Meta-analysis in clinical trials revisited
Introduction
Three decades ago in this Journal (formerly titled Controlled Clinical Trials), we proposed a simple non-iterative method to integrate the findings from a number of related clinical trials to evaluate the efficacy of a certain treatment for a specified medical condition [1]. Our approach, the random-effects model for meta-analysis, now commonly referred to as the “DerSimonian and Laird method”, has become extremely popular in medical research and other applications. According to the Web of Science Core Collection, there are more than twelve thousand citations attributed to the article with a substantial proportion of them occurring in the more recent years.
Following the introduction of the term meta-analysis in 1976 [2] and before the publication of our article, Meta-analysis in Clinical Trials, in 1986 [1], Web of Science lists 222 articles with the term meta-analysis in the title. Almost all of these were in social sciences with 50% in psychology, 32% in education research and 10% in business economics. In contrast, a large proportion of the more than 46,000 articles since 1986 with the term meta-analysis in the title are related to medical or clinical research.
In this paper, we first review the background and the setting that led to Meta-Analysis in Clinical Trials [1] and the “DerSimonian and Laird method”, briefly describe the random-effects model for meta-analysis, assess its use in various settings and trends over time and explore the reasons for its popularity in medical and clinical research. We recommend a refinement to the method using an improved variance estimator for testing an overall effect and conclude with a discussion of repurposing the method for genetic association studies and Big Data meta-analysis.
Section snippets
Background
Eugene Glass first coined the phrase meta-analysis in 1976 to mean the statistical analysis of the findings of a collection of individual studies [2]. In the following decade, meta-analysis was primarily used in the social sciences to summarize the results of a large number of studies on many behavioral, educational and psychosocial studies and experiments. For instance, Rosenthal [3] analyzed accumulating data from studies done by others to assess if a teacher's expectations can influence a
Meta-Analysis in clinical trials
For Meta-Analysis in Clinical Trials [1], we adopted this same random-effects approach to integrate the findings from a number of related clinical trials to strengthen the evidence for the efficacy of a certain treatment for a specified medical condition. The basic idea of the approach is the same, but here we assumed the treatment effect for the i-th study, Yi, was the difference in Binomial cure rates between a treated and control group. Assuming the two groups are independent, the variance, s
Acknowledgments
We thank Jelena Follweiler for her technical assistance and Jing Wang for her help with the citation graphs.
References (32)
- et al.
Meta-analysis in clinical trials
Control. Clin. Trials
(1986) - et al.
Random-effects model for meta-analysis of clinical trials: an update
Contemp. Clin. Trials
(2007) - et al.
How does the DerSimonian and Laird procedure for random effects meta-analysis compare with its more efficient by harder to compute counterparts?
J. Stat. Plan. Infer.
(2010) - et al.
Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies
Am. J. Hum. Genet.
(2011) Primary, secondary, and meta-analysis of research
Educ. Res.
(1976)- et al.
Interpersonal expectancy effects: the first 345 studies
Behav. Brain Sci.
(1978) The Tennessee study of class size in the early school grades
Futur. Child.
(1995)- et al.
Sustaining inquiry in education: lessons from skill grouping and class size
Harv. Educ. Rev.
(1996) - et al.
The scholastic aptitude test: a critical appraisal
Harv. Educ. Rev.
(1980) - et al.
Evaluating the effect of coaching on SAT scores: a meta-analysis
Harv. Educ. Rev.
(1983)
Research notes: coaching and the SAT® I
The combination of estimates from different experiments
Biometrics
Performance of statistical methods for meta-analysis when true study effects are non-normally distributed: a simulation study
Stat. Methods Med. Res.
Performance of statistical methods for meta-analysis when true study effects are non-normally distributed: a comparison between DerSimonian-Laird and restricted maximum likelihood
Stat. Methods Med. Res.
Extending DerSimonian and Laird's methodology to perform multivariate random-effects meta-analyses
Stat. Med.
A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression
Biom. J.
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