A refined method for multivariate meta-analysis and meta-regression
A refined method for multivariate meta-analysis and meta-regression
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DOI：
10.1002/sim.5957
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发表时间：
2014-02-20
影响因子：
2
通讯作者：
Riley, Richard D.
中科院分区：
文献类型：
--
作者：
Jackson, Daniel;Riley, Richard D.
在研究数量较少的常见情况下，使用荟萃分析的随机效应模型推断平均治疗效果是有问题的。这是因为研究间方差的估计值不够精确，无法准确地应用传统方法进行检验并推导出平均效应的置信区间。我们已经发现，一种改进的单变量荟萃分析方法，它将比例因子应用于估计效果的标准误，提供了更准确的推断。我们解释了如何将这种方法扩展到多变量场景，并表明我们的建议，精制多元荟萃分析和荟萃回归可以提供更准确的推论比更传统的方法。我们解释了我们所提出的方法可以使用标准的输出从多变量荟萃分析软件包，并将我们的方法应用到两个真实的例子。版权所有（c）2013约翰威利父子有限公司
Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects' standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. Copyright (c) 2013 John Wiley & Sons, Ltd.