Estimating required information size by quantifying diversity in random-effects model meta-analyses
Estimating required information size by quantifying diversity in random-effects model meta-analyses
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DOI：
10.1186/1471-2288-9-86
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发表时间：
2009-12-30
影响因子：
4
通讯作者：
Gluud, Christian
中科院分区：
文献类型：
--
作者：
Wetterslev, Jorn;Thorlund, Kristian;Gluud, Christian
工作背景：越来越多的人意识到，荟萃分析需要足够大的信息量来检测或拒绝预期的干预效果。在一个荟萃分析所需的信息大小可以从一个预期的先验干预效果或干预效果的低风险的bias.Methods的试验建议计算：信息大小的计算需要考虑总模型方差在一个荟萃分析控制I型和II型错误。在这里，我们推导出一个调整因子，所需的信息大小下的任何随机效应模型Meta-analysis.Results：我们设计了一个元分析的多样性（D-2）的措施，这是相对方差减少时，元分析模型从随机效应变成固定效应模型。D-2是试验间变异性占试验间变异性和考虑所需信息量的抽样误差估计值之和的百分比。D-2不同于基于异质性的常见量化的直观明显的调整因子，即不一致性（I-2），其可能低估了所需的信息量。因此，D-2和I-2的比较和解释使用几个模拟和临床实例。此外，我们在数学上表明，多样性等于或大于不一致，即D-2 >= I-2，为所有的meta-analysis.Conclusion：我们得出结论，D-2似乎是一个更好的选择比I2考虑模型变异在任何随机效应的meta-analysis，尽管试验之间的方差估计，构成模型的选择。此外，在任何随机效应模型荟萃分析中，D-2可以很容易地调整所需的信息大小。
Background: There is increasing awareness that meta-analyses require a sufficiently large information size to detect or reject an anticipated intervention effect. The required information size in a meta-analysis may be calculated from an anticipated a priori intervention effect or from an intervention effect suggested by trials with low-risk of bias.Methods: Information size calculations need to consider the total model variance in a meta-analysis to control type I and type II errors. Here, we derive an adjusting factor for the required information size under any random-effects model meta-analysis.Results: We devise a measure of diversity (D-2) in a meta-analysis, which is the relative variance reduction when the meta-analysis model is changed from a random-effects into a fixed-effect model. D-2 is the percentage that the between-trial variability constitutes of the sum of the between-trial variability and a sampling error estimate considering the required information size. D-2 is different from the intuitively obvious adjusting factor based on the common quantification of heterogeneity, the inconsistency (I-2), which may underestimate the required information size. Thus, D-2 and I-2 are compared and interpreted using several simulations and clinical examples. In addition we show mathematically that diversity is equal to or greater than inconsistency, that is D-2 >= I-2, for all meta-analyses.Conclusion: We conclude that D-2 seems a better alternative than I2 to consider model variation in any random-effects meta-analysis despite the choice of the between trial variance estimator that constitutes the model. Furthermore, D-2 can readily adjust the required information size in any random-effects model meta-analysis.