Meta-analysis in clinical trials revisited
Rebecca DerSimoniana b✉, Nan M. Lairdc d
- aNational Institute of Allergy and Infectious Diseases, Bethesda, MD, USA. Electronic address: DerSimonian@nih.gov
- bNational Institute of Allergy and Infectious Diseases, Bethesda, MD, USA
- cHarvard University, TH Chan School of Public Health, Boston, MA, USA. Electronic address: nanlaird@gmail.com
- dHarvard University, TH Chan School of Public Health, Boston, MA, USA
- ✉Corresponding author
Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.
Abstract
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.
TL;DRA random-effect model to summarize the evidence about treatment efficacy from a number of related clinical trials and 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 are revisited. (Semantic Scholar)