This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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170
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Type:
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Contributed
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Date/Time:
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Monday, August 2, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #308716 |
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Title:
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Modeling Variability in Blood Glucose Data Collected in Continuous Monitoring Using ARIMA and Hierarchical Bayesian MCMC GLMM Models
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Author(s):
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Alex Zolot*+
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Companies:
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StatVis Consulting
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Address:
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6148 Mystra Pt, San Diego, CA, 92130,
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Keywords:
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continuous glucose monitoring ;
hierarchical Bayesian ;
GLMM ;
MCMC ;
R
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Abstract:
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We analyzed the variability in blood glucose data collected during continuous glucose monitoring using ARIMA and hierarchical Bayesian MCMC GLMM models. We compared different measures of the glucose variability using PCA and LDA analysis and found that
ARIMA(2,1,1) model fits the blood glucose level very accurately for almost all subjects and days, and
coefficients of the model have strong linear dependence that is universal for both between days, between subjects and between groups levels.
We developed an interactive R + WinBUGS application to analyze the data, choose parameters of the models, run simulations, and view their results.
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