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

Jiandong Shi, Dehui Luo, Xiang Wan, Yue Liu, Jiming Liu, Zhaoxiang Bian, Tiejun Tong*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

20 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1338-1360
Number of pages23
JournalStatistical Methods in Medical Research
Volume32
Issue number7
Early online date10 May 2023
DOIs
Publication statusPublished - Jul 2023

Scopus Subject Areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

User-Defined Keywords

  • Evidence-based medicine
  • five-number summary
  • flow chart
  • meta-analysis
  • skewness test

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