关于将集群随机试验纳入元分析的警告:模拟研究结果
Joseph Alvin Ramos Santos1,2, Emilia Riggi3, Gian Luca Di Tanna3
1Department of Business Economics, Health and Social Care (DEASS), University of Applied Sciences and Arts of Southern Switzerland (SUPSI), Manno, Ticino, Switzerland. joseph.santos@supsi.ch.
在元分析中不适当分析集群随机试验 (CRT) 会导致假阳性结果膨胀. 正确分析CRT对于准确的治疗效果结论至关重要.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 健康研究方法 卫生研究方法
背景情况:
- 随机对照试验 (RCT) 提供了高水平的证据,但当个人随机化不切实际时,集群随机试验 (CRT) 往往是必要的.
- 可以将CRT纳入与RCT的元分析中,但需要适当的统计方法来考虑聚类.
- 在元分析中对CRT进行不正确的分析可能会导致结果偏差并影响证据合成.
研究的目的:
- 检查错误分析的CRT对元分析结果的影响.
- 在元分析中模拟不同数量的正确和不正确分析CRT的场景.
- 评估分析方法对元分析结果可靠性的影响.
主要方法:
- 生成模拟RCT和CRT数据集,具有零治疗效果.
- 使用标准线性回归 (不正确) 和混合效应回归 (正确) 分析了CRT数据集.
- 创建了具有不同比例正确分析CRT的元分析数据集,并对每个场景进行了1000个随机效应元分析.
主要成果:
- 正确分析CRT导致统计学显著结果的百分比明显较低 (p < 0.05).
- 假阳性率 (超出阿尔法值) 在错误分析CRT时 (72.84%至80.25%) 与正确分析 (2.47%) 相比显著更高.
- 虽然效果大小和偏差相似,但正确分析的CRT产生了更高的覆盖概率和基于模型的标准错误.
结论:
- 在元分析中忽视CRT的集群性质会导致对治疗疗效的高估 (膨胀的假阳性结论).
- 适当的统计方法对于准确地将CRT纳入元分析至关重要.
- 在合成CRT证据时建议谨慎,以确保研究结果的有效性.
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