Rethinking the Statistical Analysis of Neuromechanical Data

被引：26

作者：

Wilkinson, Ross D. ^{[1
]}

Mazzo, Melissa R. ^{[2
]}

Feeney, Daniel F. ^{[3
,4
]}

机构：

[1] Univ Colorado, Dept Integrat Physiol, Boulder, CO USA

[2] Strive Hlth, Denver, CO USA

[3] BOA Technol Inc, Performance Fit Lab, Denver, CO USA

[4] 3575 Ringsby Ct,Suite 200, Denver, CO 80216 USA

来源：

EXERCISE AND SPORT SCIENCES REVIEWS | 2023年 / 51卷 / 01期

关键词：

linear mixed-effects model; partial pooling; replicate measures; repeated measures; statistics; INFERENCE;

D O I：

10.1249/JES.0000000000000308

中图分类号：

Q4 [生理学];

学科分类号：

071003 ;

摘要：

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.

引用

页码：43 / 50

页数：8

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