update abstract in readme file

README.md

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-# Issues and Solutions for Time-In-Range Analyses Based on Inpatient Continuous Glucose Monitoring Data
+# Time-In-Range Analyses of Funcational Data Subject to Missing with Applications to Inpatient Continuous Glucose Monitoring
 
-Continuous glucose monitoring (CGM) has been increasingly used in the hospital for the care of patients with hyperglycemia and diabetes. “Time in range” (TIR) has been spotlighted as a pivotal metric derived from CGM data to assess glycemic control. However, a prevailing data issue that is often ignored in TIR analysis is that the use of CGM in the hospital often has a shorter sampling duration due to the patient’s early discharge. As shown by our simulation results, ignoring this issue can lead to a considerably biased evaluation of TIR. We have developed rigorous statistical procedures that properly account for the limitations of the hospital CGM use, and confer valid estimation and inference of mean TIR. We also established the 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 the existing approach.
+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 speci ed 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 insu cient CGM sampling, leading to a scenario with functional data subject to missing. 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.