Xu Jie, Lin Yong, Yang Mu, Zhang Lanjing
Department of Infectious Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey.
J Cancer. 2020 Mar 4;11(10):2957-2961. doi: 10.7150/jca.43521. eCollection 2020.
Trend analysis is the analysis using statistical models to estimate and predict potential trends over time, space or any independent continuous-variable. It has been widely used in epidemiology and public health, but much less so in clinical oncology and basic cancer research. Methodological imitations of the chosen statistical package also appear to result in biased or less rigorous interrogation of cancer-related data. We thus review the basic statistics of trend analysis, commonly used commands of statistical packages and the common pitfalls of conducting trend analysis. Four free and 3 commercial statistical-packages were discussed in depth, including Joinpoint, Epi info, R package, Python, SAS, Stata and SPSS. We hope that this review could serve as a practical yet concise guide for using statistical packages for trend analysis in translational and clinical oncology, and help improve the scientific rigor of trend analyses in these fields. The guide, however, may also be applied to other research fields.
趋势分析是指运用统计模型对随时间、空间或任何独立连续变量的潜在趋势进行估计和预测。它在流行病学和公共卫生领域已得到广泛应用，但在临床肿瘤学和基础癌症研究中的应用则少得多。所选统计软件包的方法学缺陷似乎也导致对癌症相关数据的询问存在偏差或不够严谨。因此，我们回顾了趋势分析的基本统计学、统计软件包的常用命令以及进行趋势分析时常见的陷阱。深入讨论了4种免费和3种商业统计软件包，包括Joinpoint、Epi info、R软件包、Python、SAS、Stata和SPSS。我们希望这篇综述能成为在转化医学和临床肿瘤学中使用统计软件包进行趋势分析的实用而简洁的指南，并有助于提高这些领域中趋势分析的科学严谨性。不过，该指南也可应用于其他研究领域。
