安全数据中的统计信号检测算法:与行业标准方法相比,专有方法
Eugenia Bastos1, Jeff K Allen2, Jeff Philip3
1, Cambridge, MA, USA.
Pharmaceutical medicine
|July 13, 2024
概括
与标准方法相比,回归决策树 (RDT) 模型在检测药物不良反应 (ADR) 信号方面表现出优越性. 这种机器学习方法可以更快地检测并捕获更多的不良反应,从而改善药监信号检测系统.
科学领域:
- 药监和药物安全 药监和药物安全
- 在医疗保健中的数据科学.
- 监管科学 监管科学
背景情况:
- 在药物不良反应 (ADR) 中检测不成比例报告 (SDR) 信号的确立的定量方法存在.
- 然而,这些具有高度可变数据的信号检测算法 (SDA) 的有效性仍然不清楚.
研究的目的:
- 为Biogen的全球安全数据库 (GSD) 确定最佳的SDA.
- 将行业标准方法 (EBGM,EB05,PRR,ROR) 与一种新的机器学习 (ML) 回归决策树 (RDT) 模型进行比较.
- 根据数据库特征,如事件频率,数据偏差和缺失信息来确定Biogen产品的最佳SDA.
主要方法:
- 评估六个SDA,包括五种常见的不成比例方法和RTD模型.
- 对7种已销售的Biogen产品的2004-2019年季度报告间隔的分析.
- 性能指标包括灵敏度,精度,检测新事件的时间和检测病例的频率,通过错误分类率进行验证.
主要成果:
- 没有一个单一的SDA在所有产品中始终优于其他产品;性能各不相同,取决于信号定义值.
- RDT模型和MHRA算法在产品之间显示出优越和可比的性能.
- 在所有方法中都观察到精度的普遍降低,突出显示了对创新方法的需求.
结论:
- 对于SDR的不成比例统计的选择,应优先考虑易于实施和解释,因为它们不会限制可实现的性能.
- RDT模型在检测速度和捕获的ADR数量方面表现出优越性.
- 未来的工作包括将数据扩展到其他迹象,并在外部数据库中测试概括性.
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