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Related Experiment Video

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Cross-Modal Multivariate Pattern Analysis
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Expanding behavior pattern sensitivity analysis with model selection and survival analysis.

Casey L Cazer1, Victoriya V Volkova2, Yrjö T Gröhn2

  • 1Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA. clc248@cornell.edu.

BMC Veterinary Research
|November 21, 2018
PubMed
Summary
This summary is machine-generated.

Behavior pattern sensitivity analysis helps identify key factors influencing tetracycline-resistant bacteria in cattle. This method prioritizes bacterial population parameters for effective intervention strategies.

Keywords:
Antibiotic resistanceAntimicrobial resistanceBeef cattleBehavior patternLinear regressionSensitivity analysisSurvival analysis

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Area of Science:

  • Mathematical modeling
  • Ecology
  • Veterinary science

Background:

  • Sensitivity analysis is crucial for understanding parameter influence in mathematical models.
  • Behavior pattern sensitivity analysis (BPSA) is a novel method for models with multiple output behaviors.
  • BPSA classifies model output by behavior mode and calculates pattern measures to identify input-output associations.

Purpose of the Study:

  • To expand BPSA by integrating model selection for parsimonious regression models.
  • To demonstrate methods for addressing violations of linear regression assumptions.
  • To explore Cox proportional hazards models as an alternative for censored data.

Main Methods:

  • Applied BPSA to a mathematical model of tetracycline-resistant enteric bacteria in beef cattle.
  • Utilized linear regression models with parameters as independent and behavior measures as dependent variables.
  • Employed Cox proportional hazard models when linear regression assumptions were not met.

Main Results:

  • Expanded BPSA incorporates model selection for efficient parameter prioritization.
  • Identified three distinct resistant bacteria behaviors: increasing, decreasing, and biphasic.
  • Bacterial population parameters were found to be highly influential in determining resistant bacteria population dynamics.

Conclusions:

  • Dietary interventions targeting enteric bacterial ecology may reduce tetracycline resistance in cattle.
  • BPSA is a flexible tool for sensitivity analysis in models with varied outputs.
  • Cox proportional hazard models offer an alternative for censored data or unmet regression assumptions.