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Test-retest reliability measures for curve data: an overview with recommendations and supplementary code.

Alessia Pini1,2, Jonas L Markström3, Lina Schelin1

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Summary

This study reviews methods for assessing the reliability of curve data in biomechanics. Integrated intraclass correlation and standard error of measurement are recommended for functional data analysis.

Keywords:
Agreementfunctional datakinematicssimilarity

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Area of Science:

  • Biomechanics
  • Statistics
  • Functional Data Analysis

Background:

  • Curve data, or functional data, is frequently gathered in biomechanical research to analyze human movement.
  • Statistical methods for analyzing curve data in its most detailed form (as functions) have advanced.
  • A gap exists in the literature regarding statistical methods for assessing the reliability of curve data.

Purpose of the Study:

  • To provide a comprehensive overview of existing reliability investigation methods for curve data.
  • To identify and evaluate statistical approaches suitable for assessing the reliability of functional data in biomechanics.
  • To address the lack of a consolidated review on reliability measures for curve data.

Main Methods:

  • A literature review of contemporary methods for reliability measures of curve data in biomechanics and statistics.
  • Identification of key methods: coefficient of multiple correlation, functional limits of agreement, measures of distance and similarity, and integrated pointwise indices.
  • Implementation and evaluation of these methods using simulated data in R.

Main Results:

  • Several methods were identified, including coefficient of multiple correlation, functional limits of agreement, distance/similarity measures, and integrated pointwise indices.
  • Integrated pointwise indices extend univariate reliability measures (Pearson correlation, intraclass correlation, standard error of measurement) to curve data.
  • The integrated intraclass correlation and standard error of measurement demonstrated advantages in implementation and consideration of inter-individual variation.

Conclusions:

  • Integrated intraclass correlation and standard error of measurement are recommended for reliability investigations of curve data.
  • These methods are user-friendly, provide domain-wide results, and account for inter-individual variability.
  • The recommended methods offer a more complete assessment compared to other evaluated techniques.