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贝叶斯安全监测与自适应偏差校正
Fan Bu1,2, Martijn J Schuemie1,3, Akihiko Nishimura4
1Department of Biostatistics, University of California, Los Angeles, California, USA.
Statistics in medicine
|November 27, 2023
概括
一个新的贝叶斯监测程序通过减少偏差和多重测试错误来改善疫苗安全监测. 与标准方法相比,这种方法提供更快的信号检测和更准确的估计.
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
- 现实世界的证据分析分析.
背景情况:
- 销售后疫苗安全监测对于大规模疫苗接种计划至关重要.
- 现有的方法,如最大化顺序概率比率测试 (MaxSPRT),面临多重测试和混偏差的挑战.
- 由于MaxSPRT的严格框架,因此需要预先规定的监控时间表.
研究的目的:
- 制定一个灵活的贝叶斯监测程序,解决疫苗安全监测中的偏见和多重测试问题.
- 通过在贝叶斯等级模型中分析负控制结果来减轻偏见.
- 为了提高灵活性和安全信号的顺序检测,使用更新后面概率.
主要方法:
- 开发了一种贝叶斯式监控程序,将从负控结果的经验偏差分布纳入其中.
- 采用贝叶斯的等级模型来估计疫苗对不良事件的影响.
- 使用更新后面概率进行顺序安全信号检测.
- 使用六个美国观察医疗数据库 (超过3.6亿患者) 和两种流行病学设计 (历史比较器,自我控制病例系列) 对MaxSPRT进行了评估.
主要成果:
- 与MaxSPRT相比,拟议的贝叶斯程序大大降低了1型错误率.
- 它保持了高的统计能力,并实现了更快的安全信号检测.
- 对疫苗对不良事件的影响估计的准确性大大提高.
- 经验评估涉及超过700万个结果集.
结论:
- 新的贝叶斯监测程序为推广后的疫苗安全监测提供了比MaxSPRT更灵活,更准确的替代方案.
- 它有效地解决了偏见和多重测试的关键挑战,提高了现实世界的安全数据分析的可靠性.
- 开源的R包"EvidenceSynthesis"和R ShinyApp可用于方法实现和结果可视化.
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