Hopkins William G, Marshall Stephen W, Batterham Alan M, Hanin Juri
Institute of Sport and Recreation Research, AUT University, Auckland, New Zealand.
Med Sci Sports Exerc. 2009 Jan;41(1):3-13. doi: 10.1249/MSS.0b013e31818cb278.
Statistical guidelines and expert statements are now available to assist in the analysis and reporting of studies in some biomedical disciplines. We present here a more progressive resource for sample-based studies, meta-analyses, and case studies in sports medicine and exercise science. We offer forthright advice on the following controversial or novel issues: using precision of estimation for inferences about population effects in preference to null-hypothesis testing, which is inadequate for assessing clinical or practical importance; justifying sample size via acceptable precision or confidence for clinical decisions rather than via adequate power for statistical significance; showing SD rather than SEM, to better communicate the magnitude of differences in means and nonuniformity of error; avoiding purely nonparametric analyses, which cannot provide inferences about magnitude and are unnecessary; using regression statistics in validity studies, in preference to the impractical and biased limits of agreement; making greater use of qualitative methods to enrich sample-based quantitative projects; and seeking ethics approval for public access to the depersonalized raw data of a study, to address the need for more scrutiny of research and better meta-analyses. Advice on less contentious issues includes the following: using covariates in linear models to adjust for confounders, to account for individual differences, and to identify potential mechanisms of an effect; using log transformation to deal with nonuniformity of effects and error; identifying and deleting outliers; presenting descriptive, effect, and inferential statistics in appropriate formats; and contending with bias arising from problems with sampling, assignment, blinding, measurement error, and researchers' prejudices. This article should advance the field by stimulating debate, promoting innovative approaches, and serving as a useful checklist for authors, reviewers, and editors.
现在已有统计指南和专家声明可协助某些生物医学学科的研究分析和报告。在此，我们为运动医学和运动科学中基于样本的研究、荟萃分析及案例研究提供一种更具前瞻性的资源。我们就以下有争议或新颖的问题提供直截了当的建议：使用估计精度来推断总体效应，而非进行零假设检验，因为零假设检验不足以评估临床或实际重要性；通过可接受的精度或置信度来证明样本量对于临床决策的合理性，而非通过足够的统计功效来证明其对于统计显著性的合理性；展示标准差而非标准误，以便更好地传达均值差异的大小和误差的不一致性；避免纯粹的非参数分析，因为它无法提供关于效应大小的推断且没有必要；在效度研究中使用回归统计，而非不切实际且有偏差的一致性界限；更多地使用定性方法来丰富基于样本的定量项目；以及寻求伦理批准以便公众能够获取研究的去个性化原始数据，以满足对研究进行更多审查和更好地进行荟萃分析的需求。关于争议较小问题的建议包括：在线性模型中使用协变量来调整混杂因素、考虑个体差异并确定效应的潜在机制；使用对数变换来处理效应和误差的不一致性；识别并删除异常值；以适当的格式呈现描述性、效应和推断性统计；以及应对因抽样、分配、盲法、测量误差和研究人员偏见等问题产生的偏差。本文应通过激发辩论、促进创新方法以及为作者、审稿人和编辑提供有用的清单来推动该领域的发展。
