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.

翻译：对于连续结局的临床研究，当数据可能存在偏态时，研究者可能选择报告全部或部分五数汇总（样本中位数、第一和第三四分位数、最小值和最大值），而非样本均数和标准差。近期文献常建议将五数汇总转换回样本均数和标准差，进而用于Meta分析。然而，若研究包含偏态数据，此转换及由此得出的Meta分析结论均不可靠。因此，我们提出一种仅利用五数汇总和样本量检测数据偏态的新方法，并同时设计了一套以不同方式处理偏态研究的新流程图。通过模拟研究进一步证明，我们的偏态检验能够控制第一类错误率并具备良好的统计功效，随后通过模拟Meta分析和真实数据案例展示了新方法在Meta分析及循证医学中的实用性。