Analysis of hierarchical biomechanical data structures using mixed-effects models

Timothy F. Tirrell, Alfred W. Rademaker, Richard L. Lieber*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Rigorous statistical analysis of biomechanical data is required to understand tissue properties. In biomechanics, samples are often obtained from multiple biopsies in the same individual, multiple samples tested per biopsy, and multiple tests performed per sample. The easiest way to analyze this hierarchical design is to simply calculate the grand mean of all samples tested. However, this may lead to incorrect inferences. In this report, three different analytical approaches are described with respect to the analysis of hierarchical data obtained from muscle biopsies. Each method was used to analyze an actual experimental data set obtained from muscle biopsies of three different muscles in the human forearm. The results illustrate the conditions under which mixed-models or simple models are acceptable for analysis of these types of data.

Original languageEnglish (US)
Pages (from-to)34-39
Number of pages6
JournalJournal of Biomechanics
StatePublished - Mar 1 2018


  • Biomechanical testing
  • Data analysis
  • Repeated measures
  • Sample size

ASJC Scopus subject areas

  • Biophysics
  • Orthopedics and Sports Medicine
  • Biomedical Engineering
  • Rehabilitation


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