Department of Critical Care Medicine, Jinhua Municipal Central Hospital, Jinhua Hospital of Zhejiang University, Jinhua 321000, China.
Ann Transl Med. 2016 Mar;4(5):91. doi: 10.21037/atm.2016.02.11.
In observational studies, the first step is usually to explore data distribution and the baseline differences between groups. Data description includes their central tendency (e.g., mean, median, and mode) and dispersion (e.g., standard deviation, range, interquartile range). There are varieties of bivariate statistical inference methods such as Student's t-test, Mann-Whitney U test and Chi-square test, for normal, skews and categorical data, respectively. The article shows how to perform these analyses with R codes. Furthermore, I believe that the automation of the whole workflow is of paramount importance in that (I) it allows for others to repeat your results; (II) you can easily find out how you performed analysis during revision; (III) it spares data input by hand and is less error-prone; and (IV) when you correct your original dataset, the final result can be automatically corrected by executing the codes. Therefore, the process of making a publication quality table incorporating all abovementioned statistics and P values is provided, allowing readers to customize these codes to their own needs.
在观察性研究中，通常第一步是探索数据分布和组间的基线差异。数据描述包括其集中趋势（例如均值、中位数和众数）和离散程度（例如标准差、范围、四分位距）。有各种双变量统计推断方法，分别适用于正态分布、偏态分布和分类数据，例如学生 t 检验、曼-惠特尼 U 检验和卡方检验。本文展示了如何使用 R 代码执行这些分析。此外，我认为整个工作流程的自动化非常重要，因为：(i) 它允许其他人重复您的结果；(ii) 您可以在修订过程中轻松找出进行分析的方法；(iii) 它避免了手动输入数据，减少出错的可能性；(iv) 当您更正原始数据集时，可以通过执行代码自动更正最终结果。因此，提供了一个包含所有上述统计信息和 P 值的具有出版质量的表格的制作过程，允许读者根据自己的需求自定义这些代码。
