对过度分散的二项终点的预测间隔及其应用于毒理学历史控制数据的应用
Max Menssen1, Jonathan Rathjens2
1Department of Biostatistics, Leibniz University Hannover, Hannover, Germany.
Pharmaceutical statistics
|September 14, 2025
概括
新的预测间隔提高了使用历史控制数据的毒理学研究验证. 频率主义方法最好控制1型错误,为分析二分结果提供了传统启发式方法的可靠替代方案.
科学领域:
- 毒理学 毒理学 毒理学
- 生物统计学 生物统计学
- 统计建模 统计建模
背景情况:
- 在毒理学研究中,使用历史控制数据 (HCD) 验证并发对照组是必不可少的.
- 历史控制极限 (HCL) 通常用于验证,但往往无法解释二分式HCD中的过度分散和斜率.
- 现有的HCL启发式方法在实际应用中可能无法充分控制1型错误.
研究的目的:
- 提出和评估新的预测间隔,以验证与二元化的历史控制数据并发的对照组.
- 为了比较提议的频率主义和贝叶斯预测间隔与传统的启发式HCL方法的性能.
- 评估不同统计方法的1型错误控制和覆盖概率.
主要方法:
- 开发了四个预测间隔:两个频率主义和两个贝叶斯主义.
- 综合的蒙特卡洛模拟来比较覆盖概率和1型错误率.
- 拟议方法应用于来自致癌性研究的真实世界历史控制数据.
主要成果:
- 频率主义者启动式校准预测间隔证明了对1型错误的优越控制.
- 从通用线性混合模型获得的贝叶斯预测间隔被发现是实际适用的.
- 传统的启发式方法 (历史范围,np-chart极限,平均±2SD) 始终未能控制1型错误.
结论:
- 频率主义预测间隔提供了一种强大而可靠的方法,用于验证二分化HCD的毒理学研究中的并发控制.
- 贝叶斯方法提供了一个可行的替代方案,特别是在处理复杂的数据结构时.
- 该研究强调了当前启发式HCL方法的不足,并建议采用验证的预测间隔来提高研究可靠性.
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