概率性成本效益分析需要多少蒙特卡洛样本?
1Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA; Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA.
概括
在概率灵敏度分析 (PSA) 中确定样本数量 (N) 对于准确的成本效益估计至关重要. 本研究提供了方法,以确保N不随意选择,防止误导性结论在健康经济评估.
科学领域:
- 卫生经济学 卫生经济学
- 决策科学 决策科学 决策科学
- 生物统计学 生物统计学
背景情况:
- 概率灵敏度分析 (PSA) 量化了成本效益分析中的不确定性.
- PSA通常涉及采样输入参数 (N) 和复制随机模型 (P).
- 确定N和P的适当样本大小对于可靠的结果至关重要.
研究的目的:
- 研究最佳方法来确定PSA中的参数样本 (N) 和模型复制 (P) 的数量.
- 确保准确估计成本效益指标,如增量成本效益比率 (ICER).
- 为选择样本大小提供指导,以避免在经济评估中得出误导性结论.
主要方法:
- 证明模型复制 (P) 可以任意设置 (例如,P=1).
- 根据切比舍夫不等式推导出公式,以确定所需的参数样本数量 (N),以达到ICER所需的准确性.
- 拟议的视觉和定量方法来验证确定样本大小N的充分性.
主要成果:
- 任意选择的样本大小 (N) 可以导致显著不准确的ICER估计,即使增加模型复制 (P).
- 提出的基于Chebyshev的不平等方法提供了一个数据驱动的方法来确定N,最大限度地减少估计误差.
- 验证方法证实,计算的N确保了ICER估计在规定的准确度范围内.
结论:
- 在PSA中参数样本的数量 (N) 不应随意选择;它需要严格的确定.
- 该研究提出了方法,以确保足够的样本大小可靠的概率性成本效益分析.
- 坚持这些方法可以提高经济评估的有效性和可解释性.
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