随机对照试验对连续结果的元分析中固定和随机效应的准确性和精度
Timo Gnambs1, Ulrich Schroeders2
1Leibniz Institute for Educational Trajectories, Bamberg, Germany.
Research synthesis methods
|September 26, 2023
概括
在元分析中缺少的相关性可以通过多变量元回归来处理. 这种方法,与赋值相比,为聚合效应和治疗效应研究中的异质性提供了更好的准确性.
科学领域:
- 生物统计学 生物统计学
- 心理学研究方法 心理学研究方法
- 临床试验 临床试验
背景情况:
- 分析往往缺乏测试前和测试后的得分相关性,这对于计算效果大小和差异至关重要.
- 当前的临时解决方案涉及对缺失的相关性赋值一个常数值,这可能会引入偏差.
研究的目的:
- 提出和评估多变量元回归作为一种替代方法,而不是在元分析中归因缺失的相关性.
- 在多变量元回归框架内比较可靠的差异估计和三级建模的性能.
主要方法:
- 开发了一种多变量元回归方法,以建模独立组效应大小,并考虑依赖性.
- 采用了可靠的差异估计和三级建模技术.
- 进行了一项全面的模拟研究,反映了临床和教育心理学元分析中的条件.
主要成果:
- 假定固定相关性 (0.8) 或使用具有强大的方差估计的多变量元回归,准确估计了聚合效应.
- 归算和强大的差异估计都略有扭曲了研究间异质性估计.
- 三级元回归在很大程度上产生了无偏见的固定效应,但预测间隔不那么一致.
结论:
- 多变量元回归,特别是具有强大的方差估计,是处理元分析中缺失相关性的可行替代方案.
- 三级模型提供无偏见的固定效应,但需要仔细考虑预测间隔的一致性.
- 提供了改善元分析实践和指导未来方法论发展的建议.
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