连续性校正方法在Cochrane审查中的影响与罕见事件的单零试验:一项超流行病学研究
Yasushi Tsujimoto1,2,3,4, Yusuke Tsutsumi4,5,6, Yuki Kataoka4,7,8,9
1Department of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan.
Research synthesis methods
|May 16, 2024
概括
固定连续性校正方法,经常用于元分析,可以偏差结果,特别是在单零研究. 其他统计模型提供了更可靠的效果估计,这表明RevMan软件应该采用不那么偏见的方法.
科学领域:
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 具有二分论结果的元分析经常遇到单零研究,这给分析提出了挑战.
- 固定连续性校正经常被应用,但可以引入偏差,尽管它在像RevMan.Man这样的软件中常见.
研究的目的:
- 实证地比较固定连续性校正与替代统计模型对元分析结果的影响.
- 评估不同方法对元分析中的效果估计的影响.
主要方法:
- 通过七种方法重新分析了来自885项Cochrane评论的数据:使用固定连续性校正的Mantle-Haenszel,随机效应反差与固定连续性校正,Peto方法,随机效应反差与治疗臂连续性校正,Mantle-Haenszel没有校正,后勤回归和贝叶斯随机效应模型.
- 计算的赔率比率是衡量差异的方法之间的比率.
主要成果:
- 大约30%的元分析在将固定连续性校正方法与未经校正的曼特尔-汉泽尔或逻辑回归进行比较时显示出显著差异 (比率<0.8或<1.25) .
- 统计方法的选择可以大大影响元分析中的效果估计.
结论:
- 在元分析中不慎使用固定的连续性校正可能导致现有柯克兰评论中的偏见效果估计.
- 未来的RevMan软件版本应该整合不太偏的统计方法来提高元分析结果的准确性.
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