A subset of systematic reviews; a method for systematically combining pertinent qualitative and quantitative study data from several selected studies to develop a single conclusion that has greater statistical power. This conclusion is statistically stronger than the analysis of any single study, due to increased numbers of subjects, greater diversity among subjects, or accumulated effects and results.
Definition of Meta_Analysis
- A meta-analysis would be used for the following purposes:
- To establish statistical significance with studies that have conflicting results
- To develop a more correct estimate of effect magnitude
- To provide a more complex analysis of harms, safety data, and benefits
- To examine subgroups with individual numbers that are not statistically significant.
If the individual studies utilized randomized controlled trials (RCT), combining several selected RCT results would be the highest level of evidence on the evidence hierarchy, followed by systematic reviews, which analyze all available studies on a topic.
What is a meta-analysis?
Meta-analysis is a statistical technique for combining data from multiple studies on a particular topic. Meta-analyses play a fundamental role in evidence-based healthcare. Compared to other study designs (such as randomized controlled trials or cohort studies), the meta-analysis comes in at the top of the ‘levels of evidence’ pyramid in evidence-based healthcare. This is a pyramid which enables us to weigh up the different levels of evidence available to us. As we go up the pyramid, each level of evidence is less subject to bias than the level below it. Therefore, meta-analyses can be seen as the pinnacle of healthcare evidence. Meta-analyses began to appear as a leading part of research in the late 70s. Since then, they have become a common way for synthesizing evidence and summarizing the results of individual 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.
Why should we carry out & use meta-analyses?
To make a valid decision about using an intervention, ideally, we should not rely on the results obtained from single studies. This is because results can vary from one study to another for various reasons, including confounding factors, and the different study samples used. By combining individual studies, and thus using more data, the precision and accuracy of the estimates in the individual studies can be improved upon. Additionally, if the individual studies were underpowered, combining them in a meta-analysis can increase the overall statistical power to detect an effect.
Below are the basic steps involved in a meta-analysis.
1- Identifying/formulating a problem (i.e. a question to be answered e.g. to determine the effectiveness of exercise for depression compared with no treatment and comparator treatments).
2- Doing a literature search:
this will probably involve searching multiple databases that index reliable peer-reviewed articles, such as PubMed, Scopus, Web of Science, Embase, etc.
3- Deciding on selection/inclusion criteria:
you should use inclusion and exclusion criteria that will ensure that high-quality evidence, of direct relevance to your research question, is included. For this reason, we tend mostly to include randomized controlled trials (and may exclude observational studies). Ideally, we would also include unpublished studies in order to avoid publication bias. (If we fail to include all of the relevant studies, our conclusions may be erroneous. Specifically, we may overstate the benefit of a treatment (for example), because studies which fail to find a significant effect are less likely to be published than those which do not find a significant effect. See here for more information).
4- Data extraction:
you ought to extract data for your outcomes of interest to be pooled (combined) in the final analysis set.
5- Doing the basic meta-analysis:
there is a range of software for this purpose, such as Review Manager and Comprehensive Meta-Analysis Software.
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.
Greater statistical power.
Confirmatory data analysis
Greater ability to extrapolate to the general population affected.
Considered an evidence-based resource.
Difficult and time-consuming to identify appropriate studies.
Not all studies provide adequate data for inclusion and analysis.
Requires advanced statistical techniques.
Heterogeneity of study populations.