在不同尺度上报告的抑郁症结果的综合:对建模平均差异的方法进行比较
Beatrice C Downing1, Nicky J Welton1, Hugo Pedder1
1Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Research synthesis methods
|February 2, 2026
概括
平均值比率 (RoM) 在抑郁症治疗中显示出比标准化平均差异 (SMD) 方法更好的模型匹配. 此外,RoM的异质性也是最低的,这表明治疗效果是多重的.
科学领域:
- 生物统计学 生物统计学
- 精神病学是一个精神病学.
- 网络元分析 网络元分析
背景情况:
- 在不同的尺度上合成连续的结果是具有挑战性的.
- 标准化平均差异 (SMD) 和平均比率 (RoM) 是常见的方法.
- SMD可以是研究特定的或规模特定的.
研究的目的:
- 将RoM性能与SMD方法进行比较.
- 评估元回归对基线严重性的影响.
- 评估抑郁症治疗的贝叶斯网络元分析 (NMA) 中的模型合适性和异质性.
主要方法:
- 在五个尺度上对14种抑郁症治疗进行了贝叶斯式NMA.
- 与RoM,研究SMD和规模SMD模型进行比较.
- 使用的偏差信息标准用于模型合适性和异质性的百分比收缩.
主要成果:
- 与SMD模型相比,RoM显示出更好的模型匹配.
- 在RoM中,异质性最低 (20.5%的收缩),其次是Scale-SMD (18.2%) 和Study-SMD (16.7%).
- 超回归并没有改善SMD模型的合适性;模型选择影响了治疗排名,但前三种治疗方法在模型中一致.
结论:
- 在不同尺度上,RoM是合成抑郁症治疗效果的首选方法.
- 较高的RoM收缩表明治疗效果是倍增的,而不是添加的.
- 推使用尺度SMD来添加处理效果,以尽量减少异质性.
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