Meta-analysis in clinical trials revisited

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• 作者：Rebecca DerSimonian^a ; ^{DerSimonian@nih.gov" class="auth_mail" title="E-mail the corresponding author} ; Nan Laird^b ; ^{nanlaird@gmail.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Random-effects model ; Meta-analysis ; Heterogeneity ; Clinical trials ; Genome Wide Association Studies ; Big Data
• 刊名：Contemporary Clinical Trials
• 出版年：2015
• 出版时间：November 2015
• 年：2015
• 卷：45
• 期：part_PA
• 页码：139-145
• 全文大小：970 K

文摘

In this paper, we revisit a 1986 article we published in this Journal, Meta-Analysis in Clinical Trials, where we introduced a random-effects 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.