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Last Update

  • Updated.jpg 20 April 2016


  • systematic review; knowledge synthesis; concept synthesis; critical interpretive synthesis; integrative review; meta-synthesis; meta ethnography; metastudy; meta-interpretation narrative synthesis; realist review


See also Evidence-based health care | Expert searching | Network meta-analysis | Research methods | Systematic reviews | Zotero

"...meta-analysis is the use of statistical methods to combine results of individual studies. This allows us to make the best use of all the information we have gathered in our systematic review by increasing the power of the analysis. By statistically combining the results of similar studies we can improve the precision of our estimates of treatment effect, and assess whether treatment effects are similar in similar situations. The decision about whether or not the results of individual studies are similar enough to be combined in a meta-analysis is essential to the validity of the result, and will be covered in the next module on heterogeneity. In this module we will look at the process of combining studies and outline the various methods available." — Cochrane Collaboration, 2002

A meta-analysis is an objective, analytical research methodology that will pool results from key clinical studies after the completion of a thorough systematic review. The MA combines results from several studies that address a common research hypothesis. Further, it provides precise estimates of treatment effects, weights different studies in the systematic review as to importance, and considers validity (internal/external) based on the quality of studies included. Well-performed meta-analyses strive to account for all relevant studies, heterogeneity among them and rigorous, robust approaches to synthesis. In 1904, Karl Pearson performed the first meta-analysis because he felt that it would improve statistical power in studies with small samples. Pearson analyzed results and concluded that the new pooled research would allow for better accuracy. Although the MA is widely-used in medicine, its use did not come into effect until 1955. Sophisticated techniques for meta-analysis were introduced in educational research in the 1970s. In 2013, there is considerable interest in using meta-analytical techniques to synthesize the medical literature but also the literatures in education, library and information science, and the social sciences.

Five (5) steps in the meta-analysis

I. Define your hypotheses

  • define your research statement and hypotheses before searching for studies. Make the relationship between variables under investigation explicit to define your inclusion and exclusion criteria.

II. Search the literature

III. Input data

  • gather findings from major studies (e.g., p-value, effect size, etc) and consider a statistical tool for data massage. Some studies will not provide enough statistical information to calculate effect sizes. Consult a biostatistician.

IV. Calculate effect sizes

  • the overall effect can be determined by converting statistics to a common metric; consider sample-size, bias, and central tendency (e.g., mean effect size and confidence intervals), variability (e.g., heterogeneity analysis). For accuracy, consult a biostatistician.

V. Analyze variables

  • where heterogeneity exists, analyze moderating variables by coding each and analyzing mean differences (for categorical variables) or weighted regression (for continuous variables) to determine variability in effect size. If heterogeneity is not present, analyze moderating variables.

See also Five Steps of Evidence-based health care (EBHC)

Strengths and weaknesses of the MA

The meta-analysis has several strengths and weaknesses. Some of the benefits of performing the systematic review and meta-analysis are:

  • Provides a view into the medical literature that no other method can
  • Helps health professionals synthesize hundreds of studies, and cope with information overload
  • Combines several studies and will therefore be less influenced by local bias than single studies
  • Reveals whether studies' results are more varied than anticipated
  • Makes it more likely you can safely generalize results to other populations
  • May provide higher statistical power than individual studies
  • A poorly-conducted meta-analysis may be biased due to the exclusion of important or missed studies
  • Weak and/or misleading analysis should be avoided by speaking to a biostatistician
  • Conducting the SR/MA is a long and expensive process requiring considerable resources and expertise
  • Sources of bias are hard to control in the meta-analysis. An MA of poorly-designed studies results in unreliable numbers. Slavin argues that methodologically sound studies are naturally preferable which he calls "best evidence meta-analysis". Some analysts include weaker studies, adding a study-level predictor variable to reflect the quality of studies. This examines the effect of study quality on effect size.
  • Another weakness is the heavy reliance on published studies, which may increase effect. However, it is easier to publish studies that show a significant effect. Publication bias or "file-drawer effect" (where non-significant studies end up in desk drawers rather than in public domain) must be considered in interpreting outcome. When publication bias is likely, some meta-analyses include a "failsafe N" statistic to calculate numbers of studies with null results that will make the treatment effect unreliable.

Reporting standards

Scientists and librarians can use various checklists for critical appraisal, such as:

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