A meta-analysis is a statistical analysis that combines the results of multiple scientific studies.
The basic idea behind meta-analyses is that there is a common truth behind all conceptually similar scientific studies, but which has been measured with a certain error within individual studies. The aim then is to use approaches from statistics to derive a pooled estimate closest to the unknown common truth based on how this error is perceived.
A key benefit of this approach is the aggregation of information leading to a higher statistical power and more robust point estimate than is possible from the measure derived from any individual study.
To quantify statistical power in meta-analyses I developed a simple but effective ShinyApp. It has never been easier to make this power calculations.
Fig1. The new MetaPowerCalculator App.
You only have to select the overall effect size of your meta-analysis, the average number of participants per group and the number of included studies. Automatically the app calculates the statistical power (see values on the right side). Next to sample size, number of studies and overall effect size the statistical power of meta-analyses is influenced by heterogeneity. Depending on the heterogeneity of included studies in your meta-analysis you have to decide which value best represents your results.
For further information about this topic read this article.