Symmetrized percent change for treatment comparisons
Symmetrized percent change for treatment comparisons
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DOI：
10.1198/000313006x90684
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发表时间：
2006-02-01
影响因子：
1.8
通讯作者：
Ayers, GD
中科院分区：
文献类型：
--
作者：
Berry, DA;Ayers, GD
治疗效果通常通过将治疗后的（F）测量值与相应的基线（B）测量值相关联来进行分析。作为效果衡量指标，绝对差值（D）和百分比变化（PC）比对称百分比变化（SPC）使用得更多。然而，所有这些指标都会改变与B的依赖结构，并且它们的分布可能与B和F不同。为了检验它们的可解释性和相对性能，我们考虑了在独立以及加性和乘性相关结构下进行参数和非参数分析的模拟。在独立情况下，对F的非参数分析具有最大功效。在其他情况下，对SPC进行简单方差分析的功效等于或大于其他分析方法。
Treatment effects are commonly analyzed by relating post-treatment (F) measurements with corresponding baseline (B) measurements. As effect measures, absolute difference (D) and percent change (PC) are used more than symmetrized percent change (SPC). However, all these measures alter the dependency structure with B and their distributions can differ from B and F. To examine their interpretability and relative performance, we considered simulations under independence and additive and multiplicative correlation structures for parametric and nonparametric analyses. Under independence, nonparametric analysis on F had the greatest power. Elsewhere, simple ANOVA on SPC had power equal to or greater than alternative analysis methods.