受缺失影响的功能数据的时间范围内分析及其在住院患者连续血糖监测中的应用
Time-In-Range Analyses of Functional Data Subject to Missing with Applications to Inpatient Continuous Glucose Monitoring
AI总结：
针对住院患者CGM数据因住院时间有限导致的复杂缺失问题，提出基于逆概率加权和半参数生存建模的TIR无偏估计框架，并通过数值研究验证其优于现有方法。
摘要：
连续血糖监测（CGM）已在美国医院中越来越多地用于糖尿病患者的护理。时间范围内（TIR）衡量在指定时间窗口内血糖值处于目标范围的时间百分比，已成为评估血糖控制的关键CGM指标。然而，住院患者CGM容易受到一个普遍问题的影响：有限的住院时间可能导致CGM采样不足，从而形成受复杂缺失困扰的功能数据场景。当前对住院患者CGM研究的分析却忽略了这一问题，通常将TIR计算为可用CGM血糖值中处于目标范围的比例。模拟研究表明，这可能导致相当大的估计和推断偏差，主要原因是住院患者CGM轨迹的非平稳性。在这项工作中，我们开发了一个严格的统计框架，可在现实的住院患者CGM环境中对TIR进行有效推断。我们的方法利用了一种新颖的TIR概率表示，从而能够借助逆概率加权技术和半参数生存建模来获得平均TIR的无偏估计量，并恰当地考虑未完全观测到的CGM轨迹。我们建立了所提估计量的理想渐近性质。数值研究结果表明，所提方法具有良好的有限样本性能，并且优于现有方法。所提方法通常也适用于与CGM类似的其他功能数据场景。
英文摘要：
Continuous glucose monitoring (CGM) has been increasingly used in US hospitals for the care of patients with diabetes. Time in range (TIR), which measures the percent of time over a specified time window with glucose values within a target range, has served as a pivotal CGM-metric for assessing glycemic control. However, inpatient CGM is prone to a prevailing issue that a limited length of hospital stay can cause insufficient CGM sampling, leading to a scenario with functional data plagued by complex missingness. Current analyses of inpatient CGM studies, however, ignore this issue and typically compute the TIR as the proportion of available CGM glucose values in range. As shown by simulation studies, this can result in considerably biased estimation and inference, largely owing to the nonstationary nature of inpatient CGM trajectories. In this work, we develop a rigorous statistical framework that confers valid inference on TIR in realistic inpatient CGM settings. Our proposals utilize a novel probabilistic representation of TIR, which enables leveraging the technique of inverse probability weighting and semiparametric survival modeling to obtain unbiased estimators of mean TIR that properly account for incompletely observed CGM trajectories. We establish desirable asymptotic properties of the proposed estimators. Results from our numerical studies demonstrate good finite-sample performance of the proposed method as well as its advantages over existing approaches. The proposed method is generally applicable to other functional data settings similar to CGM.
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