  For clinical studies with continuous outcomes, when the data are potentially
skewed, researchers may choose to report the whole or part of the five-number
summary (the sample median, the first and third quartiles, and the minimum and
maximum values) rather than the sample mean and standard deviation. In the
recent literature, it is often suggested to transform the five-number summary
back to the sample mean and standard deviation, which can be subsequently used
in a meta-analysis. However, if a study contains skewed data, this
transformation and hence the conclusions from the meta-analysis are unreliable.
Therefore, we introduce a novel method for detecting the skewness of data using
only the five-number summary and the sample size, and meanwhile propose a new
flow chart to handle the skewed studies in a different manner. We further show
by simulations that our skewness tests are able to control the type I error
rates and provide good statistical power, followed by a simulated meta-analysis
and a real data example that illustrate the usefulness of our new method in
meta-analysis and evidence-based medicine.
