论文标题
检测来自五个数字摘要的数据的偏度及其在荟萃分析中的应用
Detecting the skewness of data from the five-number summary and its application in meta-analysis
论文作者
论文摘要
对于具有连续结果的临床研究，当数据可能偏斜时，研究人员可能会选择报告五个数字摘要的全部或部分（样品中位数，第一和第三四分位数以及最小值和最大值），而不是样本平均值和标准偏差。在最近的文献中，通常建议将五个数字的摘要转换回样品平均值和标准偏差，随后可以在荟萃分析中使用。但是，如果一项研究包含偏斜的数据，则这种转换以及荟萃分析的结论是不可靠的。因此，我们引入了一种新的方法，仅使用五个数字摘要和样本量检测数据的偏度，同时提出了一个新的流程图，以不同的方式处理偏斜的研究。我们通过模拟进一步表明，我们的偏度测试能够控制I型错误率并提供良好的统计功率，然后进行模拟的荟萃分析以及一个真实的数据示例，以说明我们新方法在荟萃分析和基于证据的医学中的有用性。
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.
