An introduction to meta-analysis

Have you ever heard of meta-analyses? If you haven’t, I suggest you do. A meta-analyses is basically a scientific review that combines the data from many independent studies. Meta-analyses are useful for many things, including determining whether studies have a significant effect on the topic of a research study, determining the strength and significance of research statements, comparing different results from different studies, and more.

Meta-analyses can be performed using different methods. One way is called meta-analyses using meta-statistics, which is an approach that takes the data from published studies and creates an overall meta-analyses rating for each study. Another way is called meta-analyses using fixed effects and random effects, where the effect is treated as a variable and all other factors are considered as time-varying or aggregates. Meta-analyses using outcome measures are called meta-analyses of outcomes, and meta-analyses of treatment effects are called meta-analyses of treatment effects and moderators. There are more general rules, however, and there is even a paper dedicated entirely to explaining the various types of meta-analyses and their appropriate applications in specific areas of study design.

The main advantage of a meta-analyses is that it allows researchers to gather enough information from a relatively large set of research studies in order to determine the effect size of their proposed treatment. However, there is a drawback: meta-analyses can generate a lot of wrong data and misinterpretation, leading researchers to draw incorrect conclusions or make inappropriate arguments about the treatment. Also, meta-analyses are only effective if they use statistically significant methods. For example, if a treatment for a disease is found to have a statistically significant effect, then it is likely that there really is a treatment for the disease, even if the real effect size is much lower than the treatment effect size.

Another major drawback of meta-analyses is that most of the time, the results of the meta-analyses are a composite of results from many individual studies, and all the studies used in the meta-analyses did not utilize exactly the same methods to measure the outcomes of interest. For instance, let’s say one research study finds that Lortab increases an individual’s sperm count, but another study finds that exercise does the opposite. Since studies that controlled for these different variables did not report results that were significantly different, we can safely say that the study that found the increase in sperm count is truly the “correct” effect of the treatment, and that the exercise was the “contributor effect”. If, however, the other studies did not report results that were statistically significant (for example, they did not report a difference of less than 0.10 on a scale where one to five is recommended), then we can safely conclude that there is no significant effect of Lortab on sperm count.

Finally, meta-analyses can create situations in which the actual effects of interventions are highly dependent upon the study design, which often creates bias. For instance, if a patient had a good experience with one surgeon, but a terrible experience with another surgeon, and the data were pooled from surgeons who both performed a particular procedure, then the result may be biased. This is known as a spurious association. Conversely, if all the patients in a given health setting are the same, then meta-analyses can help researchers to generalize about which medical interventions work best.

As you can see, while the benefits of meta-analyses are great, they also have their drawbacks. Meta-analyses should not be used to make conclusions about individual studies, and should only be used to give generalizability to results from individual studies. However, when performed carefully and with careful due diligence, meta-analyses can provide important information that will greatly impact how we choose to treat patients and prevent diseases.