Valid inference in random effects meta-analysis
Valid inference in random effects meta-analysis
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DOI：
10.1111/j.0006-341x.1999.00732.x
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发表时间：
1999-09-01
期刊：
影响因子：
1.9
通讯作者：
Proschan, MA
中科院分区：
文献类型：
--
作者：
Follmann, DA;Proschan, MA
随机效应Meta分析推断的标准方法依赖于用标准正态分布近似检验统计量的零分布。这一近似值在研究数量k上是渐近的，在通常只有几项研究的医学荟萃分析中可能会有很大的误差。本文提出了用随机效应模型进行检验的排列法和特别方法。在分组排序法下，我们在每次试验中随机交换处理组和对照组的标签。这个想法类似于在社区干预试验中使用排列分布，其中社区是成对随机的。排列法在理论上控制了典型荟萃分析场景的I类错误率。我们还建议两个特别程序。我们的第一个建议是使用k-1个自由度的t参考分布，而不是通常的随机效应检验统计量的标准正态分布。我们还调查了已报道的治疗效果的简单t统计量的使用。
The standard approach to inference for random effects meta-analysis relies on approximating the null distribution of a test statistic by a standard normal distribution. This approximation is asymptotic on k, the number of studies, and can be substantially in error in medical meta-analyses, which often have only a few studies. This paper proposes permutation and ad hoc methods for testing with the random effects model. Under the group permutation method, we randomly switch the treatment and control group labels in each trial. This idea is similar to using a permutation distribution for a community intervention trial where communities are randomized in pairs. The permutation method theoretically controls the type I error rate for typical meta-analyses scenarios. We also suggest two ad hoc procedures. Our first suggestion is to use a t-reference distribution with k - 1 degrees of freedom rather than a standard normal distribution for the usual random effects test statistic. We also investigate the use of a simple t-statistic on the reported treatment effects.