Abstract

Assessing heterogeneity between the independent studies in a meta-analysis plays a critical role in quantifying the amount of dispersion. The well-known Higgins’ I2 statistic has been used most often for measuring heterogeneity. However, the problem of the within-study variances involved in this measure is discussed, which leads to misinterpretation. Alternatively, the between-study coefficient of variation, the ratio of the standard deviation of the random effects to the effect, is of interest. This current work is motivated by meta-analytic data on continuous outcomes reported only the sample means and sample sizes. No sampling variance estimate is available in the studies. In such a case, we introduce the mean difference estimator based on the profile likelihood and bootstrap methods and propose the coefficient of variation estimator for measuring the heterogeneity of the mean differences. The statistical power of the coefficient of variation is determined based on simulations. The results indicate that the estimated between-study coefficient of variation derived from maximum profile likelihood estimation has a lower bias than that obtained from bootstrap estimation. The Wald-type confidence interval using variance estimation derived from the delta method provides a suitable coverage probability and has a short length interval.

中文翻译：

---

没有研究特定方差信息的荟萃分析中的异质性测量

摘要

在荟萃分析中评估独立研究之间的异质性在量化离散量方面发挥着关键作用。著名的希金斯 I2 统计量最常用于测量异质性。然而，讨论了该测量中涉及的研究内方差问题，这导致了误解。或者，研究间变异系数（随机效应的标准差与效应的比率）也很有趣。目前的工作是由仅报告样本平均值和样本量的连续结果的荟萃分析数据推动的。研究中没有可用的抽样方差估计。在这种情况下，我们引入了基于轮廓似然和自举方法的均值差估计器，并提出了用于测量均值差的异质性的变异系数估计器。变异系数的统计功效是根据模拟确定的。结果表明，从最大轮廓似然估计得出的估计研究间变异系数的偏差比从自助估计中获得的偏差更低。使用从 delta 方法导出的方差估计的 Wald 型置信区间提供了合适的覆盖概率，并且具有较短的长度区间。