Exploring natural variation in tendon constitutive parameters via Bayesian data selection and mixed effects models
James Casey, Jessica Forsyth, Timothy Waite, Simon Cotter, Tom Shearer

TL;DR
This study uses Bayesian mixed effects models with hierarchical data selection to analyze natural variation in tendon properties across horses, revealing differences in stiffness and collagen density between tendon types.
Contribution
It introduces a hierarchical Bayesian data selection method for analyzing tendon parameters across populations, improving data reliability and understanding of tissue mechanics.
Findings
CDET is stiffer than SDFT, likely due to higher collagen volume.
Estimated collagen-related parameter products: 811.5 MPa (SDFT), 1430.2 MPa (CDET).
Positional tendons have stiffer collagen fibrils or higher collagen density.
Abstract
Combining microstructural mechanical models with experimental data enhances our understanding of the mechanics of soft tissue, such as tendons. In previous work, a Bayesian framework was used to infer constitutive parameters from uniaxial stress-strain experiments on horse tendons, specifically the superficial digital flexor tendon (SDFT) and common digital extensor tendon (CDET), on a per-experiment basis. Here, we extend this analysis to investigate the natural variation of these parameters across a population of horses. Using a Bayesian mixed effects model, we infer population distributions of these parameters. Given that the chosen hyperelastic model does not account for tendon damage, careful data selection is necessary. Avoiding ad hoc methods, we introduce a hierarchical Bayesian data selection method. This two-stage approach selects data per experiment, and integrates data…
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Taxonomy
TopicsAdvanced Statistical Methods and Models
