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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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Evaluating Parameter Uncertainty in a Simulation Model of Cancer Using Emulators.

Tiago M de Carvalho1,2, Eveline A M Heijnsdijk1, Luc Coffeng1

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This study introduces a computationally efficient emulator to quantify uncertainty in prostate cancer models. Using Gaussian process regression significantly reduces computation time for probabilistic sensitivity analyses, making uncertainty quantification feasible.

Keywords:
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Area of Science:

  • Computational modeling
  • Cancer epidemiology
  • Health informatics

Background:

  • Microsimulation models are vital in cancer research but face uncertainty challenges, particularly in prostate cancer overdiagnosis.
  • High computational demands often limit thorough examination of parametric uncertainty in these models.
  • Overdiagnosis in prostate cancer screening remains a significant concern requiring robust uncertainty analysis.

Purpose of the Study:

  • To quantify the impact of parameter uncertainty on microsimulation model outcomes, specifically prostate cancer overdiagnosis.
  • To develop and validate a computationally efficient emulator for uncertainty analysis.
  • To reduce the computational burden associated with probabilistic sensitivity analyses in cancer modeling.

Main Methods:

  • Utilized the microsimulation screening analysis (MISCAN) model for prostate cancer simulation.
  • Developed a Gaussian process regression emulator to approximate MISCAN's behavior.
  • Performed probabilistic sensitivity analyses (ProbSAs) using the emulator to assess parametric uncertainty's effect on overdiagnosis.
  • Evaluated emulator accuracy by comparing its predictions to MISCAN outputs and calculating prediction error.

Main Results:

  • The Gaussian process regression emulator reduced computation time for ProbSAs by over 85%.
  • The emulator achieved an average relative prediction error of 1.7% for overdiagnosis estimates.
  • Predicted that 42% of screen-detected men are overdiagnosed, with a confidence interval of 38%-48%.
  • Emulator accuracy was found to be sensitive to the selection of parameters used in training.

Conclusions:

  • Gaussian process regression emulators enable computationally feasible probabilistic sensitivity analyses for complex, parameter-heavy microsimulation models.
  • This approach significantly accelerates uncertainty quantification in cancer modeling, particularly for issues like prostate cancer overdiagnosis.
  • The developed emulation strategy provides a viable method for thoroughly examining model uncertainty within practical time constraints.