纠正基于聚类二进制结果的效果大小差异
1Northwestern University, Evanston, IL, USA.
Educational and psychological measurement
|October 27, 2025
概括
本研究提供了在系统性审查中准确分析聚类二进制数据的方法,当统计分析忽略聚类时. 它提供了风险差异和风险比率的差异计算,使用类内相关性.
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 健康研究方法
背景情况:
- 系统性审查和元分析经常包括集群随机试验.
- 一个常见的问题是统计分析未能考虑聚类数据.
- 这可能导致不准确的结果,特别是在用总结数据分析二次结果时.
研究的目的:
- 从聚类二进制数据提供准确的差异计算风险差异,日志风险比率和日志赔率比率.
- 为元分析师提供一种方法来处理在分析中没有考虑聚类的研究.
主要方法:
- 为关键效果指标推导近似方差表达式.
- 使用类内相关性 (ICCs) 来调整聚类.
- 一个说明性的例子,展示了计算过程.
主要成果:
- 为风险差异的近似变量,日志风险比率和日志赔率比率提供公式.
- 该方法允许对聚类二进制数据进行分析,即使标准分析不考虑聚类.
- 引用了对类内相关性的经验估计.
结论:
- 拟议的方法可以更准确地对聚类二进制结果的研究进行元分析.
- 研究人员现在可以更好地解决涉及集群随机试验的系统审查中的分析挑战.
- 准确的差异估计对于从集群数据中可靠合成证据至关重要.
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