Meta-Analysis: Definition, Methods & Examples

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  • 0:03 What is Meta-Analysis?
  • 1:01 Why Do a Meta-Analysis?
  • 1:29 The Steps of a Meta-Analysis
  • 7:05 Lesson Summary
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Lesson Transcript
Instructor: Erika Steele

Erika has taught college Biology, Microbiology, and Environmental Science. She has a PhD in Science Education.

How do people make decisions about healthcare using medical research? One method is to use a meta-analysis. This lesson explains what a meta-analysis is and how it is put together, as well as helps you understand the results.

What Is a Meta-Analysis?

There are several ways that individual studies can be summarized to help healthcare workers make decisions, including narrative reviews, systematic reviews, and meta-analyses. Both narrative and systematic reviews are both qualitative in nature.

Narrative reviews are not very rigorous, but they focus on the very basics of a topic.

Systematic reviews are more rigorous than narrative reviews; they focus on a single research question. For example, a systematic review will focus specifically on the relationship between cervical cancer and long-term use of oral contraceptives, while a narrative review may be about cervical cancer.

Meta-analyses are quantitative and more rigorous than both types of reviews. In addition to providing an overview, these papers provide a quantitative assessment of how well a treatment works or they may also provide an estimate of how much more likely a person is to develop a disease if they participate in a certain behavior.

Why Do a Meta-Analysis?

Medical research can be confusing. How would you make a decision if you read 30 studies that said a weight loss treatment worked and 30 that said it didn't work? What if I told you there was a better way than just flipping a coin? The reason people do meta-analyses is that research from several studies with conflicting results can be combined to make decisions about the effectiveness of a medication on a person's risks for developing a disease that is more informed than using a Magic 8-ball.

The Steps of a Meta-Analysis

DR. SEAS is an acronym to help remember the steps for doing a meta-analysis. The D and R stand for Defining the research question and Reviewing the literature, which are the first steps of the procedure. The next steps, Select appropriate studies, Extract data, Analyze data, and Synthesize data, are represented by the SEAS in the acronym.

1. Define Research Question and Review Literature

Meta-analyses begin with defining the research question. A defined research question identifies the population affected by the intervention and the potential outcome(s) of treatment. For example, Are women who use oral contraceptives for 10 or more years at greater risks for cervical cancer than women who have never used oral contraceptives? This question defines the population, and it identifies cervical cancer as the outcome of the intervention, long-term contraceptive use.

Reviewing the literature for studies that answer your research question comes after your question has been defined. When you review the literature, it is kind of like using Google to search for information, except you look in places like PubMed, Medline, EMBASE, or Google Scholar. Throughout this step, you're looking for scholarly papers that are relevant to your research question.

2. Select Appropriate Studies

Selecting the appropriate studies is probably the most important step of a meta-analysis. The studies selected to be included add strength to the analysis. There is no exact process for choosing papers, but in general, papers that are duplicates, that are written in a language you can't understand, or that are not clinical studies, can be eliminated.

After eliminating papers that are clearly not useful, screen the rest of the papers a bit more deeply for eligibility. Several elements can be used to determine a paper's eligibility for the analysis, but it is important that all of the studies you include have the information that you need in order to do your analysis. This data may include characteristics of the population, such as age, race, health status, and appropriate statistical analyses.

3. Extract Data

The next step in the process is extracting the data for analysis and synthesis. Creating a spreadsheet, table, or some other form to record the information makes this part of the process easier. The extracted data depends on the research question, but it may include information such as sample size, patient characteristics, length of study, and a statistical measure, such as confidence interval, odds ratio, risks ratio, mean difference, or hazard ratio.

4. Analyze Data

Once you have organized it, your next step is to analyze the data using statistical software. A forest plot will allow you to compare statistical differences between groups. In our example, the statistical measure that we collected was relative risk, which indicates the difference in risk between groups. It will let us see whether long-term users of oral contraceptives are more likely to develop cervical cancer.

Typical forest plots will have the following:

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