Analysis of hierarchical biomechanical data structures using mixed-effects models

• Published in: Journal of biomechanics Vol. 69; pp. 34 - 39
• Main Authors: Tirrell, Timothy F., Rademaker, Alfred W., Lieber, Richard L.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2018; Elsevier Limited
• Subjects: Biomechanical engineering; Biomechanical Phenomena; Biomechanical testing; Biomechanics; Biopsy; Data analysis; Data processing; Data structures; Datasets; Economic models; Expected values; Extracellular matrix; Forearm; Forearm - physiology; Humans; Mathematical models; Mechanical Phenomena; Methods; Models, Biological; Muscles; Muscles - pathology; Muscles - physiology; Repeated measures; Sample size; Statistical analysis; Statistical methods; Structural hierarchy; Variance analysis; Weight-Bearing; Within-subjects design; United States > US; Iowa; Data analysis; Repeated measures; Sample size; Biomechanical testing
• ISSN: 0021-9290; 1873-2380; 1873-2380
• DOI: 10.1016/j.jbiomech.2018.01.013

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.