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Robust inference from multiple test statistics via permutations: a better alternative to the single test statistic approach for randomized trials
Author(s) -
Ganju Jitendra,
Yu Xinxin,
Ma Guoguang Julie
Publication year - 2013
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.1582
Subject(s) - test statistic , type i and type ii errors , statistics , null hypothesis , statistical hypothesis testing , p value , mathematics , resampling , multiple comparisons problem , inference , statistic , log rank test , alternative hypothesis , null distribution , permutation (music) , computer science , survival analysis , artificial intelligence , physics , acoustics
Formal inference in randomized clinical trials is based on controlling the type I error rate associated with a single pre‐specified statistic. The deficiency of using just one method of analysis is that it depends on assumptions that may not be met. For robust inference, we propose pre‐specifying multiple test statistics and relying on the minimum p ‐value for testing the null hypothesis of no treatment effect. The null hypothesis associated with the various test statistics is that the treatment groups are indistinguishable. The critical value for hypothesis testing comes from permutation distributions. Rejection of the null hypothesis when the smallest p ‐value is less than the critical value controls the type I error rate at its designated value. Even if one of the candidate test statistics has low power, the adverse effect on the power of the minimum p ‐value statistic is not much. Its use is illustrated with examples. We conclude that it is better to rely on the minimum p ‐value rather than a single statistic particularly when that single statistic is the logrank test, because of the cost and complexity of many survival trials. Copyright © 2013 John Wiley & Sons, Ltd.