Abstract Details
Activity Number:
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320
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 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 #312218
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Title:
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A Bayesian Approach to Subgroup Identification
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Author(s):
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Xiaojing Wang*+ and James O. Berger and Lei Shen
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Companies:
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University of Connecticut and Duke University and Eli Lilly and Company
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Keywords:
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Bayesian analysis ;
subgroup analysis ;
multiplicity ;
model uncertainty
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Abstract:
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The paper discusses subgroup identification, the goal of which is to determine the heterogeneity of treatment effects across subpopulations. Searching for differences among subgroups is challenging because it is inherently a multiple testing problem with the complication that test statistics for subgroups are typically highly dependent, making simple multiplicity corrections such as the Bonferroni correction too conservative. In this paper, a Bayesian approach to identify subgroup effects is proposed, with a scheme for assigning prior probabilities to possible subgroup effects that accounts for multiplicity and yet allows for (pre-experimental) preference to specific subgroups. The analysis utilizes a new Bayesian model selection methodology and, as a byproduct, produces individual probabilities of treatment effect that could be of use in personalized medicine. The analysis is illustrated on an example involving subgroup analysis of biomarker effects on treatments.
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Authors who are presenting talks have a * after their name.
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