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Analyses will be undertaken on an intention-to-treat basis using random effects mixed modelling.
The effect sizes have been calculated based on the adjusted mean (SD) for random effects mixed modelling.
Main comparisons between groups will be performed using general linear mixed modelling.
The analysis of such data is usually carried out using a general mixed modelling approach.
Generalised Linear Mixed Modelling was used in order to take into account the repeated measures study design.
Since the nine records for each respondent are not independent, we used linear mixed modelling for the mediation analysis.
Generally, this confirmed that the nonlinear mixed modelling technique used in the study produced models with good levels of accuracy in estimating height for any given age.
The nonlinear mixed modelling technique used in this study produced models with good levels of accuracy in estimating height for any given age.
General Linear Mixed Modelling was used to test for relationships between initial face colour and colour change applied to optimise healthy appearance (dependent variable = colour change; random factor = participant ID; covariates = initial face colour: L*, a* and b*).
To this end, linear mixed modelling (LMM) will be employed.
Data was analysed using linear regression and mixed modelling.
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