Analysis of hierarchical biomechanical data structures using mixed-effects models. (1st March 2018)

• Record Type: Journal Article
• Authors: Tirrell, Timothy F.; Rademaker, Alfred W.; Lieber, Richard L.
• Is Part Of: Journal of biomechanics. Volume 69(2018)
• Page Start: 34
• Page End: 39
• Publication Date: 2018-03-01
• Subjects: Biomechanical testing -- Repeated measures -- Sample size -- Data analysis; Animal mechanics -- Periodicals; Biomechanics -- Periodicals; Biomechanics -- Periodicals; Mécanique animale -- Périodiques; Biomécanique -- Périodiques; Electronic journals; 571.4305
• DOI: 10.1016/j.jbiomech.2018.01.013
• Languages: English
• ISSNs: 0021-9290

Abstract

Abstract: 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.