Abstract Details
Activity Number:
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256
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Type:
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Contributed
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Date/Time:
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Monday, August 5, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Mental Health Statistics Section
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Abstract - #308886 |
Title:
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Trajectories to Dementia Identified with Mixed Membership Models
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Author(s):
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Fabrizio Lecci*+ and Brian Junker and James Becker and Oscar Lopez
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Companies:
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Carnegie Mellon University and Department of Statistics, Carnegie Mellon University and University of Pittsburgh and University of Pittsburgh
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Keywords:
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Mixed Membership Models ;
Dementia ;
Alzheimer's disease
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Abstract:
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Alzheimer's disease is the most frequent form of dementia in the elderly, and age is its most powerful risk factor. The idea is to model the probability of being diagnosed with dementia at different ages, in order to construct trajectories for different categories of people. We try to answer questions such as: what are the factors which cause rapid degeneration? Are there individuals with peculiar trajectories? How can we classify them? Mixed Membership Models constitute the most promising method for this problem. We have adapted the work of Manrique-Vallier and Fienberg (2010) on modelling trajectories toward disability by allowing the data to identify a small number of theoretically appealing "typical" trajectories and then expressing each individual's trajectory as a weighted combination of these typical trajectories.
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Authors who are presenting talks have a * after their name.
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