Bayesian Shape Invariant Model for Latent Growth Curve with Time-Invariant Covariates

TL;DR

This paper introduces a Bayesian shape-invariant growth curve model incorporating time-invariant covariates, enabling prediction of latent growth factors and analysis of medication effects on pubertal height development.

Contribution

It generalizes the SITAR model in a Bayesian framework with covariates, allowing nonlinear shape-invariant modeling of growth curves with subject-specific deviations.

Findings

Model effectively predicts latent growth factors.

Demonstrates medication effects on pubertal height by gender.

Validates the model with ADHD growth data.

Abstract

In the attention-deficit hyperactivity disorder (ADHD) study, children are prescribed different stimulant medications. The height measurements are recorded longitudinally along with the medication time. Differences among the patients are captured by the parameters suggested the Superimposition by Translation and Rotation (SITAR) model using three subject-specific parameters to estimate their deviation from the mean growth curve. In this paper, we generalize the SITAR model in a Bayesian way with time-invariant covariates. The time-invariant model allows us to predict latent growth factors. Since patients suffer from a common disease, they usually exhibit a similar pattern, and it is natural to build a nonlinear model that is shaped invariant. The model is semi-parametric, where the population time curve is modeled with a natural cubic spline. The original shape invariant growth curve model, motivated by epidemiological research on the evolution of pubertal heights over time, fits the underlying shape function for height over age and estimates subject-specific deviations from this curve in terms of size, tempo, and velocity using maximum likelihood. The usefulness of the model is illustrated in the attention deficit hyperactivity disorder (ADHD) study. Further, we demonstrated the effect of stimulant medications on pubertal growth by gender.