What is a meta-analysis?

What is meta-analysis and why is it conducted?

Meta-analysis is a statistical method for combining results from multiple studies. The aim of a meta-analysis is to reduce random variation, such as the uncertainty of the results due to a small sample size.

A key purpose is to improve the statistical power of a study, that is, the ability to statistically detect a true phenomenon.

The result of a meta-analysis is a weighted average of the study-specific results. The weighting is usually based on the sample size of each study: the larger the study, the greater its relative weight.

In its simplest form, a meta-analysis pools the results from multiple smaller studies conducted using the same analysis plan. This approach is very common in genome-wide association studies, where the final results are combined from multiple smaller studies.

A meta-analysis can also be performed using already published results. However, in this case it is highly important that the combined studies address the same research question.

Meta-analysis and systematic reviews

When a meta-analysis is conducted using already published results, it is common to simultaneously perform a systematic review to qualitatively evaluate the studies. The aim of a systematic review is to provide information on possible study biases that may affect the results of the individual studies, and consequently, to inform whether a given study is suitable for inclusion in the meta-analysis. Studies of poor quality (i.e. those with important study biases that are likely to affect the study results) should not be used in a meta-analysis.

What meta-analysis is not?

Meta-analyses are often considered as one of the most reliable sources of information in the hierarchy of scientific evidence. However, meta-analysis is not a ‘miracle cure’ for reliable research – a common misconception is that a meta-analysis can eliminate all biases from several smaller studies. Meta-analysis can only reduce the uncertainty due to random variation, but different study biases (such as confounding or selection bias) should be evaluated separately, for example in a systematic review. Publication bias, which has been previously covered in our blog, can also affect meta-analysis results.

Are there any other ways to combine results from multiple studies?

Besides meta-analyses and systematic reviews, there are other ways to combine findings across multiple studies. One example is triangulation, a relatively recent approach for combining scientific information.

The main idea of triangulation is to combine results from multiple study designs which have different assumptions and different main sources of bias. The findings which are concordant across all these study designs can be considered the most reliable.

In summary, meta-analysis is a useful way to statistically combine the results from multiple studies, and it reduces the uncertainty of the results due to random variation. However, meta-analysis is but one way to conduct research, and a meta-analysis is not inherently better than any other single study.

Author: Ville Karhunen