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最大的顺序概率比率测试回归试验
Ivair R Silva1,2, Joselito Montalban3, Fernando L P de Oliveira4
1Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA 02215, United States.
Biometrics
|December 29, 2025
概括
这项研究引入了一种新的顺序回归测试,用于监测药物和疫苗的安全性. 该方法考虑了混变量,提高了在市场后监测中检测不良事件的准确性.
科学领域:
- 药物监督 药物监督 药物监督
- 生物统计学 生物统计学
- 流行病学 流行病学
背景情况:
- 推销后对药物和疫苗安全性的连续监测至关重要.
- 像MaxSPRT和CMaxSPRT这样的现有方法可能无法完全考虑对不良事件风险的共变效应.
- 性别和年龄等混变量可以影响事件的质量和风险.
研究的目的:
- 为分析安全数据引入一种新的顺序回归测试.
- 在MaxSPRT和CMaxSPRT框架内容纳可观测的共变量.
- 通过对混因素进行调整,提高在市场后监测中检测不良事件的准确性.
主要方法:
- 为二项式和波桑数据开发一个序列回归测试.
- 将回归结构应用于MaxSPRT和CMaxSPRT.
- 历史和监测Poisson数据与异质基线率的比较.
- 包括季节性和其他可观测的混共变量.
主要成果:
- 拟议的顺序回归试验有效地纳入了共变量调整.
- 该方法适用于MaxSPRT和CMaxSPRT,提高了它们的效用.
- 使用真实世界的数据显示了监测疫苗不良事件的潜力.
结论:
- 序列回归试验为药监测提供了更强大的方法.
- 调整混变量导致更可靠的安全信号检测.
- 该方法在公共卫生监测中具有实际应用,以曼尼托巴省的疫苗安全监测为例.
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