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首页> 外文期刊>BMC Medical Research Methodology >Quantifying, displaying and accounting for heterogeneity in the meta-analysis of RCTs using standard and generalised Q statistics
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Quantifying, displaying and accounting for heterogeneity in the meta-analysis of RCTs using standard and generalised Q statistics

机译：使用标准和广义Q统计量化RCT的Meta分析中的异质性量化，显示和算法

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摘要

Background 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. Methods 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. Results 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. Conclusions 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.
机译：背景技术临床研究人员通常优选使用固定效果模型来进行META分析的主要解释。通常通过已知的Q和I 2 统计来评估异质性，以及随机效应估计它们意味着。近年来，已经提出了用于量化异质性的替代方法，即基于“广义”Q统计数据。方法审查RCT的18个IPD Meta分析进入癌症治疗方法，以量化存在的异质性，还讨论了解释异质性的实用方法。结果在标准Q和I 2 统计中用于测试异质性时获得不同的结果。进一步研究了具有最大数量的异质性的2META分析，并且在检查时，无随机效果模型的直接施用未被视为适当。与标准Q统计相比，广义Q统计提供了一种更准确的平台，用于估计18个Meta分析中的异质性。结论通过试验亚组的预先规范解释异质性，图形诊断工具和敏感性分析产生了比随机效果模型的自动应用更为理想的结果。用于量化和调整异质性的广义Q统计方法应作为标准纳入统计软件。提供软件以帮助实现这一目标。

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