George Brandon J, Beasley T Mark, Brown Andrew W, Dawson John, Dimova Rositsa, Divers Jasmin, Goldsby TaShauna U, Heo Moonseong, Kaiser Kathryn A, Keith Scott W, Kim Mimi Y, Li Peng, Mehta Tapan, Oakes J Michael, Skinner Asheley, Stuart Elizabeth, Allison David B
Office of Energetics, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Obesity (Silver Spring). 2016 Apr;24(4):781-90. doi: 10.1002/oby.21449.
This review identifies 10 common errors and problems in the statistical analysis, design, interpretation, and reporting of obesity research and discuss how they can be avoided. The 10 topics are: 1) misinterpretation of statistical significance, 2) inappropriate testing against baseline values, 3) excessive and undisclosed multiple testing and "P-value hacking," 4) mishandling of clustering in cluster randomized trials, 5) misconceptions about nonparametric tests, 6) mishandling of missing data, 7) miscalculation of effect sizes, 8) ignoring regression to the mean, 9) ignoring confirmation bias, and 10) insufficient statistical reporting. It is hoped that discussion of these errors can improve the quality of obesity research by helping researchers to implement proper statistical practice and to know when to seek the help of a statistician.
本综述识别了肥胖研究的统计分析、设计、解释和报告中10个常见的错误及问题，并讨论了如何避免这些问题。这10个主题分别为：1）对统计显著性的错误解读；2）针对基线值进行不恰当的检验；3）过度且未披露的多重检验及“P值篡改”；4）在整群随机试验中对整群的处理不当；5）对非参数检验的误解；6）对缺失数据的处理不当；7）效应量的计算错误；8）忽视均值回归；9）忽视确认偏倚；10）统计报告不充分。希望对这些错误的讨论能够通过帮助研究人员实施恰当的统计实践以及了解何时寻求统计学家的帮助，从而提高肥胖研究的质量。
