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Mixed models methods using repeated measures were used to estimate the rate of change over time (slope), and to test these slopes among the four groups.
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A mixed models method was used for testing between the groups, allowing heterogeneity between the groups.
Nonlinear models proved to fit the data better compared with linear models in Martins' study [ 44]; moreover, the mixed models method also takes the individual variability into account, which increases the variance explained to a larger extent.
The mixed models method also takes into account variations in the number of follow-up assessments available for the participants and unequal time intervals between the collected data points, which are common statistical limitations observed in longitudinal studies.
To gain further insight, we relied on mixed model methods.
A key element of our analysis was mixed model methods.
Contrary to repeated measures analysis of variance, mixed model methods analyse all available data without any data loss [ 31].
Mixed model methods were used for the analysis, with separate variances fit for each of the 2 groups (SC and C) when necessary.
We utilize mixed model methods to account for the pedigree-based structure in our sample through the use of the genomic relationship matrix [ 19- 21].
We and others [ 3, 5, 6] have shown that mixed model methods are a powerful yet parsimonious approach for analyzing microarray data.
Among the many statistical approaches employed to analyze microarrays e.g. [ 15], here we have adopted a Bayesian approach that is closely related to mixed model methods [ 16].
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