贝西斯人学习COVID-19疫苗安全性,同时包括不良事件的病理学
Bangyao Zhao1, Yuan Zhong1, Jian Kang1
1Department of Biostatistics, University of Michigan.
The annals of applied statistics
|September 16, 2024
概括
一个新的贝叶斯图模型,BGrass,通过考虑不良事件 (AE) 之间的关系来改进COVID-19疫苗安全分析. 它发现,与流感疫苗相比,在接种COVID-19疫苗后,血液凝块的发生频率更高.
科学领域:
- 生物统计学 生物统计学
- 药物监督 药物监督 药物监督
- 流行病学 流行病学
背景情况:
- 对于疫苗安全性的公众信心对于疫情控制至关重要.
- 在VAERS数据库中用于疫苗安全性分析的现有统计方法往往忽略了不良事件 (AE) 之间的固有关系.
- 了解AE相互依存性可以提高信号检测,减少疫苗安全监测中的错误阳性.
研究的目的:
- 引入一种新的贝叶斯图形辅助信号选择 (BGrass) 模型,用于分析疫苗安全数据.
- 将不良事件本体学和网络依赖性纳入统计建模.
- 为了减轻报告偏见,并确定有关AE集团.
主要方法:
- 使用完全贝叶斯推理框架开发了BGrass模型.
- 纳入了负控制方法来报告偏差缓解和AE组检测的丰富方法.
- 使用高效的吉布斯采样器进行后置计算,并通过模拟评估性能.
主要成果:
- 应用BGrass分析了大约100万份疫苗不良事件报告系统 (VAERS) 报告.
- 与流感疫苗相比,在接种COVID-19疫苗后报告的血块 (深静脉血栓,血栓,肺栓塞) 更频繁.
- 对约翰逊和约翰逊-詹森COVID-19疫苗与基于mRNA的疫苗的血液凝块报告频率较高.
结论:
- BGrass模型通过利用AE关系有效分析疫苗安全数据.
- 研究结果表明,COVID-19疫苗,特别是詹森疫苗,与血液凝块报告增加之间存在潜在的关联.
- 有BGrass的R包可用于促进疫苗安全性评估.
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