范围(计算机科学)
不确定度分析
管理科学
计算机科学
决策分析
敏感性分析
检查表
风险分析(工程)
不确定度量化
经济
数学
统计
业务
心理学
机器学习
认知心理学
程序设计语言
模拟
作者
Joke Bilcke,Philippe Beutels,Marc Brisson,Mark Jit
标识
DOI:10.1177/0272989x11409240
摘要
Accounting for uncertainty is now a standard part of decision-analytic modeling and is recommended by many health technology agencies and published guidelines. However, the scope of such analyses is often limited, even though techniques have been developed for presenting the effects of methodological, structural, and parameter uncertainty on model results. To help bring these techniques into mainstream use, the authors present a step-by-step guide that offers an integrated approach to account for different kinds of uncertainty in the same model, along with a checklist for assessing the way in which uncertainty has been incorporated. The guide also addresses special situations such as when a source of uncertainty is difficult to parameterize, resources are limited for an ideal exploration of uncertainty, or evidence to inform the model is not available or not reliable. Methods for identifying the sources of uncertainty that influence results most are also described. Besides guiding analysts, the guide and checklist may be useful to decision makers who need to assess how well uncertainty has been accounted for in a decision-analytic model before using the results to make a decision.
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