Shiny Contest Submission: Statistical Models to Combat Antibiotic Resistance

Antibiotic resistance has been declared one of the most important issues of our time by the World Health Organization. Methods currently used in practice are outdated and provide no estimates of uncertainity. Advanced statistical models have been proposed over ten years ago but have not seen widespread use due to implementation difficulties with clinicians. This Shiny app provides an interface to state-of-the-art statistical methods as well as improving existing methods and data visualization methods.

Clinicians conduct experiments to estimate how susceptible a particular bacterium strain to various antibiotics. These experiments are intended to label dosage level breakpoints that classify the bacterium strain as susceptible, indeterminant, or resistant. The determination of these breakpoints has become a challenging issue due to the increasing resistance of microorganisms to antibiotics. This Shiny app, dBETS, ( diffusion Breakpoint Estimation Testing Software) helps clinicians easily analyze data from susceptibility experiments through visualization, error-rate bounded, and model-based approaches.

In addition to providing clinicians a free to use interface, dBETS implements Bayesian monotonic non-parametric curve fitting to rounded and censored data.

References and further details below.


Paper describing dBETS:
DePalma, Glen and Turnidge, John and Craig, Bruce A. Determination of disk diffusion susceptibility testing interpretive criteria using model-based analysis: development and implementation. Diagnostic Microbiology & Infectious Disease. 2017. 87[2] 143-149.

Papers describing the model behind dBETS:

DePalma, Glen and Craig, Bruce A. Bayesian monotonic errors-in-variables models with applications to pathogen susceptibility testing. Statistics in Medicine. 2018. 37[3] 487-502. DOI: 10.1002/sim.7533.

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