Statistical Models for Estimating Linear Growth Velocity: A Systematic Review

Poor linear growth among infants is still a global public health issue. Linear growth velocity has been variously suggested as a more robust measure for growth over the classical measure of attained height for age. In this study, we systematically reviewed available literature for models used in estimating linear growth velocity. We searched Medline, Embase, Cochrane methodology register, Joanna Briggs Institute EBP, through the Ovid interface, and PubMed database to identify relevant articles that used statistical models to estimate linear growth velocity among infants. Longitudinal studies published in English were included. Two reviewers independently screened the titles and abstracts to identify potentially eligible studies. Any disagreements were discussed and resolved. Full-text articles were downloaded for all the studies that met the eligibility criteria. We synthesized literature using the preferred reporting items for systematic review and meta-analyses guidelines for the most used statistical methods for modelling infant growth trajectories. A total of 301 articles were retrieved from the initial search. Fifty-six full-text articles were assessed for eligibility and 16 of which were included in the final review with a total of 303,940 infants, median sample size of 732 (interquartile range: 241-1683). Polynomial function models were the most used growth model. Three (18.8%) of the articles modelled the linear growth. Two (12.5%) articles used mixed-effects models and another two (12.5%) used the Jenss-Bayley growth models to model linear growth. Other models included residual growth model, two-stage multilevel linear spline model, joint multilevel linear spline model, and generalized least squares with random effects. We have identified linear mixed-effects models, polynomial growth models, and the Jenss-Bayley model as the used models for characterizing linear growth among infants. Linear mixed-effects model is appealing for its robustness even under violation of largely robust even to quite severe violations of model assumptions.