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Sensitivity analysis methods in the biomedical sciences.

George Qian1, Adam Mahdi2

  • 1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.

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|January 19, 2020
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Summary
This summary is machine-generated.

This review details sensitivity analysis methods for mathematical models, covering their pros, cons, and software. It applies popular techniques like Morris and Sobol to biomedical models for better understanding.

Keywords:
Mathematical modellingSensitivity analysisUncertainty quantification

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Area of Science:

  • Mathematical Modeling
  • Computational Biology
  • Biostatistics

Background:

  • Sensitivity analysis is crucial for understanding mathematical model behavior.
  • Various techniques exist, each with unique strengths and weaknesses.
  • Effective model analysis requires careful selection and application of these methods.

Purpose of the Study:

  • To review and compare common sensitivity analysis techniques.
  • To provide practical guidance for implementing sensitivity analyses.
  • To demonstrate the application of Morris and Sobol methods in biomedical modeling.

Main Methods:

  • Review of established sensitivity analysis methodologies.
  • Comparative analysis of techniques using a simple model.
  • Application of Morris and Sobol methods to two biomedical models.

Main Results:

  • Detailed comparison of sensitivity analysis techniques' advantages and limitations.
  • Practical guide and software summary for modelers.
  • Illustrative application of Morris and Sobol methods in a biomedical context.

Conclusions:

  • Sensitivity analysis is essential for robust mathematical modeling.
  • Understanding method principles and results presentation is key for accurate interpretation.
  • The study provides valuable insights for researchers using sensitivity analysis in biomedical applications.