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Activity Number:
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237
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2008 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #300674 |
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Title:
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Modeling Risk Factors for Alzheimer's Disease Progression Using a Nonhomogeneous Markov Process
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Author(s):
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Rebecca A. Hubbard*+ and Andrew Zhou
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Companies:
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University of Washington and University of Washington
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Address:
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4311 11th Ave NE, Seattle, WA, 98105,
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Keywords:
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longitudinal ; Markov process ; Alzheimer's disease ; disease progression ; multistate disease ; panel data
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Abstract:
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Identifying individuals at risk of developing Alzheimer's disease (AD) is important for understanding the natural history of the disease and most effectively targeting interventions. One group with high probability of progression to AD are subjects suffering from mild cognitive impairment (MCI). We propose a non-homogeneous Markov process model to characterize transitions between disease states defined by normal cognition, MCI, AD, and death and identify risk factors for conversion. Nonhomogeneous Markov process models are particularly useful in the case of AD because cognitive status is ascertained only at periodic follow-up visits and conversion rates are known to be highly age dependent. We apply this model to the Uniform Data Set, a longitudinal study of subjects evaluated at one of the National Institute on Aging's Alzheimer's Disease Centers.
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