J Cancer 2020; 11(10):2957-2961. doi:10.7150/jca.43521 This issue


Statistics and pitfalls of trend analysis in cancer research: a review focused on statistical packages

Jie Xu1, Yong Lin2,3, Mu Yang4,5✉, Lanjing Zhang2,5,6,7✉

1. Department of Infectious Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
2. Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey.
3. Department of Biostatistics, School of Public Health, Rutgers University, Piscataway, New Jersey.
4. Department of Pathology, Shanghai First Hospital, Shangai Jiao Tong University, Shanghai, China.
5. Department of Pathology, Princeton Medical Center, Plainsboro, New Jersey.
6. Department of Biological Sciences, Rutgers University, Newark, New Jersey.
7. Department of Chemical Biology, Rutgers Ernest Mario School of Pharmacy, Piscataway, NJ.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
Xu J, Lin Y, Yang M, Zhang L. Statistics and pitfalls of trend analysis in cancer research: a review focused on statistical packages. J Cancer 2020; 11(10):2957-2961. doi:10.7150/jca.43521. Available from https://www.jcancer.org/v11p2957.htm

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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.

Keywords: statistical analysis, software, cancer, nonlinear trend, joinpoint regression, linear spline regression.