Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

Sample size estimation for biomechanical waveforms: Current practice, recommendations and a comparison to discrete power analysis

Robinson, MA, Vanrenterghem, J and Pataky, T Sample size estimation for biomechanical waveforms: Current practice, recommendations and a comparison to discrete power analysis. Journal of Biomechanics. ISSN 0021-9290 (Accepted)

[img] Text
power_ms r1.pdf - Accepted Version
Restricted to Repository staff only
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (777kB)

Abstract

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.

Item Type: Article
Uncontrolled Keywords: 0903 Biomedical Engineering, 0913 Mechanical Engineering, 1106 Human Movement and Sports Sciences
Subjects: R Medicine > RC Internal medicine > RC1200 Sports Medicine
Divisions: Sport & Exercise Sciences
Publisher: Elsevier
Date Deposited: 14 Apr 2021 09:23
Last Modified: 14 Apr 2021 09:30
URI: https://researchonline.ljmu.ac.uk/id/eprint/14792

Actions (login required)

View Item View Item