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在量化利益风险评估中纳入先前数据:贝叶斯方法的案例研究
Sai Dharmarajan1, Zhong Yuan2, Yeh-Fong Chen3
1Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA. sai.h.dharmarajan@gmail.com.
Therapeutic innovation & regulatory science
|January 24, 2024
概括
本研究引入了贝叶斯方法,将先前的数据整合到量化利益风险评估 (qBRA) 中,使用多重标准决策分析 (MCDA) 和随机多标准可接受性分析 (SMAA). 该方法通过结合外部信息来加强医疗产品的决策.
科学领域:
- 决策分析 决策分析
- 生物统计学 生物统计学
- 药物经济学 药物经济学
背景情况:
- 目前的多重标准决策分析 (MCDA) 和随机多重标准可接受性分析 (SMAA) 缺乏整合先前或外部利益风险数据的方法.
- 医疗产品的量化利益风险评估 (qBRA) 需要强大的方法来整合多种数据来源.
研究的目的:
- 展示一种新的贝叶斯混合模型方法,用于将先前数据纳入MCDA和SMAA.
- 通过利用外部信息,加强医疗产品的量化利益风险评估 (qBRA).
主要方法:
- 在贝叶斯框架内实施了MCDA和SMAA.
- 利用了关于益处和风险属性的混合先验,将之前的研究信息与模糊的先验混合在一起.
- 使用混合比例参数改变了借用数据的程度.
主要成果:
- 一项使用里瓦罗克萨班治疗外围动脉疾病 (PAD) 的案例研究证明了该方法的实用性.
- 纳入30%的先前数据有利地转移了Rivaroxaban的MCDA/SMAA结果.
- 该方法解决了试验数据中的差异,例如全因死亡率.
结论:
- 开发的贝叶斯方法用于将先前数据纳入MCDA和SMAA是用户友好和可解释的.
- 可用的RShiny应用程序促进了这种贝叶斯方法的应用,用于增强的qBRA.
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