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

Mark A Robinson1, Jos Vanrenterghem2, Todd C Pataky3

  • 1School of Sport and Exercise Sciences, Liverpool John Moores University, UK.

Journal of Biomechanics
|May 2, 2021
PubMed
Summary

Accurate sample size estimation is crucial for biomechanical research. This study introduces 1D power analysis for waveform data, showing it requires more participants than 0D analysis but ensures robust, hypothesis-driven research.

Keywords:
Hypothesis testingNumerical simulationStatistical parametric mappingStatistical powerWaveform analysis

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Area of Science:

  • Biomechanical Engineering
  • Statistical Modeling
  • Experimental Design

Background:

  • Scientific experiments rely on prediction testing.
  • Biomechanical research often uses 1-Dimensional (waveform) data.
  • Current sample size estimation practices in biomechanics are inconsistent.

Purpose of the Study:

  • To exemplify 1D sample size estimation for biomechanical signals.
  • To contrast 1D sample size estimation with 0D (discrete) power analysis.
  • To provide recommendations for improving sample size reporting in biomechanics.

Main Methods:

  • Reviewed biomechanics literature from 2018-2019 for current practices.
  • Sourced common biomechanical signals from existing literature.
  • Utilized open-source power1d software to generate artificial 1D effects and estimate sample sizes numerically.

Main Results:

  • Sample size estimation was reported in only ~4% of reviewed papers.
  • 1D power analysis required small-to-moderate sample sizes (approx. 5-40) for target power (0.8).
  • 1D sample sizes were consistently larger than 0D sample sizes.

Conclusions:

  • A priori sample size estimation is critical for adequately powered research.
  • 1D power analysis is essential for biomechanical studies with waveform data.
  • Standardized reporting of sample size estimation will enhance research reproducibility.