This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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498
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract - #306721 |
Title:
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A Theory for Testing Hypotheses Under Covariate-Adaptive Randomization
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Author(s):
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Jun Shao*+ and Xinxin Yu and Bob Zhong
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Companies:
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University of Wisconsin-Madison and University of Wisconsin-Madison and Johnson & Johnson
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Address:
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, , 53706,
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Keywords:
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Adaptive allocation ;
Biased coin ;
Clinical trial ;
Minimization ;
Power ;
Type I error
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Abstract:
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Some covariate-adaptive randomization methods have been used in clinical trials for a long time but very little theoretical work has been done for statistical inference after covariate-adaptive randomization. Practical users often apply test procedures developed for simple randomization, which is controversial since procedures valid under simple randomization may not be valid under covariate-adaptive randomization. We provide some theoretical results for obtaining valid tests after covariate-adaptive randomization. We also show that the simple two sample t-test without using any covariate is conservative under covariate-adaptive biased coin randomization in terms of its type I error, and that a valid bootstrap t-test can be constructed. The power of several tests under covariate-adaptive biased coin randomization are examined theoretically as well as empirically.
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Authors who are presenting talks have a * after their name.
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