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将元建模作为用于基于随机模型的成本效益分析的变量减小技术
Zongbo Li1, Gregory S Knowlton1, Margo M Wheatley1
1Division of Health Policy and Management, University of Minnesota, School of Public Health, Minneapolis, MN, USA.
在成本效益分析 (CEA) 中,随机噪声可能会掩盖结果. 超建模在模拟中减少了这种噪音,提高了概率灵敏度分析 (PSA) 的可靠性,而不会增加计算负担.
科学领域:
- 卫生经济学 卫生经济学
- 计算建模 计算建模
- 生物统计学 生物统计学
背景情况:
- 在成本效益分析 (CEA) 中的随机模型可以显示运行到运行的变化,在这种情况下,噪音超过干预效应,特别是具有较小的个人效率.
- 这种随机噪声使概率灵敏度分析 (PSA) 变得复杂,掩盖了参数不确定性对CEA结果的影响.
- 不直观的结果,如干预似乎减少质量调整寿命年 (QALYs),可能来自过度的随机噪音.
研究的目的:
- 评估元建模作为减小差异的技术,以减轻PSA中的随机噪声.
- 评估元建模是否可以在复杂的模拟模型中保持参数不确定性,同时减少噪声.
- 提高来自随机模型的CEA结果的可靠性和可解释性.
主要方法:
- 在两个模拟模型中应用了三种元建模技术 (线性回归,通用添加模型,人工神经网络):Sick-Sicker模型和基于代理的HIV传播模型.
- 在两个模型上进行了PSA,并使用验证数据集上的R平方和根平均平方误差 (RMSE) 评估了元模型的性能.
- 通过分析增量成本和QALY的分散图,成本效益可接受度曲线 (CEAC) 和非直观结果的频率来比较PSA结果.
主要成果:
- 超建模在Sick-Sicker模型中大大降低了增量成本和QALYs的差异,几乎消除了与良好模型匹配 (高R平方,低RMSE) 的非直观结果.
- 在基于艾滋病毒病原体的模型中,所有三个元模型都有效地减少了结果的变化,同时保持了参数不确定性.
- 超建模产生了更具信息性的CEAC,增加了在HIV模型中识别成本有效策略的可能性.
结论:
- 超建模是用于CEA的模拟模型中减少随机噪声的有效技术.
- 这种方法通过保持参数不确定性而提高PSA结果的可靠性,而不需要不切实际的模拟数量.
- 超建模改善了复杂的随机模型中CEA结果的解释性,为健康经济评估提供了有价值的工具.
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