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Abstract Details
Activity Number:
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653
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 4, 2011 : 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 - #301976 |
Title:
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Bayesian Analysis of Two-Phase Studies with Binary Outcome and Categorical Covariates
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Author(s):
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Michelle Ross*+ and Jon Wakefield
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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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200 17th Ave E #104, Seattle, WA, 98112,
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Keywords:
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Bayesian statistics ;
Two-phase studies ;
Wilms Tumor ;
Markov Chain Monte Carlo
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Abstract:
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Two-phase sampling is an important outcome-dependent stratification technique that was developed to increase the efficiency of parameter estimation. To date, many frequentist solutions have been proposed, however no Bayesian solution has been offered. These frequentist solutions can encounter difficulties when there are small cell counts present (for example, in the context of a rare disease and a rare exposure) since these methods rely on asymptotic results for inference. It is also not possible to simply incorporate random effects in these approaches. Hence, there is strong motivation for a Bayesian approach to the analysis of two-phase studies. In this paper, we develop methodology for the Bayesian analysis of two-phase studies with a binary outcome and categorical covariates. We use a Markov Chain Monte Carlo auxiliary variable algorithm to implement the approach. The practicality of these methods is illustrated and compared to previously established methods using data from the National Wilms Tumor Study.
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