Activity Number:
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392
- What Do the Experts Believe? Leveraging Expert Knowledge to Develop Robust Informative Prior Belief Distributions to Aid Decision Making in Drug and Medical Device Development
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 2:00 PM to 3:50 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #324050
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Title:
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glmcmp: Generalized Linear Models with Conditional Means Priors in R
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Author(s):
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David Kahle* and James Stamey and Michael Sonksen and Michael W. Seaman, Jr. and Karen Price and Fanni Natanegara
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Companies:
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Baylor University and Baylor University and Eli Lilly and Company and Baylor University and Eli Lilly and Eli Lilly and Company
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Keywords:
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Abstract:
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One of the key advantages of the Bayesian paradigm is the ability to inject expert opinion directly into the modeling process. In this talk we introduce glmcmp, an R package that provides novel data structures and methods to facilitate the use of generalized linear models with conditional means priors in R and BUGS. Included in glmcmp is a highly extensible, pipeline based prior elicitation framework that conceptually organizes the elicitation process in a way that encourages a macroscopic view of the modeling process and is user-friendly.
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Authors who are presenting talks have a * after their name.