Abstract Details
Activity Number:
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653
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Type:
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Contributed
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Date/Time:
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Thursday, August 8, 2013 : 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 - #309093 |
Title:
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A Test Based on Monte Carlo Simulations for Biomarker-Adaptive Threshold Design
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Author(s):
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Yafeng Zhang*+ and Glen Laird
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Companies:
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UCLA and Bristol-Myers Squibb
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Keywords:
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Adaptive threshold design ;
Biomarker ;
Monte Carlo test
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Abstract:
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Under the background of personalized medicine, identifying a subset of sensitive patients who benefit from a treatment is crucial to the success of the treatment. Jiang et al. (2007) proposed a biomarker-adaptive threshold phase III design to find such a subset and test the treatment effect in the identified subgroup. However, this design relies on either a permutation test or an asymptotic approximation of P value (Miller and Sigmund 1982). The permutation test is time consuming, and the asymptotic approximation of P value is shown to be too conservative by simulation studies. Therefore, we proposed a test based on Monte Carlo simulations to test the treatment effect in the identified subset of patients. Simulations studies showed that the proposed test is fast and reliable, controls type I error rate and has similar power with permutation test.
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Authors who are presenting talks have a * after their name.
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