Meta-Analysis in Scientific Studies

A meta-analysis is basically a study about studies. It is used to get an integrated result. In other words, a researcher reviews previously published studies on a topic, then analyzes the various results to find general trends across the studies. It can be used in psychology, general medical practice, or detailed studies of particular diseases, conditions, and treatments.

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

A meta-analysis is helpful because it's a review designed to summarize information. It 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.

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.

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-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. This increases the reliability of the information.

Meta-analyses offer numerous advantages over individual studies. This includes greater statistical power and more ability to extrapolate to the greater population. They are also considered to be evidence-based.

Although a powerful research tool, meta-analysis has disadvantages. 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.

While researchers acknowledge that 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. 

A biased meta-analysis can produce misleading results.

The three main types of bias are:

1. Publication bias. The problem here is that "positive" studies are more likely to go to print.
2. Search bias. The search for studies can produce unintentionally biased results. This includes using an incomplete set of keywords or varying strategies to search databases. Also, the search engine used can be a factor.
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.