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Diabetes Technol Ther. 2009 Jun;11 Suppl 1:S45-54. doi: 10.1089/dia.2008.0138.

Statistical tools to analyze continuous glucose monitor data.

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1
Division of Pediatric Endocrinology, Department of Pediatrics, and Section on Computational Neuroscience, University of Virginia Health Sciences Center, Charlottesville, Virginia 22908, USA. wlc@virginia.edu

Abstract

Continuous glucose monitors (CGMs) generate data streams that are both complex and voluminous. The analyses of these data require an understanding of the physical, biochemical, and mathematical properties involved in this technology. This article describes several methods that are pertinent to the analysis of CGM data, taking into account the specifics of the continuous monitoring data streams. These methods include: (1) evaluating the numerical and clinical accuracy of CGM. We distinguish two types of accuracy metrics-numerical and clinical-each having two subtypes measuring point and trend accuracy. The addition of trend accuracy, e.g., the ability of CGM to reflect the rate and direction of blood glucose (BG) change, is unique to CGM as these new devices are capable of capturing BG not only episodically, but also as a process in time. (2) Statistical approaches for interpreting CGM data. The importance of recognizing that the basic unit for most analyses is the glucose trace of an individual, i.e., a time-stamped series of glycemic data for each person, is stressed. We discuss the use of risk assessment, as well as graphical representation of the data of a person via glucose and risk traces and Poincaré plots, and at a group level via Control Variability-Grid Analysis. In summary, a review of methods specific to the analysis of CGM data series is presented, together with some new techniques. These methods should facilitate the extraction of information from, and the interpretation of, complex and voluminous CGM time series.

PMID:
19469677
PMCID:
PMC2903980
DOI:
10.1089/dia.2008.0138
[Indexed for MEDLINE]
Free PMC Article
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