Changes in serial biomarker measurements were evaluated using random slope, random intercept linear mixed-effects models, adjusted for study treatment. A mixed-effects model is a hierarchical regression model that includes fixed and random (subject-specific) effects, allowing for within-subject correlation between repeated measurements. Both NGAL and creatinine were log-transformed for modeling. Model selection was based on combined assessment of likelihood ratio tests of nested models for selection of random effects, and of Bayesian and Akaike’s information criteria (measures for model fit, lower is better) for selection of fixed effects, following graphical exploration of the data. Best fit was obtained using a second order polynomial (quadratic) time transformation for creatinine and third order polynomial (cubic) time transformation for NGAL, for both fixed and random effects.