神经力学数据统计分析的再思考。

IF 4.9 2区医学 Q1 PHYSIOLOGY

Exercise and Sport Sciences Reviews Pub Date : 2023-01-01 DOI:10.1249/JES.0000000000000308

Ross D Wilkinson, Melissa R Mazzo, Daniel F Feeney

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引用次数: 6

摘要

神经力学的研究人员应该升级他们的统计工具箱。我们提出线性混合效应模型来代替常用的统计检验，以更好地捕捉特定受试者的基线和神经力学中自然发生的治疗相关效应。研究人员可以使用这种方法来处理零星的缺失数据，避免观测中条件独立的假设，并成功地模拟复杂的实验方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Rethinking the Statistical Analysis of Neuromechanical Data.

Researchers in neuromechanics should upgrade their statistical toolbox. We propose linear mixed-effects models in place of commonly used statistical tests to better capture subject-specific baselines and treatment-associated effects that naturally occur in neuromechanics. Researchers can use this approach to handle sporadic missing data, avoid the assumption of conditional independence in observations, and successfully model complex experimental protocols.

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来源期刊

Exercise and Sport Sciences Reviews 社会科学-生理学

CiteScore

10.70

自引率

1.80%

发文量

期刊介绍： Exercise and Sport Sciences Reviews made the transition from an annual hardcover series book to a quarterly journal in January 2000. The mission of this American College of Sports Medicine publication is to provide premier quarterly reviews of the most contemporary scientific, medical, and research-based topics emerging in the field of sports medicine and exercise science. The publication strives to provide the most relevant, topical information to students, professors, clinicians, scientists, and professionals for practical and research applications.