This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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163
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Type:
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Topic 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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Section on Bayesian Statistical Science
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Abstract - #308562 |
Title:
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A Bayesian Shrinkage Model for Incomplete Longitudinal Binary Data with Application to the Breast Cancer Prevention Trial
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Author(s):
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Chenguang Wang*+ and Michael J. Daniels and Daniel O. Scharfstein and Stephanie R. Land
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Companies:
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FDA and University of Florida and Johns Hopkins Bloomberg School of Public Health and University of Pittsburgh
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Address:
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, , 20993,
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Keywords:
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Informative drop-out ;
Intermittent missingness ;
Prior elicitation
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Abstract:
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A Bayesian Shrinkage Model for Incomplete Longitudinal Binary Data with Application to the Breast Cancer Prevention Trial
We consider inference in randomized longitudinal studies with missing data that is generated by skipped clinic visits and loss to follow-up. We assume a non-future dependence model for the drop-out mechanism and partial ignorability for the intermittent missingness. We posit an exponential tilt model that links non-identifiable distributions and distributions identified under partial ignorability. This model is indexed by non-identified parameters, which are assumed to have an informative prior distribution elicited from subject-matter experts. To avoid the curse of dimensionality, we model the distribution of the observed data using a Bayesian shrinkage model. Our methodology is motivated by and applied to data from the Breast Cancer Prevention Trial.
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