Generalized functional linear regression models with measurement and misclassification errors

Music City Center 

Analyzing continuous glucose monitor (CGM) data is challenging. Threshold-based approaches (e.g., time in range) oversimplify CGM patterns, rely on predefined cutoffs, and fail to address measurement error biases or capture blood glucose (BG) variability. Error correction methods suited for continuous data are inadequate for binary functional predictors prone to misclassification, such as CGM-derived nocturnal glucose dips measured every 15 minutes over ten days. Scalar outcomes like birth weight are also influenced by error-prone factors like dietary intake (DI) and physical activity (PA). We propose generalized functional linear regression models that account for misclassification in nocturnal glucose dips and measurement error in scalar (e.g., DI) and functional (PA) predictors while considering individual variability and diurnal patterns. Simulations show that ignoring misclassification and measurement errors leads to biased estimates. We applied our methods to a Singapore-based cohort of 277 pregnant women to examine nocturnal glucose dips and their relationship with birth weight, accounting for DI and PA.

Continuous glucose monitor

functional logistic regression

measurement error

nocturnal glucose dip 

Biometrics Section