Evidence map›Paper›PMID 19469677›Full record

ArticleDiabetes technology & therapeutics2009

Statistical tools to analyze continuous glucose monitor data.

William Clarke, Boris Kovatchev

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diabetes technology & therapeutics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01524705 (FLAT-SUGAR), which is not on this map. Cited by 111 papers.

0numbers the graph read from it
0cells of the map it votes in
111citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT01524705 phase4completedstarted 2012, after this paper: background citation

FLAT-SUGAR: FLuctuATion Reduction With inSULin and Glp-1 Added togetheR

Ran2012Enrolled102Registered outcomes4Posted comparisons3ConditionsType 2 DiabetesArmsexenatide, Insulin glargine, Metformin, Prandial insulin
PMID 19092145PMID 27208320PMID 18972096PMID 19179212PMID 19429875PMID 19764834PMID 20154025PMID 20378541PMID 20530784PMID 20847305PMID 21525493PMID 5469118other papers from this trial
Open the trial in the graph
3 · Its place in the literature

Who cites it

111 citing papers in PubMed.

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51 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

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

Funding

Improving intensive insulin therapy through the personalization of data-driven decision support system to patients’ goals and preferences and its adaptation to long term health needsR01DK051562 · NIDDK · UNIVERSITY OF VIRGINIA CHARLOTTESVILLE · PI MARC D BRETON, Chiara Fabris · 1996 to 2026
$9.4M
NIDDK NIH HHS R01 DK 51562
6 · The paper itself

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.

Indexed as

Blood GlucoseDiabetes Mellitus, Type 1EatingHumansIslets of Langerhans TransplantationMonitoring, AmbulatoryReproducibility of ResultsSensitivity and SpecificityUnited StatesUnited States Food and Drug AdministrationBlood Glucose

Identifiers

PMID19469677
PMCPMC2903980

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.