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多重系统缩的成本效益建模用于早期健康技术评估

Tobias Sydendal Grand^1,2, Shijie Ren³, Praveen Thokala^3,4

¹Sheffield Centre for Health and Related Research (SCHARR), University of Sheffield, 30 Regent St, Sheffield City Centre, Sheffield, S1 4DA, UK. tsgrand1@sheffield.ac.uk.

PharmacoEconomics - open

|October 31, 2025

概括

多重系统缩 (MSA) 的健康经济数据有限,阻碍了早期健康技术评估. 对疾病类型的文献搜索和用户友好的建模接口可以帮助识别数据缺口,并指导未来对这种罕见疾病的证据生成.

科学领域:

卫生经济学卫生经济学
罕见疾病罕见疾病
神经退行性疾病神经退行性疾病

背景情况:

多重系统缩 (MSA) 是一种罕见的,快速进展的神经退行性疾病,具有重叠的小脑和帕金森运动表型.
对于MSA存在有限的健康经济数据,这给早期健康技术评估 (HTA) 带来了挑战.
早期HTA的可行性和疾病模拟模型对MSA等罕见疾病的实用性需要研究.

研究的目的:

评估对多个系统缩 (MSA) 进行早期健康技术评估的可行性.
探索文献搜索疾病类型的实用性,以尽量减少数据缺口,并促进MSA的成本效益建模.
评估开发用于罕见疾病健康技术评估的用户友好的模型接口的潜力.

主要方法:

对MSA进行了经济评估和健康经济参数 (成本,公用事业,自然史) 的文献搜索.
利用统一的多系统缩评级尺度IV部分 (全球残疾尺度) 来告知模型结构,由疾病模拟研究告知.
从英国国家卫生服务的角度构建了成本效益分析,使用R Shiny进行了用户友好的界面.

主要成果:

没有发现MSA的直接成本效益分析;然而,发现了有关疾病成本,生活质量和自然史的相关研究.
卫生经济参数,包括公用事业和过渡概率,使用注册表数据进行估计.
尽管数据有限,但开发了一个假设干预的成本效益分析,通过交互式R Shiny应用程序呈现.

结论:

对疾病类型的文献搜索对于MSA等罕见疾病的早期建模是有价值的,有助于识别数据缺口和指导证据生成.
来自疾病类型的替代数据可以在有足够的细粒度的情况下为模型结构提供信息.
早期的概念建模和代框架对于解决罕见疾病成本效益结果的不确定性至关重要.

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