Meta-analysis in clinical trials revisited.
Meta-analysis in clinical trials revisited.
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DOI：
10.1016/j.cct.2015.09.002
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发表时间：
2015-11
影响因子：
2.2
通讯作者：
Laird N
中科院分区：
文献类型：
--
作者：
DerSimonian R;Laird N
在这篇文章中，我们回顾了1986年发表在《临床试验中的荟萃分析》杂志上的一篇文章，在那篇文章中，我们引入了随机效应模型来总结一些相关临床试验中关于治疗有效性的证据。由于其简单易行，我们的方法已被广泛使用(到目前为止已有超过12,000条引文)，“德西蒙尼和莱尔德方法”现在经常被称为医学和临床研究中荟萃分析的“标准方法”或“流行”方法。该方法对于提供总体效应估计和表征一系列研究中效应的异质性特别有用。在这里，我们回顾了导致1986年最初文章的背景，简要描述了用于荟萃分析的随机效应方法，探索了它在各种环境中的应用和随着时间的推移的趋势，并推荐使用稳健的方差估计来测试总体效应的方法的改进。最后，我们讨论了重新调整大数据荟萃分析和基因组广泛关联研究的方法，以研究基因变异在复杂疾病中的重要性。
In this paper, we revisit a 1986 article we published in this Journal, Meta-Analysis in Clinical Trials, where we introduced a random-effect model to summarize the evidence about treatment efficacy from a number of related clinical trials. Because of its simplicity and ease of implementation, our approach has been widely used (with more than 12,000 citations to date) and the “DerSimonian and Laird method” is now often referred to as the ‘standard approach’ or a ‘popular’ method for meta-analysis in medical and clinical research. The method is especially useful for providing an overall effect estimate and for characterizing the heterogeneity of effects across a series of studies. Here, we review the background that led to the original 1986 article, briefly describe the random-effects approach for meta-analysis, explore its use in various settings and trends over time and recommend a refinement to the method using a robust variance estimator for testing overall effect. We conclude with a discussion of repurposing the method for Big Data meta-analysis and Genome Wide Association Studies for studying the importance of genetic variants in complex diseases.