Department of Pathophysiology and Transplantation, University of Milan, Italy.
Department of Pathophysiology and Transplantation, University of Milan, Italy.
J Biomech. 2021 Jun 9;122:110481. doi: 10.1016/j.jbiomech.2021.110481. Epub 2021 Apr 24.
In biomechanics, kinematic and electromyographic data can be represented as one-dimensional (1D) waveforms and compared by using 1D hypothesis tests. These statistical techniques are increasingly applied in the study of locomotion. However, although widely agreed as a key step to obtain reliable and replicable findings, no a priori sample size estimation is usually conducted. This can also be done in 1D tests by calculating the statistical power - i.e., the probability of rejecting the null hypothesis when it is false - by using statistical parametric mapping. With the present study we characterised the parameters needed to estimate sample size in locomotion, and how they impact on statistical power in 1D tests. First, noise and signal in kinematics and electromyography were defined using experimental data on locomotion in physiological and pathological participants. Then, 1D power analysis was performed in representative conditions, and a dataset of tabulated sample sizes was generated. Kinematic and electromyographic data showed a smooth Gaussian noise, with amplitude and full-width-at-half-maximum depending on the physiological or pathological condition, and the considered joint or muscle. Given a certain noise, statistical power increased i) with greater signal amplitude and signal full-width-at-half-maximum, ii) when setting a region of interest and iii) when using a paired (vs. unpaired) study design. The present work provides initial benchmarks for appropriate sampling in 1D hypothesis testing, meant to evaluate statistical power in 1D tests and assists sample size estimation in studies on locomotion.
在生物力学中，运动学和肌电图数据可以表示为一维（1D）波形，并通过使用 1D 假设检验进行比较。这些统计技术越来越多地应用于运动研究。然而，尽管作为获得可靠和可重复发现的关键步骤已被广泛认可，但通常不进行先验样本量估计。这也可以通过使用统计参数映射在 1D 检验中计算统计功效（即，当零假设为假时拒绝零假设的概率）来完成。本研究通过使用生理和病理参与者的运动实验数据来确定运动中估计样本量所需的参数，并研究它们如何影响 1D 检验中的统计功效。首先，通过使用生理和病理参与者的运动实验数据来定义运动学和肌电图中的噪声和信号。然后，在代表性条件下进行 1D 功效分析，并生成表格化样本大小的数据集。运动学和肌电图数据显示出平滑的高斯噪声，其幅度和半峰全宽取决于生理或病理条件以及所考虑的关节或肌肉。给定一定的噪声，统计功效会随着以下因素的增加而提高：i）信号幅度和信号半峰全宽越大，ii）设置感兴趣区域时，iii）使用配对（而非非配对）研究设计时。本工作为 1D 假设检验中的适当采样提供了初步基准，旨在评估 1D 检验中的统计功效，并辅助运动研究中的样本量估计。
