Zero- vs. one-dimensional, parametric vs. non-parametric, and confidence interval vs. hypothesis testing procedures in one-dimensional biomechanical trajectory analysis
Zero- vs. one-dimensional, parametric vs. non-parametric, and confidence interval vs. hypothesis testing procedures in one-dimensional biomechanical trajectory analysis
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DOI：
10.1016/j.jbiomech.2015.02.051
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发表时间：
2015-05-01
影响因子：
2.4
通讯作者：
Robinson, Mark A.
中科院分区：
文献类型：
--
作者：
Pataky, Todd C.;Vanrenterghem, Jos;Robinson, Mark A.
生物力学过程通常表现为一维（1D）轨迹。已经证明，当基于OD统计程序时，1D置信区间（CI）存在偏倚，并且非参数1D bootstrap CI已在生物力学文献中作为可行的解决方案出现。本文的主要目的是澄清，对于1D生物力学数据集，OD和1D方法之间的区别比参数和非参数程序之间的区别重要得多。次要目的是证明，一个参数相当于1D自助存在的形式，随机场理论（RFT）校正多重比较。为了强调这些点，我们分析了六个数据集，包括单样本，配对，双样本和回归设计中的力和运动轨迹。结果表明，首先，1D bootstrap和其他1D非参数CI与RFT CI在性质上相同，但与OD CI有很大差异。其次，所有6个数据集的10个参数和1D非参数假设检验结果在定性上相同。最后，我们强调了一维CI的局限性，证明它们是复杂的，设计依赖的，因此不可推广。这些结果表明，（i）基于OD随机性模型的1D数据分析通常是有偏差的，除非在实验前明确识别OD变量，以及（ii）参数和非参数1D假设检验提供了一个明确的分析框架，当一个人的假设明确或隐含地涉及整个1D轨迹。(C)2015爱思唯尔有限公司版权所有。
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.