过度分散的鱼类数据的预测间隔及其在医学和临床前质量控制中的应用
Max Menssen1, Martina Dammann2, Firas Fneish3
1Department of Biostatistics, Leibniz University Hannover, Hanover, Germany.
Pharmaceutical statistics
|October 30, 2024
概括
这项研究为过度分散的计数数据引入了新的预测间隔,为质量控制提供了改进的历史控制极限 (HCL). 引导校准可确保精确的错误控制,在模拟中表现优于传统方法.
科学领域:
- 生物统计学 生物统计学
- 统计过程控制 统计过程控制
- 质量控制 质量控制
背景情况:
- 历史控制极限 (HCL) 对于过程监测和验证临床前和医疗质量控制中的观察结果至关重要.
- 应用用于计数数据的传统HCL方法 (例如,艾姆斯试验,多发性硬化症复发) 经常与过度分散,右倾和不同集群大小作斗争.
研究的目的:
- 为过度分散的计数数据提出可靠的预测间隔作为有效的HCL.
- 开发一个启动校准算法,以准确控制1型错误率,特别是偏斜数据和可变基线量.
主要方法:
- 利用准波松分布和负二项式分布来建模过度分散的计数数据.
- 集成的偏移来处理可变的基线量 (例如,培养皿计数,监测时间).
- 开发并应用了一个引导校准算法,以确保相同的尾部概率和控制类型-1错误.
主要成果:
- 与蒙特卡洛模拟中的其他八种HCL方法相比,Bootstrap校准预测间隔证明了对1型错误的优越控制.
- 发现Shewhart图表 (c-或u-图表,平均 ± 2 SD) 等传统启发式测试不充分控制预先指定的覆盖概率.
- 提出的方法成功地应用于艾姆斯试验数据和多发性硬化症复发数量.
结论:
- 拟议的引导校准预测间隔提供了一个统计学上可靠和可靠的方法,用于通过过度分散的计数数据来确定HCL.
- 这种方法比传统方法有显著的改进,确保更好的过程稳定性评估和观察验证.
- 方法和校准算法可以通过R包"predint"访问.
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