Department of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK.
Pharmacoeconomics. 2011 Jan;29(1):35-49. doi: 10.2165/11584600-000000000-00000.
In economic evaluation, mathematical models have a central role as a way of integrating all the relevant information about a disease and health interventions, in order to estimate costs and consequences over an extended time horizon. Models are based on scientific knowledge of disease (which is likely to change over time), simplifying assumptions and input parameters with different levels of uncertainty; therefore, it is sensible to explore the consistency of model predictions with observational data. Calibration is a useful tool for estimating uncertain parameters, as well as more accurately defining model uncertainty (particularly with respect to the representation of correlations between parameters). Calibration involves the comparison of model outputs (e.g. disease prevalence rates) with empirical data, leading to the identification of model parameter values that achieve a good fit. This article provides guidance on the theoretical underpinnings of different calibration methods. The calibration process is divided into seven steps and different potential methods at each step are discussed, focusing on the particular features of disease models in economic evaluation. The seven steps are (i) Which parameters should be varied in the calibration process? (ii) Which calibration targets should be used? (iii) What measure of goodness of fit should be used? (iv) What parameter search strategy should be used? (v) What determines acceptable goodness-of-fit parameter sets (convergence criteria)? (vi) What determines the termination of the calibration process (stopping rule)? (vii) How should the model calibration results and economic parameters be integrated? The lack of standards in calibrating disease models in economic evaluation can undermine the credibility of calibration methods. In order to avoid the scepticism regarding calibration, we ought to unify the way we approach the problems and report the methods used, and continue to investigate different methods.
在经济评估中，数学模型作为一种整合疾病和健康干预相关信息的方法具有核心作用，以便在较长时间内估计成本和后果。模型基于对疾病的科学认识（这可能会随时间变化），对具有不同不确定性水平的简化假设和输入参数进行简化；因此，探索模型预测与观察数据的一致性是合理的。校准是估计不确定参数的有用工具，还可以更准确地定义模型不确定性（特别是在表示参数之间的相关性方面）。校准涉及模型输出（例如疾病流行率）与经验数据的比较，从而确定实现良好拟合的模型参数值。本文提供了有关不同校准方法理论基础的指导。校准过程分为七个步骤，讨论了每个步骤的不同潜在方法，重点关注经济评估中疾病模型的特定特征。这七个步骤是：（i）校准过程中应调整哪些参数？（ii）应使用哪些校准目标？（iii）应使用哪种拟合优度度量？（iv）应使用哪种参数搜索策略？（v）应使用什么来确定可接受的拟合优度参数集（收敛标准）？（vi）应使用什么来确定校准过程的终止（停止规则）？（vii）如何整合模型校准结果和经济参数？经济评估中校准疾病模型缺乏标准可能会削弱校准方法的可信度。为了避免对校准的怀疑，我们应该统一我们处理问题的方法并报告使用的方法，并继续研究不同的方法。
