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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Bayesian monotonic errors-in-variables models with applications to pathogen susceptibility testing.

Glen DePalma1, Bruce A Craig1

  • 1Department of Statistics, Purdue University, 250 N. University Street, West Lafayette, 47907, IN, U.S.A.

Statistics in Medicine
|November 21, 2017
PubMed
Summary
This summary is machine-generated.

This study enhances antimicrobial susceptibility testing by improving the calibration of disk diffusion assays to MIC assays. New Bayesian models offer more precise and accurate estimation of antibiotic resistance breakpoints.

Keywords:
Bayesian inferencemeasurement errormonotonicitynonparametricreversible jump Markov chain Monte Carlosusceptibility testing

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Area of Science:

  • Microbiology
  • Biostatistics
  • Pharmacology

Background:

  • Antimicrobial susceptibility testing (AST) is crucial for guiding antibiotic treatment.
  • Disk diffusion (DIA) and drug dilution (MIC) assays are common AST methods.
  • Accurate calibration between DIA and MIC assays is essential for reliable results.

Purpose of the Study:

  • To improve the calibration of DIA breakpoints to MIC assays.
  • To introduce flexible Bayesian models for enhanced accuracy in breakpoint estimation.
  • To address limitations of existing error-rate bounded methods.

Main Methods:

  • Developed a Bayesian 4-parameter logistic model, extending Craig's 3-parameter model.
  • Introduced a Bayesian nonparametric spline model to describe the relationship between DIA and MIC assays.
  • Proposed two methods for spline knot selection, including random walk priors and treating knots as unknown parameters.

Main Results:

  • The proposed Bayesian models offer greater precision and accuracy in estimating DIA breakpoints.
  • Simulations demonstrated the effectiveness of the enhanced model-based calibration approaches.
  • Real-world data sets were analyzed to validate the practical application of the methods.

Conclusions:

  • The enhanced Bayesian models provide a more robust framework for calibrating antimicrobial susceptibility assays.
  • These methods improve the accuracy of determining antibiotic resistance breakpoints, aiding clinical decision-making.
  • The study advances statistical approaches in infectious disease diagnostics.