Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong.
Shenzhen Research Institute of Big Data, Shenzhen, China.
Stat Methods Med Res. 2023 Jul;32(7):1338-1360. doi: 10.1177/09622802231172043. Epub 2023 May 10.
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.
对于具有连续结局的临床研究，当数据可能存在偏态时，研究人员可能会选择报告五数概括（样本中位数、第一四分位数、第三四分位数和最小值、最大值）的全部或部分内容，而不是报告样本均值和标准差。在最近的文献中，经常建议将五数概括转换回样本均值和标准差，然后可将其用于荟萃分析。然而，如果研究中存在偏态数据，这种转换以及荟萃分析的结论将不可靠。因此，我们提出了一种仅使用五数概括和样本量来检测数据偏态的新方法，同时还提出了一种新的流程图，以不同的方式处理偏态研究。我们通过模拟进一步表明，我们的偏度检验能够控制第一类错误率并提供良好的统计功效，然后进行模拟荟萃分析和实际数据示例，说明我们的新方法在荟萃分析和循证医学中的有用性。
