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Abstract Details
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Activity Number:
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668
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Biopharmaceutical Section
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| Abstract - #302420 |
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Title:
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A Nonparametric Model for Analyzing Blood Glucose Data
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Author(s):
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Hui Zheng*+ and David Alan Schoenfeld
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Companies:
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Harvard Medical School and Massachusetts General Hospital
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Address:
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50 Staniford Street 5th Floor Suite 560, Boston, MA, 02114,
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Keywords:
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B-spline ;
random effects ;
diabetes
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Abstract:
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We show how continuous glucose monitoring (CGM) data can be analyzed using a B-spline model, facilitating the estimation of inter-patient variability, within-patient inter-day variability, and measurement error. We propose methods for statistical comparison of glucose profiles among patient groups. We applied a random effects model using quadratic B-spline functions to analyze inter-patient and within-patient inter-day variations of the glucose trend. The estimated standard deviation (SD) of the glucose curves are time-dependent, and were averaged over a 24-hour period. We analyzed CGM data from 322 type 1 diabetes, 223 type 2 diabetes, and 86 non-diabetic subjects with interstitial glucose levels measured every five minutes, for approximately 8 days per patient. We compared group-wide glucose profiles from the insulin pump-treated and multiple daily injection treated type 1 diabetes patients.
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