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Abstract Details
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Activity Number:
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284
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Social Statistics Section
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| Abstract - #304463 |
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Title:
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A Powerful and Robust Test Statistic for Randomization Inference in Group-Randomized Trials with Matched Pairs of Groups
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Author(s):
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Kai Zhang*+ and Mikhail Traskin and Dylan Small
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Companies:
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University of Pennsylvania and Amazon.com and The Wharton School
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Address:
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3730 Walnut Street, Philadelphia, PA, 19104, United States
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Keywords:
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Causal effect ;
Group-randomized trials ;
Randomization inference ;
Rank-based statistics ;
Robustness
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Abstract:
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For group-randomized trials, randomization inference based on rank statistics provides robust, exact inference against nonnormal distributions. However, in a matched-pair design, the currently available rank-based statistics lose significant power compared to normal linear mixed model (LMM) test statistics when the LMM is true. In this article, we investigate and develop an optimal test statistic over all statistics in the form of the weighted sum of signed Mann-Whitney-Wilcoxon statistics under certain assumptions. This test is almost as powerful as the LMM even when the LMM is true, but it is much more powerful for heavy tailed distributions. A simulation study is conducted to examine the power.
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