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Abstract Details
Activity Number:
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408
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #306869 |
Title:
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Frequentist Conditional Power, Bayesian Predictive Power, and Their Applications
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Author(s):
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Aijun Gao*+ and Fanni Natanegara and Karen Price
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Companies:
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PharmaNet/i3 and Eli Lilly and Company and Eli Lilly and Company
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Address:
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, , ,
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Keywords:
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Abstract:
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In clinical trial monitoring, Frequentist conditional power is often used to make a decision whether to stop or continue a trial in the planned interim analysis time point. Similarly Bayesian predictive power can be used to make a decision whether to stop a clinical trial or quantifying what is going to happen in a trial from any time point on, given the currently available data information. Both conditional power and predictive power can be used for sample size re-estimation in adaptive trials. A binary variable (any event of interest) was assumed as the main outcome for a randomized controlled clinical trial with two treatment groups. Both conditional power and predictive power were calculated at the interim analysis for the trial with available observation. The beta-binomial distribution was used in Bayesian predictive power calculation. The conditional power and the predictive power were also used for the sample size re-estimation. All the computations were implemented using R. The R code could be used for general cases. In addition, the equivalence between three different conditional power formulae from Dmitrienko, Denne and Proschan was also addressed.
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