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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, USA.

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|June 11, 2024

Summary

This summary is machine-generated.

Artificial neural networks (ANNs) effectively emulate complex colorectal cancer (CRC) simulation models, reducing computational demands for Bayesian calibration. This approach aids health decision scientists in quantifying parameter uncertainty for policy analysis.

Keywords:

Bayesian calibration artificial neural networks colorectal cancer model emulator machine learning

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Area of Science:

Computational epidemiology
Health policy modeling
Biostatistics

Background:

Colorectal cancer (CRC) simulation models are crucial for policy analysis but computationally intensive.
Bayesian calibration of these models requires significant computational resources.
Existing methods for calibrating complex simulation models can be burdensome.

Purpose of the Study:

To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET)'s SimCRC, MISCAN-Colon, and CRC-SPIN models using an emulator-based Bayesian algorithm.
To internally validate the model-predicted outcomes against calibration targets.
To provide a practical solution for reducing computational burden in Bayesian calibration of individual-level simulation models.

Main Methods:

Utilized Latin hypercube sampling to generate parameter sets and model outputs.
Trained multilayer perceptron artificial neural networks (ANNs) as emulators for each CISNET-CRC model.
Implemented ANN emulators in a probabilistic programming language with Hamiltonian Monte Carlo-based algorithms for calibration.
Selected optimal ANN structures and hyperparameters based on minimizing validation errors.

Main Results:

Optimal ANNs were identified for SimCRC (4 hidden layers, 360 nodes), MISCAN-Colon (4 hidden layers, 114 nodes), and CRC-SPIN (1 hidden layer, 140 nodes).
Training and calibration times were significantly reduced (e.g., 4.0 hours for MISCAN-Colon).
Model-predicted outputs closely matched calibration targets, with high concordance rates across models (e.g., 98/110 for SimCRC).

Conclusions:

ANN emulators offer a practical and efficient method to reduce the computational burden associated with Bayesian calibration of complex CRC simulation models.
This approach facilitates the quantification of parameter uncertainty for health decision scientists.
The study presents a step-by-step guide for constructing ANN emulators for Bayesian calibration of individual-level simulation models.