Why perform a meta-analysis?
What is a meta-analysis?
Meta-analysis is the statistical procedure for combining data from multiple studies. When the treatment effect (or effect size) is consistent from one study to the next, meta-analysis can be used to identify this common effect. When the effect varies from one study to the next, meta-analysis may be used to identify the reason for the variation.
Why perform a meta-analysis?
Decisions about the utility of an intervention or the validity of a hypothesis cannot be based on the results of a single study, because results typically vary from one study to the next. Rather, a mechanism is needed to synthesize data across studies. Narrative reviews had been used for this purpose, but the narrative review is largely subjective (different experts can come to different conclusions) and becomes impossibly difficult when there are more than a few studies involved. Meta-analysis, by contrast, applies objective formulas (much as one would apply statistics to data within a single study), and can be used with any number of studies.
Meta-analysis in applied and basic research
Pharmaceutical companies use meta-analysis to gain approval for new drugs, with regulatory agencies sometimes requiring a meta-analysis as part of the approval process. Clinicians and applied researchers in medicine, education, psychology, criminal justice, and a host of other fields use meta-analysis to determine which interventions work, and which ones work best. Meta analysis is also widely used in basic research to evaluate the evidence in areas as diverse as sociology, social psychology, sex differences, finance and economics, political science, marketing, ecology and genetics, among others.
Where does meta-analysis fit in the research process?
Many journals encourage researchers to submit systematic reviews and meta-analyses that summarize the body of evidence on a specific question, and this approach is replacing the traditional narrative review. Meta-analyses also play supporting roles in other papers. For example, a paper that reports results for a new primary study might include a meta-analysis in the introduction to synthesize prior data and help to place the new study in context.
Planning new studies
Meta-analyses can play a key role in planning new studies. The meta-analysis can help identify which questions have already been answered and which remain to be answered, which outcome measures or populations are most likely to yield significant results, and which variants of the planned intervention are likely to be most powerful.
Meta-analyses are used in grant applications to justify the need for a new study. The meta-analysis serves to put the available data in context and to show the potential utility of the planned study. The graphical elements of the meta-analysis, such as the forest plot, provide a mechanism for presenting the data clearly, and for capturing the attention of the reviewers. Some funding agencies now require a meta-analysis of existing research as part of the grant application to fund new research.
"Comprehensive meta‐analysis (CMA) is essential software for the meta‐analyst. Complex analyses can be conducted easily using this intuitive software. The support staff are always helpful and respond quickly when questions arise. I highly recommend CMA."
Erika A. Patall - Department of Educational Psychology, The University of Texas at Austin, Austin, TX
"I've used CMA for years now as a researcher and teacher. It is uncommon to see a program that is so full‐featured yet easy enough to use that you can pick it up in an afternoon. The calculations are spoton and the images are sharp enough for use in publication. I recommend the software to all of my students and colleagues who are performing meta‐analysis."
David Walton PT, PhD - Assistant Professor, The University of Western Ontario
Frequently Asked Questions
Comprehensive Meta-Analysis (CMA) is a powerful computer program for meta-analysis. The program combines ease of use with a wide array of computational options and sophisticated graphics.