Meta-analysis is basically research about research. It is used to obtain comprehensive results. In other words, researchers review previously published research on a topic, and then analyze various results to find the overall trend of the research. It can be used for detailed research in psychology, general medical practice, or specific diseases, conditions, and treatments.
Why is meta-analysis important?
With the continuous publication of new research from all over the world, the amount of medical research available is huge. This is true even for the most experienced practitioners.
Meta-analysis is helpful because it is a type of comment designed to summarize information. Meta-analysis follows some general principles:
- Completed systematically
- Follow certain standards
- Contains the result pool
- Based on quantitative analysis
The review provides important conclusions and trends that will influence future research, policy makers’ decisions, and how patients receive care.
As you know now, a meta-analysis is a summary of the combined results of an analysis of differences. Other goals of this type of clinical review are:
- Assess the impact of different subsets of participants.
- Create new hypotheses to inspire future clinical research.
- Overcome the limitation of small sample size.
- Establish statistical significance.
Meta-analysis “increases” sample size
One of the reasons that meta-analysis is so useful is that there is a very common problem in many 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 do not study a large number of topics, it may be difficult to draw reliable and valid conclusions.
Meta-researches help overcome the problem of small sample sizes because they review multiple studies in the same subject area.
Establish statistical significance
Meta-analysis can also help establish statistical significance across studies, otherwise these studies may produce conflicting results.
When you consider multiple studies at the same time, the statistical significance established is much greater than that of a single study. This is important because statistical significance increases the validity of any observed differences. This increases the reliability of the information.
Meta-analysis offers many advantages over individual studies. This includes greater statistical power and more extrapolation capabilities. They are also considered evidence-based.
Although meta-analysis is a powerful research tool, it also has drawbacks. Finding all suitable studies to check can be a difficult and time-consuming task. Meta-analysis also requires complex statistical skills and techniques.
Why meta-analysis is controversial
Although the researchers acknowledge that meta-analysis is an effective tool, the controversy lies in the procedures used by the reviewers. Following the above principles is essential to draw valid and reliable conclusions.
Experts warn that even slight deviations from the agreement can produce biased and misleading results. In addition, once completed and peer-reviewed, some meta-analysis has proven to be inappropriate and unfounded.
Types of bias
Biased meta-analysis can produce misleading results.
The three main types of bias are:
- Publication bias. The problem here is that “active” research is more likely to be published.
- Search bias. Search research may inadvertently produce biased results. This includes using an incomplete set of keywords or different strategies to search the database. In addition, the search engine used is also a factor.
- Select deviation. Researchers must clearly define the criteria for selecting from a long list of potential studies to ensure that their results are unbiased.