This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 498
Type: Topic Contributed
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract - #306721
Title: A Theory for Testing Hypotheses Under Covariate-Adaptive Randomization
Author(s): Jun Shao*+ and Xinxin Yu and Bob Zhong
Companies: University of Wisconsin-Madison and University of Wisconsin-Madison and Johnson & Johnson
Address: , , 53706,
Keywords: Adaptive allocation ; Biased coin ; Clinical trial ; Minimization ; Power ; Type I error
Abstract:

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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