Meta-analysis refers to a research process. You might think of it as a ‘Cliff’s Notes’ summary that compiles, evaluates, and synthesizes all of the individual research reports that were completed by the others. Using statistics, a meta-analysis synthesizes the findings of multiple similar scientific studies to produce an overall result. By combining findings from multiple studies, the size and diversity of the studied population can be increased dramatically, thereby strengthening the ability of researchers to come to a supported conclusion. Moreover, this inherent increase in population size means that a meta-analysis has greater statistical power and validity compared to single, individual studies and that its findings are more likely to apply to the real-world population, not just study subjects. As a result, a more precise assessment of the impact of certain disease risk factors can be outlined in a meta-analysis’ outcome(s).
Steps in a Meta-analysis
Generally, meta-analysis can be conducted in the following steps:
The first step is identifying a scientific research question and proposing a corresponding hypothesis.
Then, a systematic review is done to find all the studies that are relevant and of good enough quality to be worth including. This step usually includes published and unpublished studies to avoid ‘publication bias’, which considers that only studies that show positive findings are typically published in peer-reviewed journals.
After studies are chosen to be part of the meta-analysis, summary data or results are extracted from each study. Then the next step is to calculate the appropriate summary measures from each study to conduct further analysis.
Lastly, a proper meta-analysis model is selected to analyze and interpret the combined results. Researchers typically work in duplicate to complete many of the meta-analysis steps to further strengthen its scientific validity.
Meta-analysis and Ataxia Research
Meta-analyses make a significant contribution to scientific research, especially in the field of medicine. For instance, one study shares the findings of a meta-analysis that outlines the cognitive symptoms experienced by Friedreich Ataxia patients. This meta-analysis included and analyzed eighteen studies. The meta-analysis concluded that Friedreich Ataxia patients had much more difficulty than individuals without Friedreich Ataxia in many languages, attention, executive function, memory, emotion regulation, and social cognitive tasks.
Other meta-analyses have been used to examine which biomarkers are best for ataxia. For example, this meta-analysis article found that neurofilament light chain is a good biomarker for ataxia caused by genetic mutations.
Benefits and Risks
Meta-analyses improve the accuracy of results, and because of that, are considered an evidence-based resource for most researchers. They allow researchers to merge several smaller studies into one larger study. This may make it easier to show the effect of a treatment (or lack thereof).
However, while a meta-analysis has its strengths, this research method also contains several pitfalls. For example, it is challenging to identify eligible studies during the selection process since not all studies provide sufficient and qualified data to be included and analyzed.
Another major criticism of meta-analyses is that they carry the risk of incorporating different types of studies, and therefore the summary effect may ignore the fundamental differences between studies. For example, some topics may not have enough blinded clinical trials, so researchers must resort to pooling data from prospective and retrospective observational studies. This can be thought of as mixing ‘apples and oranges’ – and can bias the findings of the resultant statistical analysis.
While meta-analyses are an excellent tool for researchers and doctors to gain a broad and definitive understanding of a topic, like with any scientific research, they should be approached with a degree of caution and reviewed with a critical eye.
Snapshot Written by: Lin Dong
Edited by: Dr. Siddharth Nath
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