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Progressive statistics for studies in sports medicine and exercise science

机译:运动医学和运动科学研究的渐进统计

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摘要

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 nullhypothesis 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.
机译:现在可以使用统计指南和专家声明来协助某些生物医学学科的研究分析和报告。我们在这里为运动医学和运动科学的基于样本的研究,荟萃分析和案例研究提供了更为先进的资源。我们对以下有争议的或新颖的问题提供了直接的建议:优先使用估计精度推断人口效应,而不是使用假设假设检验,这不足以评估临床或实际重要性;通过可接受的精度或对临床决策的信心来证明样本量的合理性,而不是通过具有统计学意义的足够能力来证明样本量的合理性;显示SD而不是SEM,以更好地传达均值差异和误差的不均匀性;避免纯粹的非参数分析,这种分析无法提供有关幅度的推论并且是不必要的;在有效性研究中使用回归统计数据,而不是不切实际和有偏见的协议限制;大量使用定性方法来丰富基于样本的定量项目;并寻求道德规范的批准,以便公众访问研究的个性化原始数据,以解决对研究进行更严格审查和进行更好的荟萃分析的需求。关于争议较少的问题的建议包括以下内容:在线性模型中使用协变量来调整混杂因素,解决个体差异,并确定影响效果的潜在机制;使用对数变换来处理效果和误差的不均匀性;识别和删除异常值;以适当的格式显示描述性,效果和推断性统计数据;以及由于采样,分配,致盲,测量误差和研究人员的偏见等问题而产生的偏见。本文应通过激发辩论,促进创新方法并为作者,审稿人和编辑提供有用的清单,来推动这一领域的发展。

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