Functional dynamic models with functional and scalar predictors prone to measurement errors.

Music City Center 

Extensive literature explores the modeling of dynamic and functional responses using functional regression approaches that apply smoothing techniques to capture complex data trends in functional covariates and time-varying scalar predictors. However, there has been relatively less focus on understanding how longitudinal and functional predictors prone to measurement errors influence dynamic functional outcomes. Addressing this gap, we propose a functional dynamic modeling framework that accounts for measurement errors in both functional and scalar predictors. This approach aims to enhance our understanding of how self-reported mealtimes, which serve as longitudinal measures, influence glycemic dynamics over time. Additionally, our model incorporates actigraphy-measured physical activity, which is prone to measurement errors, to provide a more comprehensive analysis. Finite sample properties were established through simulations. We applied the methods to data from a prospective cohort study of 277 healthy pregnant women to determine optimal meal timing and its association with dynamic glycemic outcomes in pregnancy.

Functional data

Glycemic dynamics

Measurement Error

Optimal Meal Timing

Physical Activity

Meal Type 

Biometrics Section