Zhang Yichi, Alagoz Oguzhan
medRxiv. 2024 Nov 19:2024.11.18.24317357. doi: 10.1101/2024.11.18.24317357.
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. This work reviews the literature of cancer simulation models with a natural history component and identifies 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. After a comprehensive search of the PubMed database from 1981 to June 2023, 68 studies were included in the review. Nearly all (n=66) articles specified the calibration targets, and most articles (n=56) specified the parameter search algorithms they used, whereas goodness-of-fit metric (n=51) and acceptance criteria/stopping rule (n=45) were reported for fewer times. The most frequently used calibration targets were incidence, mortality, and prevalence, whose data sources primarily come from cancer registries and observational studies. The most used goodness-of-fit measure was weighted mean squared error. 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. More research is needed to compare different parameter search algorithms used for calibration.
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.
校准是模拟模型开发中的关键步骤，涉及调整不可观测参数，以确保模型结果与观测到的目标数据紧密匹配。在具有自然史成分的癌症模拟模型中，这一过程尤为重要，因为很少有直接数据可用于确定自然史参数。这项工作回顾了具有自然史成分的癌症模拟模型的文献，并根据以下属性确定了这些模型中使用的校准方法：校准目标、拟合优度（GOF）度量、参数搜索算法、接受标准和停止规则。在对1981年至2023年6月的PubMed数据库进行全面搜索后，68项研究被纳入综述。几乎所有（n = 66）文章都指定了校准目标；大多数文章（n = 56）指定了所使用的参数搜索算法；而拟合优度度量（n = 51）和接受标准/停止规则（n = 45）的报道次数较少。最常用的校准目标是发病率、死亡率和患病率，其数据来源主要来自癌症登记处和观察性研究。最常用的拟合优度度量是加权均方误差。随机搜索一直是参数搜索的主要方法，其次是网格搜索和Nelder-mead方法。基于机器学习的算法尽管在近十年中发展迅速，但在癌症模拟模型中却未得到充分利用。需要更多的研究来比较用于校准的不同参数搜索算法。
这项工作回顾了具有自然史成分的癌症模拟模型的文献，并根据以下属性确定了这些模型中使用的校准方法：校准目标、拟合优度（GOF）度量、参数搜索算法、接受标准和停止规则。随机搜索一直是参数搜索的主要方法，其次是网格搜索和Nelder-mead方法。基于机器学习的算法尽管在近十年中发展迅速，但在癌症模拟模型中却未得到充分利用。此外，需要更多的研究来比较用于校准的不同参数搜索算法。
