Overview of Quantitative Methodologies to Understand Antimicrobial Resistance via Minimum Inhibitory Concentration
Abstract
:Simple Summary
Abstract
1. Introduction
2. Regression for Dichotomized Minimum Inhibitory Concentration (MIC) Data
2.1. Epidemiological Cutoffs and Clinical Breakpoints
2.2. Logistic Regression
2.3. Considerations
2.4. Information Loss and Minimum Inhibitory Concentration (MIC) Creep
3. Models for Ordinal Data
3.1. Cumulative Logistic Regression
3.2. Applications of Regression Approaches for Ordinal Data
3.3. Considerations for Cumulative Logistic Regression
4. Models on the Continuous Scale for IntervalCensored Data
4.1. Mixture Models
4.2. Considerations for Mixture Models
4.3. Accelerated Failure Time Models
4.4. Considerations for Accelerated Failure Time Models
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Model  Data Type  Advantages  Disadvantages 

Logistic regression  Dichotomous 


Proportional odds cumulative logit model  Ordinal 


Generalized ordered logit model  Ordinal 


Mixture models  IntervalCensored 


Accelerated failure time model with frailties  IntervalCensored 


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Michael, A.; Kelman, T.; Pitesky, M. Overview of Quantitative Methodologies to Understand Antimicrobial Resistance via Minimum Inhibitory Concentration. Animals 2020, 10, 1405. https://doi.org/10.3390/ani10081405
Michael A, Kelman T, Pitesky M. Overview of Quantitative Methodologies to Understand Antimicrobial Resistance via Minimum Inhibitory Concentration. Animals. 2020; 10(8):1405. https://doi.org/10.3390/ani10081405
Chicago/Turabian StyleMichael, Alec, Todd Kelman, and Maurice Pitesky. 2020. "Overview of Quantitative Methodologies to Understand Antimicrobial Resistance via Minimum Inhibitory Concentration" Animals 10, no. 8: 1405. https://doi.org/10.3390/ani10081405