统计方法的比较,以整合现实世界的证据在一个罕见的事件随机对照试验的元分析随机对照试验
Minghong Yao1,2,3, Yuning Wang1,2,3, Yan Ren1,2,3
1Institute of Integrated Traditional Chinese and Western Medicine and Chinese Evidence-Based Medicine Center and Cochrane China Center and MAGIC China Center, West China Hospital, Sichuan University, Chengdu, China.
Research synthesis methods
|June 13, 2023
概括
将随机对照试验 (RCT) 与现实世界的证据 (RWE) 结合起来,可以改善罕见事件分析. 偏差纠正的元分析模型在罕见事件研究中表现出卓越的性能.
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:
- 使用随机对照试验 (RCT) 进行的罕见事件元分析由于结果不频繁,因此经常不足.
- 来自非随机研究的真实世界证据 (RWE) 为罕见事件提供了补充的见解,增加了人们对将其纳入决策的兴趣.
- 对于结合RCT和RWE的现有方法的比较性能仍然不完全理解.
研究的目的:
- 评估和比较各种贝叶斯方法的性能,将RWE纳入来自RCT的罕见事件的元分析.
- 评估单独用于评估罕见事件的RCT数据的可靠性.
- 在罕见事件元分析中确定结合RCT和RWE最有效的方法.
主要方法:
- 进行了一项模拟研究,以评估多种贝叶斯方法:天真数据合成,设计调整合成,RWE作为先前信息,三级层次模型和偏差纠正的元分析模型.
- 使用百分比偏差,根-平均-平方-误差,可信的间隔宽度,覆盖概率和功率来衡量性能.
- 使用对使用/葡萄糖共运输体2抑制剂的糖尿病酸性症风险的系统审查来说明方法.
主要成果:
- 偏差纠正的元分析模型在各种模拟场景和性能指标中始终与其他方法相比或比其他方法更好.
- 模拟表明,单独的RCT数据可能不足以可靠地评估罕见事件的影响.
- 整合RWE可以提高罕见事件的证据的确定性和全面性.
结论:
- 偏差纠正元分析模型是采用RWE的罕见事件元分析的首选方法.
- 现实世界的证据显著加强了关于罕见事件的随机对照试验结果的稳定性.
- 这项研究为在临床研究中更可靠地评估罕见事件风险提供了框架.
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