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Peak axial pull force analysis was conducted using a mixed effects model at significance level of 0.05 (two-sided).
Comparison analysis was carried out using a mixed effects model with pig, sample, and sample location as factors.
Significant differences between the conditions were detected using a mixed effects repeated measures ANCOVA with walking speed as a covariate.
Femoral angle measurements were analysed using a mixed effects linear model for repeated measures to determine the levels of intra-observer agreement (repeatability) and inter-observer agreement (reproducibility).
The latter are analyzed using a mixed effects model based on the new diameter growth function, to take into account inter-mouse variability and treatment factors.
The statistical significance of differences for different variables was determined using a mixed effects analysis of variance [39].
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Accounting for patient-effects using a mixed-effects model (adjusted analysis) also led to unbiased estimates of treatment effect and correct type I error rate in all scenarios.
The effect of water deficits on tested variables were examined using a mixed-effects model that included treatment as a fixed effect, with block as a random effect.
Higher-level between-subject analysis was carried out using a mixed-effects analysis with the FLAME (FMRIB's Local Analysis of Mixed Effects) tool, part of FSL.
Using a mixed-effects model approach permitted us to take into consideration the hierarchy of the experimental design with its multiple error terms.
These data were analyzed by Johannes et al. (2009), using a mixed-effects model, to explore the proportion of phenotypic variance explained by different effects.
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