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Related Experiment Video

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Emulator-based Bayesian calibration of the CISNET colorectal cancer models.

Carlos Pineda-Antunez1, Claudia Seguin2, Luuk A van Duuren3

  • 1The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, University of Washington, Seattle, WA, United States.

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Summary

This study calibrated colorectal cancer (CRC) simulation models using artificial neural network (ANN) emulators and Bayesian methods. The approach successfully reduced computational complexity for CRC policy analysis.

Keywords:
Bayesian calibrationartificial neural networkscolorectal cancer modelemulatormachine learning

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Area of Science:

  • Computational biology
  • Epidemiology
  • Biostatistics

Background:

  • Colorectal cancer (CRC) modeling is crucial for policy analysis.
  • Existing simulation models (SimCRC, MISCAN-Colon, CRC-SPIN) require computationally intensive calibration.
  • Bayesian calibration methods are effective but complex for individual-level simulation models.

Approach:

  • Developed artificial neural network (ANN) emulators to approximate CISNET CRC models.
  • Employed Latin hypercube sampling and multilayer perceptron ANNs for emulator training.
  • Utilized Hamiltonian Monte Carlo algorithms for Bayesian calibration of model parameters.

Key Points:

  • Optimal ANN structures were identified for SimCRC, MISCAN-Colon, and CRC-SPIN.
  • Emulator training and calibration times were significantly reduced.
  • Calibrated models demonstrated high accuracy, with most predicted outputs within calibration target confidence intervals.

Conclusions:

  • ANN emulators offer a practical and efficient solution for Bayesian calibration of complex simulation models.
  • This methodology streamlines the calibration process for CRC individual-level models used in policy research.
  • The study provides a guide for constructing emulators for Bayesian calibration of simulation models.