在动态队列中处理疫苗类型缺失数据,以评估时间变化的疫苗接种与自身免疫性疾病之间的联系
Belén Castillo-Cano1, Marc Comas-Cufí2, Elisa Martín-Merino1
1Division of Pharmacoepidemiology and Pharmacovigilance, Spanish Agency of Medicines and Medical Devices (AEMPS), Madrid, Spain.
Pharmacoepidemiology and drug safety
|November 26, 2024
概括
在健康记录中缺少人类乳头瘤病毒 (HPV) 疫苗类型可能会对研究产生偏见. 使用关联因素的简单归算策略提高了HPV疫苗和甲状腺炎研究的准确性.
科学领域:
- 药学流行病学 药学流行病学
- 免疫学 免疫学 免疫学
- 公共卫生 公共卫生
背景情况:
- 准确的疫苗类型数据对于流行病学研究至关重要.
- 在健康记录中缺少疫苗信息可能会导致偏见的结果.
- 人类乳头瘤病毒 (HPV) 疫苗接种是一个关键的公共卫生干预措施.
研究的目的:
- 开发和评估一个简单的归算策略,用于医疗记录中缺少的人类乳头瘤病毒 (HPV) 疫苗类型.
- 评估归算对研究HPV疫苗接种与甲状腺炎之间的关联的影响.
- 为了将归算方法与完整案例和缺失类别分析进行比较.
主要方法:
- 一项使用西班牙女孩健康记录 (BIFAP) 的队列研究.
- 基于临床和非临床因素的HPV疫苗类型的随机推算.
- 计算后对危险比率 (HR) 的估计和与完整案例 (CC) 和缺失类别 (MiCat) 分析进行比较.
主要成果:
- 对35%缺乏疫苗类型的接种疫苗的女孩进行了输入.
- 确定了15个关联因素和4个混因素用于归算.
- 与CC和MiCat分析相比,归算方法显示偏差较小,HR在CC和MiCat中偏差高达24%和10%.
结论:
- 开发的归算策略简单,高效,并提高了遗漏随机假设的可信性.
- 完整病例和缺失类别分析可以在缺失疫苗数据时引入显著的偏差,而不是随机.
- 这种归算方法适用于药物流行病学中的时间变化的暴露.
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