在测试负面设计中调整混的方法COVID-19有效性研究:模拟研究研究
Elizabeth Ak Rowley1, Patrick K Mitchell1, Duck-Hye Yang1
1Westat, Rockville, MD, United States.
JMIR formative research
|January 27, 2025
概括
多变量模型在COVID-19疫苗有效性研究中提供了比疾病风险得分模型更好的混调整. 这些发现有助于设计可靠的现实世界有效性评估.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 在现实世界中,COVID-19疫苗有效性 (VE) 研究面临复杂暴露和混因素的挑战.
- 倾向性评分方法对于二分法暴露是有效的,但对于多项性暴露则不那么有效.
研究的目的:
- 在使用测试负面设计的COVID-19 VE研究中比较替代混调整方法.
- 为了评估疾病风险评分 (DRS) 调整与多变量逻辑回归的性能.
主要方法:
- 使用了模拟数据集与多项疫苗接种暴露.
- 使用DRS对所有或关键共变量的多变量逻辑回归进行比较分层和直接调整.
主要成果:
- 多变量模型显示出最小的偏差 (-5.3%至6.1%) 和良好的覆盖概率 (93.7%至95.3%).
- 经DRS调整的模型偏差较低 (-2.2%至4.2%),但低估了标准误差,导致覆盖率较低 (87.8%-94.8%).
- 性能在各种建模策略和暴露组中各不相同.
结论:
- 多变量模型调整个人共变量表现得比DRS调整的模型更好.
- 对于复杂的VE研究,DRS调整足够,但不如多变量方法精确.
关键词:
在 COVID-19 疫情中,评估评估的评估评估的评估.伴随性疾病发生率.疾病风险得分的得分是疾病风险得分.倾向性得分是指倾向性得分.模拟研究是模拟研究.实用性 实用性的 实用性的 实用性的疫苗的有效性 疫苗的有效性更多相关视频
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