Modeling continuous monitoring glucose curves by Beta generalized non-parametric models

TL;DR

This paper introduces a novel Beta distribution-based functional data analysis method for continuous glucose monitoring data, requiring fewer observations and applicable to small sample sizes, with competitive performance against existing methods.

Contribution

We extend local likelihood estimation to two time-varying parameters and demonstrate its effectiveness for modeling glucose curves with limited data.

Findings

Method performs well on synthetic and real datasets.

Requires fewer observations than previous methods.

Competitively matches spline-based estimation approaches.

Abstract

We present a functional data analysis approach for studying time-dependent, continuous glucose monitoring data with repeated measures for each individual in an experiment. After scaling the glucose concentration curves to the interval [0, 1], we model them by using a Beta distribution with two time-varying parameters. In this context, we develop a local linear maximum likelihood smoothing procedure that is valid when more than one parameter depends on time. Our approach requires much fewer observations than previous functional methods for this setting and is also applicable when only one individual (or a few) is available. We evaluate the performance of our estimator in terms of computation time and model fit using a synthetic dataset as well as a large, real clinical trial dataset. We also compare our method with existing methods in the literature. From a methodological point of view, we contribute to extend local likelihood estimation from one to two time-varying parameters by developing theoretical expressions for estimation and for approximating the leave-one-out cross-validation. Moreover, we show that this kernel-based approach competes with spline-based estimation methods, the dominant line of functional regression models today.