what is meta analysis in qualitative research

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Meta-Analysis – Definition, Purpose And How To Conduct It

Published by Owen Ingram at April 26th, 2023 , Revised On September 23, 2024

The number of studies being published in biomedical and clinical literature is increasing day by day. The massive abundance of research studies in these areas makes it rather difficult to synthesise all data and accumulate knowledge from various studies. All of this delays certain clinical decisions and conclusions to be made.

To determine the validity of a hypothesis , it is necessary to look into multiple studies rather than just one. For this purpose, systematic reviews or narrative reviews have been used to synthesise data from multiple studies, which often leads to an objective approach as different people can have different opinions. Meta-analysis, on the other hand, provides an objective and quantitative approach to combining evidence from various studies.

What Is Meta-Analysis?

The meaning of meta-analysis is a statistical method of combining results from numerous studies on a certain research question. The term was first used in 1976 and can be used to determine if the effect reported in the literature is real or not.

To conduct a quality meta-analysis, you need to identify an area in which the effect of treatment is uncertain. It is also recommended that you collect as many studies similar to the effect as possible so that you can compare them and get a better picture of it. This assists the researcher in understanding how big or small the effect is, and how different the results are from other studies.

Purpose Of Meta-Analysis In Research

The purpose of meta-analysis is more than just combining results from studies to give a statistical assessment. It also helps to point out:

• Any potential reasons for variations and differences in results, also known as heterogeneity in meta-analysis. Some popular reasons for this might be differences in sample size, or differences in analysis methods in research.
• The real estimate of the effect size that is reported in literature than any individual study. Combining multiple studies reduces research bias and the chances of random errors.

Meta-Analysis In Applied And Basic Research

Basic research involves seeking knowledge and gathering data on any subject, whereas applied research is more experimental and uses methods to solve real-life problems. Meta-analyses are used in both types of research that is applied and basic research.

• Pharmaceutical companies use meta-analyses to gain approval for new drugs, such as antibiotics for bacterial infections. Even regulatory authorities use this research method to gain approval for different processes. Hence, meta-analysis is used in medicine, crime, education and psychology for applied research.
• In terms of basic research, it is used in various fields such as sociology, finance, economics, marketing and social psychology. An example of meta-analysis in basic research is studying the effect of caffeine on cognitive performance.

Strengths and Limitations Of Meta-Analysis

There are many key benefits of meta-analysis in research studies, as it is a powerful tool. Here are some strengths of it:

• A meta-analysis takes place after a systematic review, which means the end product will be reliable and accurate.
• It has great statistical power, as it combines multiple studies rather than one individual study, which might suffer from a lack of sufficient data.
• This analysis can confirm existing research or refute it. Either way, it gives a confirmatory data analysis.
• It is regarded highly in the scientific community, as it provides an objective and solid analysis of evidence.

Challenges Associated With Meta-Analysis

Meta-analysis also has certain challenges that result in limitations while carrying out this statistical quantitative approach. Some challenges faced by meta-analyses are:

• It is not always possible to predict the outcome of a large-scale study. This is because meta-analysis mostly rely on small-scale studies which do not represent the broad population.
• A decent meta-analysis can not make up for flawed or bad research designs. Thus, it can not control the potential for bias to arise in studies. Therefore, it is urged to only include research with sound methodologies known as “best evidence synthesis”.

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How To Conduct A Meta-Analysis

Before conducting a meta-analysis and defining the research scope, it is necessary to evaluate the number of publications that have grown over the years. It can be quite hard to scan and skim through a large number of studies and literature reviews, which is why it is necessary to define the research question with care, including only relevant aspects. Here are steps on how to perform a meta-analysis:

• Formulate a research question that showcases the effects or interventions to be studied. This is mostly a binary question, such as “Does drug X improve outcome Y” in clinical studies.
• Conduct a systematic review that analyses and synthesises all data related to the one research question.
• Gather all data such as sample sizes and research methods used to indicate data variability. All decide which dependent variables are allowed.
• The selection of criteria is also a crucial step as it is necessary to understand whether published or unpublished studies are to be included or not. Based on the research question, it is important to choose studies that are quality-based and relevant.
• Choosing the right meta-analytic methods and meta-analysis software to be used in meta-analysis is another significant step. Some methods used are traditional univariate meta-analysis, meta-regression and meta-analytic structural equation modelling methods.
• While evaluating the data, it is necessary to use a meta-analysis forest plot, which is the graphical representation of the results of a meta-analysis studies. Its visual representation helps understand the heterogeneity among studies and helps compare the overall effect sizes of an intervention.
• The final step of literature meta-analysis is to report the results. They should be comprehensive and precise for the reader’s understanding.

Meta-Analysis Vs Systematic Review

A systematic review is a comprehensive analysis of existing research, whereas a meta-analysis is a statistical analysis or combination of results from two separate studies. Here’s how the two differ from each other:

Frequently Asked Questions

Where does meta-analysis fit in the research process.

It plays a key role in planning new studies and identifying answers to research questions. It is also widely sought for publications. Lastly, it is also used for grant applications that are used to justify the need for a new study.

Which fields use meta-analysis?

Common fields where meta-analyses are used are medicine, psychology, sociology, education, and health. It may also be used in finance, marketing and economics.

Is meta-analysis qualitative or quantitative?

Meta-analysis is a quantitative method that uses statistical methods to synthesise and collect data from various studies to estimate the size of the effect of a particular intervention or treatment.

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• How It Works

Study Design 101: Meta-Analysis

• Case Report
• Case Control Study
• Cohort Study
• Randomized Controlled Trial
• Practice Guideline
• Systematic Review

Meta-Analysis

• Helpful Formulas
• Finding Specific Study Types

A subset of systematic reviews; a method for systematically combining pertinent qualitative and quantitative study data from several selected studies to develop a single conclusion that has greater statistical power. This conclusion is statistically stronger than the analysis of any single study, due to increased numbers of subjects, greater diversity among subjects, or accumulated effects and results.

Meta-analysis would be used for the following purposes:

• To establish statistical significance with studies that have conflicting results
• To develop a more correct estimate of effect magnitude
• To provide a more complex analysis of harms, safety data, and benefits
• To examine subgroups with individual numbers that are not statistically significant

If the individual studies utilized randomized controlled trials (RCT), combining several selected RCT results would be the highest-level of evidence on the evidence hierarchy, followed by systematic reviews, which analyze all available studies on a topic.

• Greater statistical power
• Confirmatory data analysis
• Greater ability to extrapolate to general population affected
• Considered an evidence-based resource

Disadvantages

• Difficult and time consuming to identify appropriate studies
• Not all studies provide adequate data for inclusion and analysis
• Requires advanced statistical techniques
• Heterogeneity of study populations

Design pitfalls to look out for

The studies pooled for review should be similar in type (i.e. all randomized controlled trials).

Are the studies being reviewed all the same type of study or are they a mixture of different types?

The analysis should include published and unpublished results to avoid publication bias.

Does the meta-analysis include any appropriate relevant studies that may have had negative outcomes?

Fictitious Example

Do individuals who wear sunscreen have fewer cases of melanoma than those who do not wear sunscreen? A MEDLINE search was conducted using the terms melanoma, sunscreening agents, and zinc oxide, resulting in 8 randomized controlled studies, each with between 100 and 120 subjects. All of the studies showed a positive effect between wearing sunscreen and reducing the likelihood of melanoma. The subjects from all eight studies (total: 860 subjects) were pooled and statistically analyzed to determine the effect of the relationship between wearing sunscreen and melanoma. This meta-analysis showed a 50% reduction in melanoma diagnosis among sunscreen-wearers.

Real-life Examples

Goyal, A., Elminawy, M., Kerezoudis, P., Lu, V., Yolcu, Y., Alvi, M., & Bydon, M. (2019). Impact of obesity on outcomes following lumbar spine surgery: A systematic review and meta-analysis. Clinical Neurology and Neurosurgery, 177 , 27-36. https://doi.org/10.1016/j.clineuro.2018.12.012

This meta-analysis was interested in determining whether obesity affects the outcome of spinal surgery. Some previous studies have shown higher perioperative morbidity in patients with obesity while other studies have not shown this effect. This study looked at surgical outcomes including "blood loss, operative time, length of stay, complication and reoperation rates and functional outcomes" between patients with and without obesity. A meta-analysis of 32 studies (23,415 patients) was conducted. There were no significant differences for patients undergoing minimally invasive surgery, but patients with obesity who had open surgery had experienced higher blood loss and longer operative times (not clinically meaningful) as well as higher complication and reoperation rates. Further research is needed to explore this issue in patients with morbid obesity.

Nakamura, A., van Der Waerden, J., Melchior, M., Bolze, C., El-Khoury, F., & Pryor, L. (2019). Physical activity during pregnancy and postpartum depression: Systematic review and meta-analysis. Journal of Affective Disorders, 246 , 29-41. https://doi.org/10.1016/j.jad.2018.12.009

This meta-analysis explored whether physical activity during pregnancy prevents postpartum depression. Seventeen studies were included (93,676 women) and analysis showed a "significant reduction in postpartum depression scores in women who were physically active during their pregnancies when compared with inactive women." Possible limitations or moderators of this effect include intensity and frequency of physical activity, type of physical activity, and timepoint in pregnancy (e.g. trimester).

Related Terms

A document often written by a panel that provides a comprehensive review of all relevant studies on a particular clinical or health-related topic/question.

Publication Bias

A phenomenon in which studies with positive results have a better chance of being published, are published earlier, and are published in journals with higher impact factors. Therefore, conclusions based exclusively on published studies can be misleading.

Now test yourself!

1. A Meta-Analysis pools together the sample populations from different studies, such as Randomized Controlled Trials, into one statistical analysis and treats them as one large sample population with one conclusion.

a) True b) False

2. One potential design pitfall of Meta-Analyses that is important to pay attention to is:

a) Whether it is evidence-based. b) If the authors combined studies with conflicting results. c) If the authors appropriately combined studies so they did not compare apples and oranges. d) If the authors used only quantitative data.

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