平均差异和标准化平均差异的比较,用于相同规模的连续结果测量
1Boehringer Ingelheim Pharmaceuticals, Inc, Ridgefield, CT, USA.
JBI evidence synthesis
|February 22, 2024
概括
对于系统性审查中的连续结果,当数据尺度相匹配时,平均差异 (MD) 通常比标准化平均差异 (SMD) 更好. SMDs可以引入偏差并减少置信区间覆盖范围,特别是当违反正常性假设时.
科学领域:
- 生物统计学 生物统计学
- 证据综合 证据综合
- 医学研究方法学 医学研究方法学
背景情况:
- 系统性审查和元分析通常使用平均差异 (MDs) 和标准化平均差异 (SMDs) 来获得连续结果.
- 当研究使用不同的测量尺度,标准化合成数据时,通常会优先使用SMD.
- 有关SMD可解释性和增加异质性的可能性存在担忧.
研究的目的:
- 使用模拟研究来比较MD和SMD的性能.
- 评估它们在连续测量在相同尺度的场景中的有用性.
- 评估对违反正常性假设的强度.
主要方法:
- 进行了模拟研究,比较MD和SMD.
- 多种设置,包括正常性假设不成立的情况.
- 评估偏差,平均平方误差和置信区间覆盖概率.
主要成果:
- 在具有可比规模的场景中,SMD表现低于MD.
- SMDs表现出更大的偏差,更高的平均平方误差和更低的CI覆盖率.
- 医学博士对违反正常性假设的违规行为表现出更大的稳定性.
结论:
- SMD对于在不同尺度上合成连续数据非常有价值.
- 当尺度是可比的,MDs通常是首选的由于更好的统计属性.
- 对于类似的连续数据,MDs提供了更强大,更易于解释的效果测量方法.
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