Continuous glucose monitoring (CGM) is a minimally invasive technology that allows continuous monitoring of an individual's blood glucose. We focus on a large clinical trial that collected CGM data every few minutes for 26 weeks and assumes that the basic observation unit is the distribution of CGM observations in a four-week interval. The resulting data structure is multilevel (because each individual has multiple months of data) and distributional (because the data for each four-week interval is represented as a distribution). The scientific goals are to: (1) identify and quantify the effects of factors that affect glycemic control in type 1 diabetes (T1D) patients; and (2) identify and characterize the patients who respond to treatment. To address these goals, we propose a new multilevel functional model that treats the CGM distributions as a response. Methods are motivated by and applied to data collected by The Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Group. Reproducible code for the methods introduced here is available on GitHub.

翻译：持续血糖监测（CGM）是一种微创技术，可对个体的血糖水平进行连续监测。本研究聚焦于一项大型临床试验，该试验每几分钟采集一次持续血糖监测数据，持续26周，并将基本观测单元设定为四周时间间隔内持续血糖监测观测值的分布。由此产生的数据结构具有多层级性（每个个体拥有多个月份的数据）和分布性（每个四周间隔的数据以分布形式呈现）。科学目标包括：（1）识别并量化影响1型糖尿病（T1D）患者血糖控制的因素效应；（2）识别并描述对治疗有反应的患者特征。为实现这些目标，我们提出了一种以持续血糖监测分布为响应变量的新型多层级函数模型。该方法源于青少年糖尿病研究基金会持续血糖监测研究组收集的数据的驱动，并应用于该数据集。本文所提出方法的可复现代码已在GitHub上公开。