Viechtbauer Wolfgang
Department of Methodology and Statistics, University of Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands.
Stat Med. 2007 Jan 15;26(1):37-52. doi: 10.1002/sim.2514.
Effect size estimates to be combined in a systematic review are often found to be more variable than one would expect based on sampling differences alone. This is usually interpreted as evidence that the effect sizes are heterogeneous. A random-effects model is then often used to account for the heterogeneity in the effect sizes. A novel method for constructing confidence intervals for the amount of heterogeneity in the effect sizes is proposed that guarantees nominal coverage probabilities even in small samples when model assumptions are satisfied. A variety of existing approaches for constructing such confidence intervals are summarized and the various methods are applied to an example to illustrate their use. A simulation study reveals that the newly proposed method yields the most accurate coverage probabilities under conditions more analogous to practice, where assumptions about normally distributed effect size estimates and known sampling variances only hold asymptotically.
在系统评价中要合并的效应量估计值，通常比仅基于抽样差异所预期的更具变异性。这通常被解释为效应量存在异质性的证据。然后经常使用随机效应模型来解释效应量的异质性。本文提出了一种为效应量异质性程度构建置信区间的新方法，该方法即使在小样本中，当模型假设得到满足时，也能保证名义覆盖概率。总结了各种现有的构建此类置信区间的方法，并将各种方法应用于一个示例以说明其用法。一项模拟研究表明，新提出的方法在更类似于实际情况的条件下产生最准确的覆盖概率，在实际情况中，关于效应量估计值呈正态分布以及已知抽样方差的假设仅渐近成立。
