How meta-analysis increases statistical power
How meta-analysis increases statistical power
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DOI：
10.1037/1082-989x.8.3.243
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发表时间：
2003-09-01
影响因子：
7
通讯作者：
Becker, BJ
中科院分区：
文献类型：
--
作者：
Cohn, LD;Becker, BJ
进行荟萃分析最常被引用的原因之一是它为评审者提供的统计能力的增加。本文证明了固定效应荟萃分析通过降低加权平均效应量（T）的标准误来提高统计功效。并且，在这样做的过程中，缩小了T周围的置信区间。小的置信区间使评论者更有可能检测到非零的群体效应，从而增加统计功效。较小的置信区间也表示估计的群体效应量的精度增加。计算的例子提供了3个效应量指数：d（标准化平均差），皮尔逊的r，比值比。随机效应荟萃分析也可能显示统计功效增加，加权平均效应量的标准误较小。然而，作者证明，增加随机效应荟萃分析中的研究数量并不总是增加统计功效。
One of the most frequently cited reasons for conducting a meta-analysis is the increase in statistical power that it affords a reviewer. This article demonstrates that fixed-effects meta-analysis increases statistical power by reducing the standard error of the weighted average effect size (T.) and, in so doing, shrinks the confidence interval around T. Small confidence intervals make it more likely for reviewers to detect nonzero population effects, thereby increasing statistical power. Smaller confidence intervals also represent increased precision of the estimated population effect size. Computational examples are provided for 3 effect-size indices: d (standardized mean difference), Pearson's r, and odds ratios. Random-effects meta-analyses also may show increased statistical power and a smaller standard error of the weighted average effect size. However, the authors demonstrate that increasing the number of studies in a random-effects meta-analysis does not always increase statistical power.