To assess the compliance of "integrated" continuous glucose monitoring (CGM) systems with U.S. Food and Drug Administration requirements, the calculation of confidence intervals (CIs) on agreement rates (ARs), that is, the percentage of CGM measurements lying within a certain deviation of a comparator method, is stipulated. However, despite the existence of numerous approaches that could yield different results, a specific procedure for calculating CIs is not described anywhere. This report, therefore, proposes a suitable statistical procedure to allow transparency and comparability between CGM systems. Three existing methods were applied to six data sets from different CGM performance studies. The results indicate that a bootstrap-based method that accounts for the clustered structure of CGM data is reliable and robust. We thus recommend its use for the estimation of CIs of ARs. A software implementation of the proposed method is freely available (https://github.com/IfDTUlm/CGM_Performance_Assessment).
Keywords: Accuracy; Agreement rate; Bootstrapping; Confidence interval; Continuous glucose monitoring; FDA iCGM requirements; Wilson interval.