Impact of Missing Data on the Accuracy of Glucose Metrics from Continuous Glucose Monitoring Assessed Over a 2-Week Period

Objective: To explore the impact of missing data on the accuracy of continuous glucose monitoring (CGM) metrics collected for a 2-week period in a clinical trial.Research Design and Methods: Simulations were conducted to examine the effect of various patterns of missingness on the accuracy of CGM metrics as compared with a complete data set. The proportion of missing data, the block size in which the data were missing, and the missing mechanism were modified for each scenario. The degree of agreement between simulated and true glycemic measures under each scenario was presented as R-2.Results: Under all missing patterns, R-2 declined as the proportion of missing data increased, however, as the block size of missing data increased, the percentage of missing data had a more pronounced effect on the agreement between measures. For a 14-day CGM data set to be considered representative for percentage time in range (%TIR), at least 70% of CGM data should be available over at least 10 days (R-2 > 0.9). Skewed outcome measures, such as percentage time below range and coefficient of variation, were more affected by missing data than the less skewed measures (%TIR, percentage time above range, mean glucose).Conclusions: Both the degree and pattern of missing data impact upon the accuracy of recommended CGM-derived glycemic measures. In planning research, an understanding of patterns of missing data in the study population is required to gauge the likely effects of missing data on outcome accuracy.Trial registration number: Australian New Zealand Clinic Trials Registry ACTRN12616000753459.

Automated summary

The research aims to explore the impact of missing data on the accuracy of continuous glucose monitoring (CGM) metrics in a clinical trial. Through simulations, it is found that the proportion and block size of missing data affect the agreement of CGM metrics, with a higher proportion of missing data having a more pronounced effect. The study suggests that at least 70% of CGM data should be available over at least 10 days (R-2 > 0.9) for a 14-day CGM data set to be considered representative for percentage time in range (%TIR).