Dataset Information

Analysis of hierarchical biomechanical data structures using mixed-effects models.

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.

SUBMITTER: Tirrell TF

PROVIDER: S-EPMC5913736 | biostudies-other | 2018 Mar

REPOSITORIES: biostudies-other

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Publications

Analysis of hierarchical biomechanical data structures using mixed-effects models.

Tirrell Timothy F TF Rademaker Alfred W AW Lieber Richard L RL

Journal of biomechanics 20180116

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 r ...[more]

PMID: 29366561

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Dataset Information

Analysis of hierarchical biomechanical data structures using mixed-effects models.

Publications

Analysis of hierarchical biomechanical data structures using mixed-effects models.

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