Growth curve modeling is a popular methodological tool due to its flexibility in simultaneously analyzing both within-person effects (e.g., assessing change over time for one person) and between-person effects (e.g., comparing differences in the change trajectories across people). This paper is a practical exposure to fitting growth curve models in the hierarchical Bayesian framework. First the...

To study behavioral or psychiatric phenotypes, multiple indices of the behavior or disorder are often collected that are thought to best reflect the phenotype. Combining these items into a single score (e.g. a sum score) is a simple and practical approach for modeling such data, but this simplicity can come at a cost in longitudinal studies, where the relevance of individual items often changes...

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the objectives of this study were to identify a suitable mathematical model for describing the growth curve of baluchi sheep based on monthly records of live weight from birth to yearling; and to evaluate the efficacies of nonlinear mixed effect model (nlmm) and the nonlinear fixed effect model (nlm) methodologies. growth models were fitted to a total of 16,650 weight–age data belonging to 2071...