Model Parameter Estimation and Uncertainty Analysis: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-6
Model Parameter Estimation and Uncertainty Analysis: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-6
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DOI：
10.1177/0272989x12458348
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发表时间：
2012-09-01
影响因子：
3.6
通讯作者：
Paltiel, A. David
中科院分区：
文献类型：
--
作者：
Briggs, Andrew H.;Weinstein, Milton C.;Paltiel, A. David
模型的目的是为医疗决策和卫生保健资源分配提供信息。建模人员采用定量方法构建临床、流行病学和经济学证据基础，并获得定性见解，以帮助决策者做出更好的决策。从政策角度来看，基于模型的分析的价值不仅在于它能够为具体结果产生精确的点估计，而且还在于系统地审查和负责任地报告围绕这一结果和所处理的最终决定的不确定性。不同的概念有关的决策建模的不确定性进行了探讨。随机（一阶）不确定性与参数（二阶）不确定性和异质性不同，与模型本身相关的结构不确定性形成另一个层次的不确定性。本文认为，点估计和参数的不确定性的估计是一个单一的过程的一部分，并探讨了参数的不确定性之间的联系，通过决策的不确定性和信息价值分析的关系。本文还就不确定性的报告提出了广泛的建议，包括确定性敏感性分析技术和概率方法。期望值的完美信息被认为是最合适的表述技术，成本效益的可接受性曲线，代表决策的不确定性，从概率分析。
A model's purpose is to inform medical decisions and health care resource allocation. Modelers employ quantitative methods to structure the clinical, epidemiological, and economic evidence base and gain qualitative insight to assist decision makers in making better decisions. From a policy perspective, the value of a model-based analysis lies not simply in its ability to generate a precise point estimate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty surrounding this outcome and the ultimate decision being addressed. Different concepts relating to uncertainty in decision modeling are explored. Stochastic (first-order) uncertainty is distinguished from both parameter (second-order) uncertainty and from heterogeneity, with structural uncertainty relating to the model itself forming another level of uncertainty to consider. The article argues that the estimation of point estimates and uncertainty in parameters is part of a single process and explores the link between parameter uncertainty through to decision uncertainty and the relationship to value-of-information analysis. The article also makes extensive recommendations around the reporting of uncertainty, both in terms of deterministic sensitivity analysis techniques and probabilistic methods. Expected value of perfect information is argued to be the most appropriate presentational technique, alongside cost-effectiveness acceptability curves, for representing decision uncertainty from probabilistic analysis.