Xie Xuanqian, Schaink Alexis K, Gajic-Veljanoski Olga, Yeung Man Wah, Wang Myra, Li Chunmei, Ungar Wendy J
Health Technology Assessment Program, Ontario Health, Toronto, ON, Canada.
Centre for Vaccine and Therapeutics Readiness, Public Health Agency of Canada, Toronto, ON, Canada.
Expert Rev Pharmacoecon Outcomes Res. 2025 Feb;25(2):123-135. doi: 10.1080/14737167.2024.2416255. Epub 2024 Oct 21.
Probabilistic analysis, also referred to as probabilistic sensitivity analysis (PSA), is used extensively in cost-effectiveness evaluations of health technologies. We present methodological guidance for implementing probabilistic analysis and interpreting its results for policy and decision-making.
We review the methodological issues related to common practices in probabilistic analysis, explore aspects that are currently not widely addressed in the health economics literature, and provide an overview of recent methodological developments.
We use examples to highlight the advantages and disadvantages of common tools used for presenting probabilistic analysis results, including the cost-effectiveness acceptability curve (CEAC), cost-effectiveness acceptability frontier (CEAF), and value of information (VOI) analysis. We raise and address issues related to using Monte Carlo standard error to determine the number of iterations required, the implications of large uncertainty, and the credibility and meaningfulness of small differences in quality-adjusted life-years (QALYs). We then discuss evolving methods in probabilistic analysis, cautious uses of probabilistic analysis, and factors impacting parameter uncertainty.
A deeper understanding of probabilistic analysis methods enables health economists and decision-makers to more effectively address and interpret parameter uncertainty in health economic evaluations, which is essential for making informed policy decisions.
概率分析，也称为概率敏感性分析（PSA），在卫生技术的成本效益评估中被广泛使用。我们提供了实施概率分析以及为政策和决策解释其结果的方法学指导。
我们回顾了与概率分析常见做法相关的方法学问题，探讨了卫生经济学文献中目前未广泛涉及的方面，并概述了近期的方法学发展。
我们通过实例突出了用于呈现概率分析结果的常用工具的优缺点，包括成本效益可接受性曲线（CEAC）、成本效益可接受性前沿（CEAF）和信息价值（VOI）分析。我们提出并解决了与使用蒙特卡罗标准误差来确定所需迭代次数、大不确定性的影响以及质量调整生命年（QALYs）中微小差异的可信度和意义相关的问题。然后，我们讨论了概率分析中的不断发展的方法、概率分析的谨慎使用以及影响参数不确定性的因素。
对概率分析方法有更深入的理解，使卫生经济学家和决策者能够更有效地处理和解释卫生经济评估中的参数不确定性，这对于做出明智的政策决策至关重要。
