Bayesian Methods for Calibrating Health Policy Models: A Tutorial.
Bayesian Methods for Calibrating Health Policy Models: A Tutorial.
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DOI：
10.1007/s40273-017-0494-4
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发表时间：
2017-06
影响因子：
4.4
通讯作者：
Kim JJ
中科院分区：
文献类型：
--
作者：
Menzies NA;Soeteman DI;Pandya A;Kim JJ
数学模拟模型通常用于为卫生政策决策提供信息。这些健康政策模型代表了决定健康和经济结果的社会和生物机制，结合了关于政策替代方案将如何影响这些结果的多种证据来源，并将结果综合为政策决定的重要摘要措施。校准这些卫生政策模型以符合经验数据，可以提供表面有效性，并提高模型预测的质量。贝叶斯方法为模型校正提供了强有力的工具。这些方法将与特定政策决策相关的信息总结为(I)模型参数的先验分布，(Ii)模型的结构假设，以及(Iii)根据校准数据创建的似然函数，通过贝叶斯定理组合这些不同的证据来源。本文提供了贝叶斯模型校准方法的教程，描述了贝叶斯校准方法的理论基础，以及在创建校准目标、估计后验分布和获得用于决策的结果的任务中出现的实用考虑因素。这些考虑因素以及实施校准的具体步骤在一个扩展的工作例子的背景下描述，该例子涉及为假想的传染病提供(或不提供)治疗的政策选择。考虑到创建先验分布、模型结构和可能性所需的许多简化和主观决定，校准应被视为创建为政策提供有效证据的合理模型的练习，而不是识别唯一的、理论上最佳的证据摘要的技术。
Mathematical simulation models are commonly used to inform health policy decisions. These health policy models represent the social and biological mechanisms that determine health and economic outcomes, combine multiple sources of evidence about how policy alternatives will impact those outcomes, and synthesize outcomes into summary measures salient for the policy decision. Calibrating these health policy models to fit empirical data can provide face validity and improve the quality of model predictions. Bayesian methods provide powerful tools for model calibration. These methods summarize information relevant to a particular policy decision into (i) prior distributions for model parameters, (ii) structural assumptions of the model, and (iii) a likelihood function created from the calibration data, combining these different sources of evidence via Bayes’ theorem. This paper provides a tutorial on Bayesian approaches for model calibration, describing the theoretical basis for Bayesian calibration approaches as well as pragmatic considerations that arise in the tasks of creating calibration targets, estimating the posterior distribution, and obtaining results to inform the policy decision. These considerations, as well as the specific steps for implementing the calibration, are described in the context of an extended worked example about the policy choice to provide (or not provide) treatment for a hypothetical infectious disease. Given the many simplifications and subjective decisions required to create prior distributions, model structure, and likelihood, calibration should be considered an exercise in creating a reasonable model that produces valid evidence for policy, rather than as a technique for identifying a unique, theoretically optimal summary of the evidence.