Powerful and Robust Test Statistic for Randomization Inference in Group‐Randomized Trials with Matched Pairs of Groups

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 sta...

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Bibliographic Details
Published in:Biometrics 2012-03, Vol.68 (1), p.75-84
Main Authors: Zhang, Kai, Traskin, Mikhail, Small, Dylan S.
Format: Article
Language:English
Subjects:
BIOMETRIC METHODOLOGY
Biometrics
biometry
Case-Control Studies
Causal effect
Control units
Correlations
Data Interpretation, Statistical
Depressive disorders
Experimentation
Generalized linear models
Group-randomized trials
Inference
Probabilities
Probability distribution
Random Allocation
Random variables
Randomization inference
Randomized Controlled Trials as Topic - methods
Randomized Controlled Trials as Topic - statistics & numerical data
Rank-based statistics
Robustness
statistical models
Statistical variance
Statistics
Statistics, Nonparametric
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Summary: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.
ISSN:0006-341X
1541-0420
DOI:10.1111/j.1541-0420.2011.01622.x