Longitudinal models and generalised additive models
We applied a gate-keeper approach to assess longitudinal associations between growth outcomes and Se biomarkers based on significant associations observed in cross-sectional linear analyses at ages 0, 1 and 2 years. We used unadjusted multilevel models of change (‘proc mixed’) to assess whether relevant Se biomarkers in VC blood predicted a change in growth-z-scores between 0 and 3 years based on the availability of anthropometric data. We also used generalised additive models (‘proc gam’) to examine smoothing splines for predicted associations between Se biomarkers and relevant measures of growth.