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Robust multivariate nonparametric tests for detection of two-sample location shift in clinical trials
Xuejun Jiang1, Xu Guo2, Ning Zhang3,4
1Department of Mathematics, Southern University of Science and Technology, Shenzhen, Guangzhou, P.R. China.
Plos One
|April 20, 2018
Summary
Robust multivariate nonparametric tests effectively detect location shifts in clinical trials. Hodges-Lehmann estimators and permutation tests offer superior efficiency and error control for analyzing intervention effects.
Area of Science:
- Statistics
- Biostatistics
- Clinical Trials
Background:
- Detecting location shifts between multivariate samples is crucial in randomized controlled trials.
- Existing methods often assume normality or lack robustness to outliers.
Purpose of the Study:
- To present and evaluate robust multivariate nonparametric tests for detecting location shifts.
- To assess the performance of tests based on medians, Hodges-Lehmann estimators, and extended U statistics.
Main Methods:
- Development of robust multivariate nonparametric tests using various location estimators.
- Application of bootstrap and permutation approaches for p-value determination.
- Simulation studies to evaluate test performance and efficiency.
Main Results:
- Tests based on Hodges-Lehmann estimators demonstrated higher efficiency compared to medians and extended U statistics.
- Permutation tests offered better Type I error control and higher power than bootstrap procedures.
- The proposed tests were successfully applied to analyze the Thai Healthy Choices study data.
Conclusions:
- Robust multivariate nonparametric tests provide a reliable method for detecting distributional differences in clinical trials.
- Hodges-Lehmann estimators and permutation methods are recommended for enhanced statistical power and accuracy.
- These methods are valuable for evaluating interventions, such as motivational interviewing, in reducing risk behaviors.
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