Zero- vs. one-dimensional, parametric vs. non-parametric, and confidence interval vs. hypothesis testing procedures in one-dimensional biomechanical trajectory analysis

JOURNAL OF BIOMECHANICS

卷:48

Article

Pataky, Todd C.¹  Vanrenterghem, Jos²  Robinson, Mark A.² 

[1] Shinshu Univ, Dept Bioengn, Matsumoto, Nagano, Japan

[2] Liverpool John Moores Univ, Res Inst Sport & Exercise Sci, Liverpool L3 5UX, Merseyside, England

关键词: Bootstrap confidence interval;    Kinematics;    Ground reaction force;    Statistical parametric mapping;    Random field theory;    Time series analysis;   

DOI  :  10.1016/j.jbiomech.2015.02.051

来源: Elsevier

【 摘 要 】

Biomechanical processes are often manifested as one-dimensional (1D) trajectories. It has been shown that 1D confidence intervals (CIs) are biased when based on OD statistical procedures, and the non-parametric 1D bootstrap CI has emerged in the Biomechanics literature as a viable solution. The primary purpose of this paper was to clarify that, for 1D biomechanics datasets, the distinction between OD and 1D methods is much more important than the distinction between parametric and non-parametric procedures. A secondary purpose was to demonstrate that a parametric equivalent to the 1D bootstrap exists in the form of a random field theory (RFT) correction for multiple comparisons. To emphasize these points we analyzed six datasets consisting of force and kinematic trajectories in one-sample, paired, two-sample and regression designs. Results showed, first, that the 1D bootstrap and other 1D non-parametric CIs were qualitatively identical to RFT CIs, and all were very different from OD CIs. Second, 10 parametric and 1D non-parametric hypothesis testing results were qualitatively identical for all six datasets. Last, we highlight the limitations of 1D CIs by demonstrating that they are complex, design-dependent, and thus non-generalizable. These results suggest that (i) analyses of 1D data based on OD models of randomness are generally biased unless one explicitly identifies OD variables before the experiment, and (ii) parametric and non-parametric 1D hypothesis testing provide an unambiguous framework for analysis when one's hypothesis explicitly or implicitly pertains to whole 1D trajectories. (C) 2015 Elsevier Ltd. All rights reserved.

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