Abstract
This work reviewed the literature of cancer simulation models with a natural history component and identified the calibration approaches used in these models with respect to the following attributes: calibration target, goodness-of-fit (GOF) measure, parameter search algorithm, acceptance criteria, and stopping rules.Random search has been the predominant method for parameter search, followed by grid search and Nelder-mead method.Machine learning-based algorithms, despite their fast advancement in the recent decade, has been underutilized in the cancer simulation models. Furthermore, more research is needed to compare different parameter search algorithms used for calibration.
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