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Activity Number:
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482
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Type:
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Contributed
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Date/Time:
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Thursday, August 7, 2008 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #301965 |
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Title:
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Bayesian Evaluation of Surrogate Endpoints Using Power Priors
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Author(s):
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Chunyao (Amy) Feng*+ and John W. Seaman, Jr. and Stacy Lindborg
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Companies:
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Amgen, Inc and Baylor University and Eli Lilly and Company
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Address:
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One Amgen Center Dr., Thousand Oaks, CA, 91362,
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Keywords:
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Bayesian Analysis ; Surrogate Endpoint ; Power Prior
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Abstract:
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To save time and reduce the size and cost of clinical trials, surrogate endpoints are frequently measured instead of true endpoints. The proportion of the treatment effect explained by surrogate endpoints (PTE) is a widely used, albeit controversial, validation criteria. As an alternative to the use of PTE, we develop a Bayesian model for relative effect and the association between surrogate and true endpoints. Three prior structures are used: diffuse normal, informative, and power priors. The latter two prior structures are based on historical data. The power priors, developed by Ibrahim and Chen (2000) are used to attenuate the influence of the historical data on the current likelihood.
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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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