Health Technology and Services Research Department, Faculty of Behavioural, Management and Social Sciences, Technical Medical Centre, University of Twente, Enschede, Overijssel, the Netherlands.
Cancer Health Services Research Unit, School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia.
Med Decis Making. 2020 Apr;40(3):348-363. doi: 10.1177/0272989X20912233.
Metamodels can be used to reduce the computational burden associated with computationally demanding analyses of simulation models, although applications within health economics are still scarce. Besides a lack of awareness of their potential within health economics, the absence of guidance on the conceivably complex and time-consuming process of developing and validating metamodels may contribute to their limited uptake. To address these issues, this article introduces metamodeling to the wider health economic audience and presents a process for applying metamodeling in this context, including suitable methods and directions for their selection and use. General (i.e., non-health economic specific) metamodeling literature, clinical prediction modeling literature, and a previously published literature review were exploited to consolidate a process and to identify candidate metamodeling methods. Methods were considered applicable to health economics if they are able to account for mixed (i.e., continuous and discrete) input parameters and continuous outcomes. Six steps were identified as relevant for applying metamodeling methods within health economics: 1) the identification of a suitable metamodeling technique, 2) simulation of data sets according to a design of experiments, 3) fitting of the metamodel, 4) assessment of metamodel performance, 5) conducting the required analysis using the metamodel, and 6) verification of the results. Different methods are discussed to support each step, including their characteristics, directions for use, key references, and relevant R and Python packages. To address challenges regarding metamodeling methods selection, a first guide was developed toward using metamodels to reduce the computational burden of analyses of health economic models. This guidance may increase applications of metamodeling in health economics, enabling increased use of state-of-the-art analyses (e.g., value of information analysis) with computationally burdensome simulation models.
可变形模型可用于降低计算密集型模拟模型分析的计算负担，尽管在健康经济学中的应用仍然很少。除了对其在健康经济学中的潜在用途缺乏认识外，缺乏关于开发和验证可变形模型的复杂和耗时过程的指导，可能也是导致其应用有限的原因之一。为了解决这些问题，本文向更广泛的健康经济学受众介绍了可变形建模，并介绍了在这种情况下应用可变形建模的过程，包括适合的方法和选择和使用这些方法的方向。一般（即非健康经济特定）的可变形建模文献、临床预测建模文献和之前发表的文献综述被利用来整合一个过程，并确定候选的可变形建模方法。如果方法能够处理混合（即连续和离散）输入参数和连续结果，则认为它们适用于健康经济学。确定了六个步骤与在健康经济学中应用可变形建模方法相关：1）识别合适的可变形建模技术，2）根据实验设计模拟数据集，3）拟合可变形模型，4）评估可变形模型性能，5）使用可变形模型进行所需的分析，6）验证结果。不同的方法被讨论以支持每个步骤，包括它们的特点、使用方向、关键参考文献以及相关的 R 和 Python 包。为了解决关于可变形建模方法选择的挑战，开发了第一个指南，用于使用可变形模型来降低健康经济模型分析的计算负担。该指南可以增加可变形建模在健康经济学中的应用，使具有计算负担的模拟模型能够更广泛地应用最新的分析方法（例如，信息价值分析）。
