Introduction. Calibration, a critical step in the development of simulation models, involves adjusting unobservable parameters to ensure that the outcomes of the model closely align with observed target data. This process is particularly vital in cancer simulation models with a natural history component, where direct data to inform natural history parameters are rarely available. Methods. We conducted a scoping review of studies published from 1980 to August 11, 2024, using keyword searches in PubMed and Web of Science. Eligible studies included cancer simulation models with a natural history component that used calibration methods for parameter estimation. Results. A total of 117 studies met the inclusion criteria. Nearly all studies (n = 115) specified calibration targets, while most studies (n = 91) described the parameter search algorithms used. Goodness-of-fit metrics (n = 87), acceptance criteria (n = 53), and stopping rule (n = 46) were reported less frequently. The most commonly used calibration targets were incidence, mortality, and prevalence, typically drawn from cancer registries and observational studies. Mean squared error was the most commonly used goodness-of-fit measure. Random search was the predominant method for parameter search, followed by the Bayesian approach and the Nelder–Mead method. Discussion. Despite recent advances in machine learning, such algorithms remain underutilized in the calibration of cancer simulation models. Further research is needed to compare the efficiency of different parameter search algorithms used for calibration.
Highlights
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: cancer type, calibration target data source, calibration target type, goodness-of-fit metrics, search algorithms, acceptance criteria, stopping rule, computational time, modeling approach, and model stochasticity.
Random search has been the predominant method for parameter search, followed by Bayesian approach and Nelder–Mead method.
Machine learning–based algorithms, despite their fast advancement in the recent decade, have 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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