在R (DESCIPHR) 中用于癌症干预和人口健康的离散事件模拟建模框架:一个开源的管道
Selina Pi1, Carolyn M Rutter2, Carlos Pineda-Antunez3
1Department of Biomedical Data Science, School of Medicine, Stanford University, 300 Pasteur, Edwards, Floor 3, Palo Alto, CA, 94304, USA. sjpi@stanford.edu.
PharmacoEconomics
|January 23, 2026
概括
我们开发了DESCIPHR,这是一个用于癌症模拟建模的开源R框架. 它集成了离散事件模拟 (DES) 和贝叶斯校准,以帮助癌症干预的卫生政策决策.
科学领域:
- 卫生政策 卫生政策
- 决策科学科学 决策科学
- 计算流行病学计算流行病学
背景情况:
- 模拟模型对于卫生政策至关重要,特别是对于癌症,这是全球主要的死亡原因.
- 离散事件模拟 (DES) 和贝叶斯校准是模拟复杂健康状况和量化不确定性的强大工具.
- 对于端到端的DES癌症建模和政策评估的贝叶斯校准现有的指导是有限的.
研究的目的:
- 介绍DESCIPHR,一个用于癌症干预和人口健康建模的开源R框架.
- 提供关于DES模型结构,使用贝叶斯校准进行参数估计和政策评估的端到端指导.
- 促进癌症干预的未来决策模型的开发.
主要方法:
- 开发了R (DESCIPHR) 中癌症干预和人口健康的DES建模框架.
- 集成了一个灵活的DES模型,用于癌症自然史与贝叶斯校准参数估计.
- 应用 DESCIPHR 校准膀和结直肠癌模型使用现实世界癌症注册数据.
主要成果:
- 通过校准膀和结直肠癌模型来证明框架的实用性.
- 引入了一种自动化方法,用于生成基于数据的参数先前分布.
- 改进了一个基于神经网络模拟器的贝叶斯校准算法,以提高功能.
结论:
- DESCIPHR提供了一个可适应的模板,用于在癌症研究中构建决策模型.
- 该框架支持对癌症干预和健康政策的强有力的评估.
- 有助于更好地了解癌症流行病学和政策决策中的数据不确定性.
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