How Meta-Analysis Works | NEJM Evidence

Summary

This script from 'NEJM Evidence' introduces meta-analysis as a statistical technique for synthesizing results across studies, akin to aggregating movie reviews for a collective score. It outlines five key steps in conducting a meta-analysis, emphasizing the importance of systematic study search, study design consistency, similar patient populations, identical interventions, and uniform outcome definitions. The script uses a clinical research example to illustrate pooling results from multiple randomized trials, highlighting the use of random effects models to account for between-trial heterogeneity, and concludes with the presentation of findings in a forest plot, emphasizing the value of meta-analysis in gaining more comprehensive insights.

Takeaways

• 🎬 Meta-analysis is a statistical technique that synthesizes results from multiple studies to provide a more comprehensive understanding of a subject.
• 🔍 It involves a systematic search for studies, aiming to include all relevant publications to avoid bias.
• 📊 The design of the studies is considered, with preference often given to combining studies of the same design for simplicity.
• 👥 Patient populations in the studies should be similar to ensure the results are informative and comparable.
• 💊 The intervention being evaluated must be consistent across all studies for a meaningful meta-analysis.
• 📈 Outcome definitions should be the same or similar across studies to allow for accurate pooling of results.
• ⚖️ In clinical research, meta-analysis often uses a random effects approach to account for between-trial heterogeneity.
• 📉 A fixed effects approach assumes the same underlying treatment effect across trials, which is rarely the case in medicine.
• 📊 Forest plots are used to display the results of a meta-analysis, with horizontal lines representing individual trials and a diamond representing the pooled estimate.
• 🎞 The analogy of synthesizing movie reviews to determine a film's quality is used to illustrate the concept of meta-analysis in an accessible way.
• 🌟 The pooled result from multiple sources, whether in research or movie reviews, provides more confidence in making a choice.

Q & A

What is the main theme of the video script?

The main theme of the video script is the concept of meta-analysis, which is introduced through the analogy of aggregating movie reviews to make an informed choice, and then explained in the context of clinical research.

What is a meta-analysis in the context of clinical research?

A meta-analysis in clinical research is a statistical technique that combines the results of multiple studies to estimate the strength of an association between an intervention and an outcome, providing a more comprehensive understanding than a single study can offer.

Why is it important to systematically search for studies in a meta-analysis?

Systematically searching for studies helps to avoid bias by including all relevant publications, ensuring a comprehensive and unbiased overview of the research topic.

What is the significance of considering the design of studies in a meta-analysis?

Considering the design of studies is important because different study designs may have different levels of evidence and methodological rigor, which can affect the validity of the pooled results.

Why is it necessary for studies in a meta-analysis to have similar patient populations?

Studies need to have similar patient populations to ensure that the results are comparable and relevant to the target demographic, increasing the informativeness of the meta-analysis.

What is the importance of evaluating the same intervention across studies in a meta-analysis?

Evaluating the same intervention ensures that the comparison is consistent across studies, allowing for a meaningful aggregation of results.

Why is it important for studies to have the same or similar outcome definitions in a meta-analysis?

Having the same or similar outcome definitions allows for a valid comparison of results across studies, as it ensures that the outcomes being measured are consistent.

What is the issue with simply averaging the results of different trials in a meta-analysis?

Simply averaging the results does not account for the differences in study size and variance, which can lead to misleading conclusions about the average effect of an intervention.

What is the difference between a fixed effects and a random effects analysis in a meta-analysis?

A fixed effects analysis assumes that the underlying treatment effect is the same across all trials, while a random effects analysis assumes that there is variability in the treatment effect between trials due to factors other than sampling error.

What is a forest plot and how is it used in meta-analysis?

A forest plot is a graphical representation used in meta-analysis to display the results of individual trials and the pooled estimate. Each horizontal line represents an individual trial's result, with the length indicating the confidence interval, and the diamond represents the overall pooled estimate with its confidence interval.

How does the script relate the concept of meta-analysis to the process of choosing a movie to watch?

The script uses the analogy of aggregating movie reviews with different rating scales to find an overall score, similar to how meta-analysis pools results from multiple studies to get a more informed understanding of a film's quality or a treatment's effectiveness.

Outlines

00:00

🎬 Movie Night Meta-Analysis

In this paragraph, Chana Sacks introduces the concept of meta-analysis through the relatable scenario of choosing a movie for a Saturday night. The analogy of synthesizing various movie reviews to get an overall score is used to explain how meta-analysis pools results from multiple studies to provide a more comprehensive understanding. Key components of conducting a meta-analysis are outlined, including systematic study search, consideration of study design, similarity in patient populations, consistency in intervention evaluation, and matching outcome definitions. The paragraph also touches on the challenges of pooling results from different-sized trials and the decision between fixed effect and random effects analysis.

05:02

📊 Understanding Meta-Analysis for Better Decision Making

The second paragraph concludes the discussion on meta-analysis by emphasizing its value in enhancing confidence in decision-making, akin to choosing a movie based on aggregated reviews. It reiterates the principle that combining data from multiple sources can provide a more reliable outcome than a single study or review. The paragraph leaves the reader with a simple yet powerful message about the utility of meta-analysis in both research and everyday life, before transitioning into the enjoyment of the movie night.

Keywords

💡Meta-analysis

Meta-analysis is a statistical technique used to combine the results of multiple studies to provide a more comprehensive understanding of a particular phenomenon. In the context of the video, it is compared to synthesizing movie reviews to get an overall score, illustrating how aggregating data can lead to a clearer consensus. The script mentions meta-analysis in the context of clinical research, where it is used to pool results across trials to estimate the strength of an association between an intervention and an outcome.

💡Systematic search

A systematic search is a methodical approach to finding and reviewing all relevant literature on a specific topic to minimize bias. The video script emphasizes the importance of this process in conducting a meta-analysis, where investigators use a well-defined strategy to include all pertinent publications. This ensures that the analysis is as comprehensive and unbiased as possible.

💡Randomized trials

Randomized trials, or randomized controlled trials (RCTs), are a type of scientific experiment where subjects are randomly assigned to either a treatment or control group. The script uses RCTs as an example of study designs that can be included in a meta-analysis, highlighting the need for similar study designs for easier comparison and aggregation of results.

💡Patient populations

In the context of clinical research, patient populations refer to the groups of individuals who participate in a study. The video script points out the importance of having similar patient populations across studies in a meta-analysis to ensure that the results are comparable and relevant to the target demographic.

💡Intervention

An intervention in medical research is any treatment or procedure that is applied to a patient population to assess its effectiveness. The script mentions a medication for heart failure as an example of an intervention, which is the subject of the hypothetical meta-analysis discussed in the video.

💡Outcome

In research, an outcome is the result or effect that is measured in a study. The video script uses hospitalization for heart failure as an example of an outcome, which is what researchers are interested in when evaluating the effectiveness of an intervention.

💡Relative risks

Relative risks are a way of expressing the likelihood of an event occurring in an exposed group compared to an unexposed group. In the script, the concept is introduced when discussing how to pool results from different trials, emphasizing the need to consider the size and variance of each study when combining data.

💡Confidence intervals

Confidence intervals provide a range of values within which the true population parameter is likely to fall, with a certain level of confidence. The video script describes how tighter confidence intervals (less variance) are given more weight in a meta-analysis, reflecting the reliability of the study results.

💡Fixed effect vs. random effects

Fixed effect and random effects are two approaches used in meta-analysis. The fixed effect model assumes that the treatment effect is consistent across all studies, while the random effects model accounts for variability between studies. The script explains that most meta-analyses, especially of RCTs, use a random effects approach due to the heterogeneity often present in medical trials.

💡Forest plot

A forest plot is a graphical representation used in meta-analysis to display the results of individual studies and the pooled estimate. The video script describes how each line in a forest plot represents an individual study's results, with the diamond indicating the overall pooled estimate, providing a visual summary of the aggregated data.

💡Heterogeneity

Heterogeneity in research refers to the variability or differences between studies included in a meta-analysis. The script discusses how unmeasured heterogeneity, such as differences in patient characteristics or health systems, can affect the results of a meta-analysis and why a random effects model is often preferred to account for this variability.

Highlights

Meta-analysis is a statistical technique that pools results across studies to provide a better understanding of a subject.

In clinical research, meta-analysis can be used to estimate the strength of an association between an intervention and an outcome, such as the effect of a medication on hospitalization rates.

A meta-analysis is an observational study of studies, not a randomized trial.

The first step in conducting a meta-analysis is to systematically search for relevant studies using a well-defined search strategy to avoid bias.

The design of the studies included in a meta-analysis should be considered, as combining different types of studies can complicate the analysis.

Studies included in a meta-analysis should have similar patient populations to ensure the results are informative and comparable.

The intervention being evaluated must be consistent across all studies in a meta-analysis.

Studies should have the same or similar outcome definitions to allow for meaningful pooling of results.

When pooling results, it is important to consider the size and variance of the trials, as larger trials with tighter confidence intervals may be more informative.

Fixed effects and random effects analysis are two approaches used in meta-analysis, with the latter accounting for between-trial heterogeneity.

Random effects analysis is commonly used in meta-analyses of randomized controlled trials due to the potential for unmeasured heterogeneity.

A forest plot is a visual representation of the results from a meta-analysis, with horizontal lines representing individual trial results and a diamond representing the pooled estimate.

The width of the diamond in a forest plot represents the 95% confidence interval around the pooled estimate.

Meta-analysis provides more confidence in conclusions by pooling results from multiple studies, similar to synthesizing multiple movie reviews to make an informed choice.

The process of conducting a meta-analysis involves refining a systematic review strategy, identifying relevant trials, and employing an appropriate statistical approach.

Understanding the basics of meta-analysis allows for more informed decision-making in both clinical research and everyday choices, such as selecting a movie to watch.

Transcripts

Hello, I’m Chana Sacks, editor-in-chief of NEJM Evidence, and this is Stats, STAT!

After a long week in the hospital, Saturday has arrived, and it’s movie  

night. A rom-com would be perfect to end the week on a lighter note. Or maybe an  

action pic? You search “new streaming releases” and start reading reviews.  

Some reviews provide a rating on a five-star scale, some use a scale from 0–100 tomatoes,  

and another one provides no score at all and simply waxes poetic about the film. At last,  

you find a single website that synthesizes all the reviews and summarizes an overall score from  

0 to 10. This site makes it easy to find a movie you’ll enjoy, and as you’re scrolling through,  

you think, this synthesis resembles a meta-analysis. And guess what, you’re right.

Meta-analysis is a statistical technique that pools results across studies.  

For example, aggregating results of multiple movie reviews gives you a better sense of how  

reviewers in general felt about a film or the types of movies that are likely to be  

reviewed favorably. In clinical research, one use of meta-analysis is to pool results  

across trials, to estimate the strength of an association between an intervention (say,  

a medication for heart failure) and an outcome (say, hospitalization for heart  

failure). A meta-analysis is not a randomized trial; it’s an observational study of studies.

Let’s look at five key components involved in conducting a meta-analysis. First, you  

systematically search for studies (and, of course, there need to be at least two). Investigators  

typically review all relevant databases using a well-defined search strategy. Such a systematic  

approach seeks to avoid bias by including all relevant publications. Second, you consider the  

design of the studies. For example, you might have three randomized trials, one case-control study,  

and one cohort study. While there are methods to combine different types of studies, doing so  

complicates the analysis and is less common in clinical research than combining studies  

of a single design, such as randomized trials. Third, you want trials to have similar patient  

populations. Defining “similar” can be subjective, but you can imagine that combining results of a  

trial of an intervention among older adults with another that included only infants is unlikely to  

be informative for adults. Fourth, the studies need to be evaluating the same intervention.  

And fifth, the studies need to have the same, or again similar, outcome definitions. For example,  

all the movie reviews had the same outcome (movie quality), even though many used different scales.

Let’s work through an example. You conduct a review of the literature on a specific medication  

in heart failure and identify three RCTs that tested the effect of the medication among adults  

with heart failure with reduced ejection fraction on heart failure hospitalization.  

Two of the three trials suggested that the medication had no effect and one suggested  

that it decreased heart failure hospitalization. Pooling the results should allow you to estimate  

the average effect … but how? Can you simply add up the three relative risks  

and divide by 3? While that is one approach, it doesn’t feel right. Doing so implicitly  

gives the same weight to each trial, but two of the trials were tiny, and one was large.

One common approach in clinical research gives more weight to trials that have results with  

tighter confidence intervals (that is, less variance) and less weight to trials with wider  

confidence intervals (or more variance). Of course, there is also more to consider.  

Investigators decide whether to employ what’s a called fixed effect or random effects analysis.   

A fixed effects approach assumes that the underlying treatment effect is the same in all trials and  

differences are due only to sampling error. That assumption is rarely true in medicine,  

where trial results are often heterogenous for reasons other than just sampling. For example,  

trials you identified may have a lot of unmeasured between-trial heterogeneity — say,  

characteristics of the patients or of the health systems where the trials are conducted,  

all of which could impact the magnitude of an intervention’s effect. For that reason,  

most meta-analyses of RCTs use a random effects approach, which assumes there is  

between-trial heterogeneity and the underlying treatment effect.

So, now that you get the basics, you’re ready to jump in. You refine your systematic review  

strategy, identify 13 RCTs, and employ a random effects approach. You display  

your results in what’s called a forest plot. Each horizontal line represents the results  

of an individual trial; the length of the line is the confidence interval,  

and the diamond is the pooled estimate of all the trials. The width of the  

diamond represents the 95% confidence interval around the pooled estimate.

All this stats talk is making you tired, and it’s almost movie time. The key point is that  

meta-analysis pools results from multiple studies to learn more than can be learned  

from a single one. And like in a meta-analysis, the pooled result from multiple movie reviews  

gives you more confidence in your movie night choice. Simple enough! Time to enjoy the show!