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Activity Number:
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70
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Type:
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Contributed
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Date/Time:
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Sunday, August 6, 2006 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #305679 |
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Title:
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A Bayesian Dynamic Spatio-Temporal Interaction Model
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Author(s):
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Jacob Oleson*+ and Hoon Kim
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Companies:
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The University of Iowa and California State Polytechnic University, Pomona
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Address:
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200 Hawkins Drive, C22GH, Iowa City, IA, 52242-1009,
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Keywords:
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autoregressive prior ; disease mapping ; hierarchical Bayes ; lob-linear mixed model
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Abstract:
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During the past three decades, prostate cancer incidence has changed substantially in the United States. A fully Bayesian hierarchical spatio-temporal interaction model is proposed to estimate prostate incidence rates in Iowa. We introduce random spatial effects to capture the local dependence among regions, random temporal effects to explain the nonlinearity of rates over time, and random spatio-temporal interactions. In addition, we introduce fixed-age effects, as most epidemiologic data are related strongly to age. We found prostate cancer incidence in Iowa counties increased sharply over age while incidence rates increased initially, then decreased over time. We identify hotspots of high and low rates for age groups and time periods using disease mapping.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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