School of Sport and Exercise Sciences, Liverpool John Moores University, UK.
Musculoskeletal Rehabilitation Research Group, Faculty of Movement and Rehabilitation Sciences, Leuven KU, Belgium.
J Biomech. 2021 Jun 9;122:110451. doi: 10.1016/j.jbiomech.2021.110451. Epub 2021 Apr 23.
Testing a prediction is fundamental to scientific experiments. Where biomechanical experiments involve analysis of 1-Dimensional (waveform) data, sample size estimation should consider both 1D variance and hypothesised 1D effects. This study exemplifies 1D sample size estimation using typical biomechanical signals and contrasts this with 0D (discrete) power analysis. For context, biomechanics papers from 2018 and 2019 were reviewed to characterise current practice. Sample size estimation occurred in approximately 4% of 653 papers and reporting practice was mixed. To estimate sample sizes, common biomechanical signals were sourced from the literature and 1D effects were generated artificially using the open-source power1d software. Smooth Gaussian noise was added to the modelled 1D effect to numerically estimate the sample size required. Sample sizes estimated using 1D power procedures varied according to the characteristics of the dataset, requiring only small-to-moderate sample sizes of approximately 5-40 to achieve target powers of 0.8 for reported 1D effects, but were always larger than 0D sample sizes (from N + 1 to >N + 20). The importance of a priori sample size estimation is highlighted and recommendations are provided to improve the consistency of reporting. This study should enable researchers to construct 1D biomechanical effects to address adequately powered, hypothesis-driven, predictive research questions.
检验预测是科学实验的基础。在涉及一维（波形）数据分析的生物力学实验中，样本量估计应同时考虑一维方差和假设的一维效应。本研究使用典型的生物力学信号示例一维样本量估计，并将其与零维（离散）功效分析进行对比。为了说明问题，回顾了 2018 年和 2019 年的生物力学论文，以描述当前的实践情况。大约有 4%的 653 篇论文进行了样本量估计，报告实践情况参差不齐。为了估计样本量，从文献中获取了常见的生物力学信号，并使用开源的 power1d 软件人工生成一维效应。将平滑高斯噪声添加到模型化的一维效应中，以数值估计所需的样本量。使用一维功效程序估计的样本量根据数据集的特征而有所不同，仅需要大约 5-40 的小到中等样本量即可实现报告的一维效应的 0.8 目标功效，但总是大于零维样本量（从 N+1 到> N+20）。强调了事先进行样本量估计的重要性，并提出了改进报告一致性的建议。本研究应使研究人员能够构建一维生物力学效应，以解决具有足够功效、基于假设、具有预测性的研究问题。
