使用权重决策错误率选择定量决策标准
1Statistics and Data Science Innovation Hub, GSK, Stevenage, UK.
Pharmaceutical statistics
|December 4, 2025
概括
这项研究引入了加权决策错误率 (WDER),以优化药物开发决策,最大限度地减少错误的去/不去错误. 它指导了第二阶段的样本大小和值选择,以实现更高效,更强大的药物开发.
科学领域:
- 药物开发 药物开发
- 决策分析 决策分析
- 生物统计学 生物统计学
背景情况:
- 药物开发涉及到关键的去/不去决策.
- 错误包括继续使用失败的药物 (错误的去) 或停止成功的药物 (错误的不去).
研究的目的:
- 在药物开发的2/3阶段,尽量减少错误的"去"和"不去"决定的综合风险.
- 在错误类型具有不同的成本时,引入加权决策错误率 (WDER) 来优化去值.
- 探索先前信念和权重对决策规则和第二阶段样本大小的影响.
主要方法:
- 开发一个定量决策框架.
- 定义和应用加权决策错误率 (WDER).
- 分析先前信念的影响,并对最佳决策规则进行权衡.
主要成果:
- 最佳的进度值可以最大限度地降低联合决策错误风险.
- WDER指导第二阶段的样本大小确定,通常增加大小和严格性.
- 之前的信念显著影响最佳决策规则.
结论:
- 对第二阶段成功概率的新定义应该侧重于正确的决策,而不仅仅是进步.
- 药物开发框架提高了药物开发决策的稳定性和定制性.
- 这种方法改善了阶段过渡的风险效益评估.
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