Detecting the skewness of data from the five-number summary and its application in meta-analysis

TL;DR

This paper introduces a new method to detect data skewness using only the five-number summary and sample size, improving meta-analysis reliability when data are skewed.

Contribution

A novel skewness detection method based solely on five-number summaries and sample size, with a new flow chart for handling skewed data in meta-analyses.

Findings

The method controls type I error rates effectively.

It demonstrates good statistical power in simulations.

It improves meta-analysis accuracy with real data examples.

Abstract

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.