Partlett Christopher, Riley Richard D
National Perinatal Epidemiology Unit, Oxford, U.K.
University of Birmingham, Birmingham, U.K.
Stat Med. 2017 Jan 30;36(2):301-317. doi: 10.1002/sim.7140. Epub 2016 Oct 7.
A random effects meta-analysis combines the results of several independent studies to summarise the evidence about a particular measure of interest, such as a treatment effect. The approach allows for unexplained between-study heterogeneity in the true treatment effect by incorporating random study effects about the overall mean. The variance of the mean effect estimate is conventionally calculated by assuming that the between study variance is known; however, it has been demonstrated that this approach may be inappropriate, especially when there are few studies. Alternative methods that aim to account for this uncertainty, such as Hartung-Knapp, Sidik-Jonkman and Kenward-Roger, have been proposed and shown to improve upon the conventional approach in some situations. In this paper, we use a simulation study to examine the performance of several of these methods in terms of the coverage of the 95% confidence and prediction intervals derived from a random effects meta-analysis estimated using restricted maximum likelihood. We show that, in terms of the confidence intervals, the Hartung-Knapp correction performs well across a wide-range of scenarios and outperforms other methods when heterogeneity was large and/or study sizes were similar. However, the coverage of the Hartung-Knapp method is slightly too low when the heterogeneity is low (I < 30%) and the study sizes are quite varied. In terms of prediction intervals, the conventional approach is only valid when heterogeneity is large (I > 30%) and study sizes are similar. In other situations, especially when heterogeneity is small and the study sizes are quite varied, the coverage is far too low and could not be consistently improved by either increasing the number of studies, altering the degrees of freedom or using variance inflation methods. Therefore, researchers should be cautious in deriving 95% prediction intervals following a frequentist random-effects meta-analysis until a more reliable solution is identified. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
随机效应荟萃分析综合了多项独立研究的结果，以总结关于某个特定感兴趣指标（如治疗效果）的证据。该方法通过纳入关于总体均值的随机研究效应，来考虑真实治疗效果中无法解释的研究间异质性。通常假设研究间方差已知来计算平均效应估计值的方差；然而，已证明这种方法可能不合适，尤其是在研究数量较少时。已提出了一些旨在考虑这种不确定性的替代方法，如哈通 - 克纳普法、西迪克 - 琼克曼法和肯沃德 - 罗杰法，并且在某些情况下已证明这些方法优于传统方法。在本文中，我们进行了一项模拟研究，以检验其中几种方法在基于受限最大似然估计的随机效应荟萃分析得出的95%置信区间和预测区间覆盖范围方面的性能。我们表明，就置信区间而言，哈通 - 克纳普校正法在广泛的情形下表现良好，并且在异质性大且/或研究规模相似时优于其他方法。然而，当异质性低（I² < 30%）且研究规模差异较大时，哈通 - 克纳普法的覆盖范围略低。就预测区间而言，传统方法仅在异质性大（I² > 30%）且研究规模相似时有效。在其他情况下，尤其是当异质性小且研究规模差异较大时，覆盖范围过低，并且通过增加研究数量改变自由度或使用方差膨胀方法都无法持续改善。因此，在找到更可靠的解决方案之前，研究人员在进行频率主义随机效应荟萃分析后推导95%预测区间时应谨慎。© 2016作者。《医学统计学》由约翰·威利父子有限公司出版。
