A meta-analysis is defined by Haidlich (2010) as "a quantitative, formal, epidemiological study design used to systematically assess previous research studies to derive conclusions about that body of research. Outcomes from a meta-analysis may include a more precise estimate of the effect of treatment or risk factor for disease, or other outcomes, than any individual study contributing to the pooled analysis" (p. 29).
According to Grant & Booth (2009), a meta-analysis is defined as a "technique that statistically combines the results of quantitative studies to provide a more precise effect of the results" (p. 94).
When to Use It: According to the Cochrane Handbook, "an important step in a systematic review is the thoughtful consideration of whether it is appropriate to combine the numerical results of all, or perhaps some, of the studies. Such a meta-analysis yields an overall statistic (together with its confidence interval) that summarizes the effectiveness of an experimental intervention compared with a comparator intervention" (section 10.2).
Conducting meta-analyses can have the following benefits, according to Deeks et al. (2019, section 10.2):
To improve precision. Many studies are too small to provide convincing evidence about intervention effects in isolation. Estimation is usually improved when it is based on more information.
To answer questions not posed by the individual studies. Primary studies often involve a specific type of participant and explicitly defined interventions. A selection of studies in which these characteristics differ can allow investigation of the consistency of effect across a wider range of populations and interventions. It may also, if relevant, allow reasons for differences in effect estimates to be investigated.
To settle controversies arising from apparently conflicting studies or to generate new hypotheses. Statistical synthesis of findings allows the degree of conflict to be formally assessed, and reasons for different results to be explored and quantified.
A meta-analysis can only be conducted after the completion of a systematic review, as the meta-analysis statistically summarizes the findings from the studies synthesized in a particular systematic review. A meta-analysis cannot exist without a pre-existing systematic review. Grant & Booth (2009) state that "although many systematic reviews present their results without statistically combining data [in a meta-analysis], a good systematic review is essential to a meta-analysis of the literature" (p. 98).
Conducting a meta-analysis requires all studies that will be statistically summarized to be similar - i.e. the population, intervention, and comparison. Grant & Booth (2009) state that "most importantly, it requires that the same measure or outcome be measured in the same way at the same time intervals" (p. 98).
It can be challenging to ensure that studies used in a meta-analysis are similar enough, which is a crucial component
Meta-analyses can perhaps be misleading due to biases such as those concerning specific study designs, reporting, and biases within studies
The following resource provides further support on conducting a meta-analysis.
PRISMA (2020) is a 27-item checklist that replaces the PRISMA (2009) statement, which ensures proper and transparent reporting for each element in a systematic review and meta-analysis. "It is an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews."
Check out the supplementary resources page for additional information on meta-analyses.
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