Meta-Analysis in Scientific Studies

A Meta-Analysis Looks at Multiple Qualifying Studies

Woman going over research. Getty Images

A meta-analysis is basically a study about studies in order to get an integrated result. In other words, a researcher reviews previously published studies on a topic, and analyzes the various results to find general trends across the studies.

Nowadays, with new studies from around the world constantly being published, the amount of medical research available is overwhelming, even for the most experienced practitioner.

A meta-analysis is helpful because it's a review designed to summarize information, and follows a few general principles in that a meta-analysis:

  • is done systematically
  • follows certain criteria
  • contains a pool of results
  • is based on a quantitative analysis 

The review provides important conclusions and trends that influence future research, policy-makers' decisions and how patients receive care.

The Main Objectives of Meta-Analysis

As you now know, a meta-analysis is a summary of integrated results analyzed for their differences. Other objectives of this type of clinical review are to:

  • Evaluate effects in different subsets of participants
  • Create new hypotheses to inspire future clinical studies
  • Overcome the limitations of small sample sizes
  • Establish statistical significance 

Meta-Analysis "Increases" Sample Size

One of the reasons why meta-analyses are so useful is because of an all too common problem across many research studies: small sample sizes.

Using a large sample size requires more resources, including funds and personnel, than a small sample size. When individual research projects don't study a significant number of subjects, it can be difficult to draw reliable and valid conclusions. 

Meta-studies help overcome the issue of small sample sizes because they review multiple studies across the same subject area.

Meta-Analysis and Establishing Statistical Significance

Meta-analyses can also help establish statistical significance across studies that might otherwise seem to have conflicting results.

When you take many studies into consideration at once, the statistical significance established is much greater than with one study alone. This is important because statistical significance increases the validity of any observed differences, which increases the reliability of the information.

Advantages of Meta-Analysis

Meta-analyses offer numerous advantages over individual studies, including greater statistical power, more ability to extrapolate to the greater population and they are considered evidence based.

Disadvantages of Meta-Analysis

Although a powerful research tool, meta-analysis has disadvantages.  For a meta-analysis, it can be a difficult and time-consuming endeavor to find all of the appropriate studies to examine. Meta-analyses also require complex statistical skills and techniques.

Why Meta-Analysis Is Controversial

While researchers acknowledge meta-analysis is an effective tool, the controversy lays in the procedure the reviewers use.

Following the aforementioned principles is critical to drawing valid and reliable conclusions.

Experts warn that even minor deviations from protocol can produce biased and misleading results. Additionally, once completed and peer-reviewed, some meta-analyses have been proven to be inappropriate and unwarranted. 

Types of Bias in Meta-Analysis

A biased meta-analysis can produce misleading results.

The three main types of bias are:

  1. Publication bias. The problem is "positive" studies are more likely to go to print.
  2. Search bias. Identification of relevant studies using an incomplete set of key words to search databases, the search engine itself and the big differences in search strategies can all produce unintentionally biased results.
  3. Selection bias. Researchers must clearly define criteria for choosing from the long list of potential studies to be included in the meta-analysis to ensure unbiased results.


Walker, et al. Cleveland Clinic Journal of Medicine: Meta-Analysis - Its Strengths and Limitations (2008)

Continue Reading