations in model selection include the objective of the study (e.g., modeling MIC creep vs. clinical resistance), degree of censoring in the data (e.g., heavily left/right censored vs. primarily interval censored), and consistency of testing parameters (e.g., same range of concentrations tested for a given antibiotic)." @default.
• W3048398812 created "2020-08-18" @default.
• W3048398812 creator A5014297164 @default.
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• W3048398812 date "2020-08-12" @default.
• W3048398812 modified "2023-10-16" @default.
• W3048398812 title "Overview of Quantitative Methodologies to Understand Antimicrobial Resistance via Minimum Inhibitory Concentration" @default.
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• W3048398812 doi "https://doi.org/10.3390/ani10081405" @default.
• W3048398812 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7459578" @default.
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