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Concept
Meta-analysis
Summary
A meta-analysis is a statistical analysis that combines the results of multiple scientific studies. Meta-analyses can be performed when there are multiple scientific studies addressing the same question, with each individual study reporting measurements that are expected to have some degree of error. The aim then is to use approaches from statistics to derive a pooled estimate closest to the unknown common truth based on how this error is perceived. It is thus a basic methodology of Metascience. Meta-analytic results are considered the most trustworthy source of evidence by the evidence-based medicine literature.
Not only can meta-analyses provide an estimate of the unknown effect size, it also has the capacity to contrast results from different studies and identify patterns among study results, sources of disagreement among those results, or other interesting relationships that may come to light with multiple studies.
However, there are some methodological problems with meta-analysis. If individual studies are systematically biased due to questionable research practices (e.g., data dredging, data peeking, dropping studies) or the publication bias at the journal level, the meta-analytic estimate of the overall treatment effect may not reflect the actual efficacy of a treatment. Meta-analysis has also been criticized for averaging differences among heterogeneous studies because these differences could potentially inform clinical decisions. For example, if there are two groups of patients experiencing different treatment effects studies in two randomised control trials (RCTs) reporting conflicting results, the meta-analytic average is representative of neither group, similarly to averaging the weight of apples and oranges, which is neither accurate for apples nor oranges. In performing a meta-analysis, an investigator must make choices which can affect the results, including deciding how to search for studies, selecting studies based on a set of objective criteria, dealing with incomplete data, analyzing the data, and accounting for or choosing not to account for publication bias.
Official source
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