无论如何,谁需要真实数据呢? 在卫生干预措施的经济评估中探索合成数据的使用
N van der Linden1, X G L V Pouwels2, B Jahn3
1Multi-Actor Systems Department, Delft University of Technology, Delft, The Netherlands.
概括
合成数据可以帮助医疗保健经济评估,但结果可能与原始数据不同,特别是较小的数据集. 仔细的验证对于可靠的政策决策至关重要.
科学领域:
- 卫生经济学 卫生经济学
- 数据科学数据科学数据科学
- 生物统计学 生物统计学
背景情况:
- 由于数据隐私和保密性限制,医疗保健经济评估面临着挑战.
- 综合数据提供了一个潜在的解决方案,以提高经济评估的可访问性.
- 合成数据在健康经济建模中的可靠性需要进行彻底的调查.
研究的目的:
- 评估使用合成数据对健康经济评估结果的影响.
- 为了比较使用原始与合成数据集的经济评估结果.
- 评估数据集大小和合成方法对合成数据可靠性的影响.
主要方法:
- 基于模型的经济评估进行了比较"shamectomy"与"常规护理"的虚构疾病.
- 用1000名患者的原始数据 (Dorg) 来生成合成数据集 (Dsyn).
- 在各种场景中评估了结果的相似性 (ICER),包括数据集大小,合成方法和缺失数据.
主要成果:
- 来自合成数据的ICER与基准情景中的原始数据 (每QALY25.857欧元与25.848欧元) 密切匹配.
- 与原始数据相比,大量 (15%) 的合成数据集导致了与原始数据相比不同的报销决定.
- 较小的原始数据集 (n=50,n=500) 导致ICER信任区间大幅扩大,表明不确定性增加.
结论:
- 合成数据可以产生与健康经济评估的原始数据相似的结果,但可能会出现偏差.
- 对于小于1000名患者的数据集,这是常见的,合成数据可能会导致实质性的偏差和潜在的低于最佳的政策决策.
- 对于基于模型的健康经济评估,建议使用大量与原始数据大小相匹配的合成数据集 (>100) 的逐步方法.
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