MRC Clinical Trials Unit, 222 Euston Road, London NW1 2DA, UK.
BMC Med Res Methodol. 2011 Apr 7;11:41. doi: 10.1186/1471-2288-11-41.
Clinical researchers have often preferred to use a fixed effects model for the primary interpretation of a meta-analysis. Heterogeneity is usually assessed via the well known Q and I2 statistics, along with the random effects estimate they imply. In recent years, alternative methods for quantifying heterogeneity have been proposed, that are based on a 'generalised' Q statistic.
We review 18 IPD meta-analyses of RCTs into treatments for cancer, in order to quantify the amount of heterogeneity present and also to discuss practical methods for explaining heterogeneity.
Differing results were obtained when the standard Q and I2 statistics were used to test for the presence of heterogeneity. The two meta-analyses with the largest amount of heterogeneity were investigated further, and on inspection the straightforward application of a random effects model was not deemed appropriate. Compared to the standard Q statistic, the generalised Q statistic provided a more accurate platform for estimating the amount of heterogeneity in the 18 meta-analyses.
Explaining heterogeneity via the pre-specification of trial subgroups, graphical diagnostic tools and sensitivity analyses produced a more desirable outcome than an automatic application of the random effects model. Generalised Q statistic methods for quantifying and adjusting for heterogeneity should be incorporated as standard into statistical software. Software is provided to help achieve this aim.
临床研究人员通常更倾向于使用固定效应模型来对荟萃分析进行主要解释。通常通过著名的 Q 和 I2 统计量以及它们所暗示的随机效应估计值来评估异质性。近年来，已经提出了一些基于“广义”Q 统计量的量化异质性的替代方法。
我们回顾了 18 项针对癌症治疗的 RCT 的 IPD 荟萃分析，以量化存在的异质性，并讨论解释异质性的实用方法。
当使用标准 Q 和 I2 统计量来检验异质性的存在时，得到了不同的结果。对具有最大异质性的两个荟萃分析进行了进一步研究，并且在检查时，随机效应模型的直接应用被认为是不合适的。与标准 Q 统计量相比，广义 Q 统计量为 18 项荟萃分析中量化和调整异质性提供了更准确的平台。
通过预先指定试验亚组、图形诊断工具和敏感性分析来解释异质性，比自动应用随机效应模型产生了更理想的结果。应该将用于量化和调整异质性的广义 Q 统计量方法作为标准纳入统计软件中。提供了软件来帮助实现这一目标。
