Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models.
Carlos Pineda-AntunezClaudia SeguinLuuk A van DuurenAmy B KnudsenBarak DavidiPedro Nascimento de LimaCarolyn RutterKaren M KuntzIris Lansdorp-VogelaarNicholson CollierJonathan OzikFernando Alarid-EscuderoPublished in: Medical decision making : an international journal of the Society for Medical Decision Making (2024)
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.