Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models

Carlos Pineda-Antunez, Claudia Seguin, Luuk A. van Duuren, Amy B. Knudsen, Barak Davidi, Pedro Nascimento de Lima, Carolyn Rutter, Karen M. Kuntz, Iris Lansdorp-Vogelaar, Nicholson Collier, Jonathan Ozik, Fernando Alarid Escudero

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET)’s SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets. Methods: We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANNs) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo–based algorithms to obtain the joint posterior distributions of the CISNET-CRC models’ parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets. Results: The optimal ANN for SimCRC had 4 hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had 1 hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 h for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN. Conclusions: Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, such as the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating 3 realistic CRC individual-level models using a Bayesian approach. We use artificial neural networks (ANNs) to build emulators that surrogate complex individual-based models to reduce the computational burden in the Bayesian calibration process. ANNs showed good performance in emulating the CISNET-CRC microsimulation models, despite having many input parameters and outputs. Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis. This work aims to support health decision scientists who want to quantify the uncertainty of calibrated parameters of computationally intensive simulation models under a Bayesian framework.

Original languageEnglish (US)
Pages (from-to)543-553
Number of pages11
JournalMedical Decision Making
Volume44
Issue number5
DOIs
StatePublished - Jul 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

Keywords

  • artificial neural networks
  • Bayesian calibration
  • colorectal cancer model
  • emulator
  • machine learning

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

Fingerprint

Dive into the research topics of 'Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models'. Together they form a unique fingerprint.

Cite this