Dynamic Child Growth Prediction: A Comparative Methods Approach

• Activity Number: 341
• Type: Topic Contributed
• Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Andrada E. Ivanescu* and Ciprian Crainiceanu and William Checkley
• Companies: Montclair State University and The Johns Hopkins University and The Johns Hopkins University
• Keywords: functional data ; longitudinal data ; functional regression

Abstract

We introduce a class of dynamic regression models designed to predict the future of growth curves based on their historical dynamics. This class of models incorporates both baseline and time-dependent covariates, start with simple regression models and build up to dynamic function-on-function regressions. We compare the performance of the dynamic prediction models in a variety of signal-to-noise scenarios and provide practical solutions for model selection. We conclude that: 1) prediction performance increases substantially when using the entire growth history relative to using only the last and first observation; 2) smoothing incorporated using functional regression approaches increases prediction performance; and 3) the interpretation of model parameters is substantially improved using functional regression approaches. Because many growth curve data sets exhibit missing and noisy data we propose a bootstrap of subjects approach to account for the variability associated with the missing data imputation and smoothing. Methods are motivated by and applied to the CONTENT data set, a study that collected monthly child growth data on 197 children from birth until month 15.